Amazon threat intelligence has identified an active Interlock ransomware campaign exploiting CVE-2026-20131, a critical vulnerability in Cisco Secure Firewall Management Center (FMC) Software that could allow an unauthenticated, remote attacker to execute arbitrary Java code as root on an affected device, which was disclosed by Cisco on March 4, 2026.

After Cisco’s disclosure, Amazon threat intelligence began research into this vulnerability using Amazon MadPot’s global sensor network—a system of honeypot servers that attract and monitor cybercriminal activity. While looking for any current or past exploits of this vulnerability, our research found that Interlock was exploiting this vulnerability 36 days before its public disclosure, beginning January 26, 2026. This wasn’t just another vulnerability exploit, Interlock had a zero-day in their hands, giving them a week’s head start to compromise organizations before defenders even knew to look. Upon making this discovery, we shared our findings with Cisco to help support their investigation and protect customers.

A misconfigured infrastructure server—essentially, a poorly secured staging area used by the attackers—exposed Interlock’s complete operational toolkit. This rare mistake provided Amazon’s security teams with visibility into the ransomware group’s multi-stage attack chain, custom remote access trojans (backdoor programs that give attackers control of compromised systems), reconnaissance scripts (automated tools for mapping victim networks), and evasion techniques.

AWS infrastructure and customer workloads on AWS were not observed to be involved in this campaign. This advisory shares comprehensive technical analysis and indicators of compromise to help organizations identify potential compromise and defend against Interlock’s operations. Organizations running Cisco Secure Firewall Management Center should immediately apply Cisco’s security patches and review the indicators provided below.

Amazon threat intelligence identified threat activity potentially related to CVE-2026-20131 beginning January 26, 2026, predating the public disclosure. Observed activity involved HTTP requests to a specific path in the affected software. Request bodies contained Java code execution attempts and two embedded URLs: one used to deliver configuration data supporting the exploit, and another designed to confirm successful exploitation by causing a vulnerable target to perform an HTTP PUT request and upload a generated file. Multiple variations of these URLs were observed across different exploit attempts.

To advance the investigation and obtain additional threat intelligence, we performed the expected HTTP PUT request with the anticipated file content—essentially, we pretended to be a successfully compromised system. This successfully prompted Interlock to proceed to the next stage, issuing commands to fetch and execute a malicious ELF binary (a Linux executable file) from a remote server.

When analysts retrieved the binary, they discovered the same host (attacker-controlled server) is used for distributing Interlock’s entire operational toolkit. The exposed infrastructure organized artifacts into separate paths corresponding to individual targets, with the same paths used for both downloading tools to compromised hosts and uploading operational artifacts back to the staging server.

The ELF binary and associated artifacts are attributable to the Interlock ransomware family based on convergent technical and operational indicators. The embedded ransom note and TOR negotiation portal are consistent with Interlock’s established branding and infrastructure. The ransom note’s invocation of multiple data protection regulations reflects Interlock’s documented practice of citing regulatory exposure to pressure victims, essentially threatening organizations not just with data encryption, but with regulatory fines and compliance violations. The campaign-specific organization identifier embedded in the note aligns with Interlock’s per-victim tracking model.

Interlock has historically targeted specific sectors where operational disruption creates maximum pressure for payment. Education represents the largest share of their activity, followed by engineering, architecture, and construction firms, manufacturing and industrial organizations, healthcare providers, and government and public sector entities.

Temporal analysis performed on timestamps from observed threat activities, artifacts stored on the misconfigured infrastructure server, and metadata embedded within recovered threat artifacts indicates the actor most likely operates in UTC+3 with 75–80% confidence. Systematic analysis across all UTC offsets showed UTC+3 produced the best fit: first activity around 08:30, peak activity between 12:00 and 18:00, and a probable sleep window of 00:30–08:30.

Once Interlock gains initial access, they use a variety of priority tools to complete their attack. Amazon threat intelligence teams recovered a PowerShell script designed for systematic Windows environment enumeration (automated information gathering about the victim’s network). The script collects operating system and hardware details, running services, installed software, storage configuration, Hyper-V virtual machine inventory, user file listings across Desktop, Documents, and Downloads directories, browser artifacts from Chrome, Edge, Firefox, Internet Explorer, and 360 browser (including history, bookmarks, stored credentials, and extensions), active network connections correlated with responsible processes, ARP tables, iSCSI session data, and RDP authentication events from Windows event logs.

The script stages results to a centralized network share (\JK-DC2\Temp) using each system’s fully qualified hostname to create dedicated directories—essentially creating a folder for each compromised computer. Following collection, it compresses data into ZIP archives named after each hostname and removes original raw data. This structured per-host output format indicates the script operates across multiple machines within a network—a hallmark of ransomware intrusion chains that prepare for organization-wide encryption.

Remote access trojans (RATs) are malicious programs that give attackers persistent control over compromised systems, functioning like unauthorized remote desktop software.

JavaScript implant: Amazon threat intelligence recovered an obfuscated JavaScript remote access trojan that suppresses debugging output by overriding browser console methods (hiding its activity from basic detection tools). On execution, it profiles the infected host using PowerShell and Windows Management Instrumentation (WMI), collecting system identity, domain membership, username, OS version, and privilege context before transmitting this data during an encrypted initialization handshake.

Command-and-control communication occurs over persistent WebSocket connections with RC4-encrypted messages using per-message 16-byte random keys embedded in packet headers—essentially, each message uses a different encryption key, making interception more difficult. The implant cycles through multiple operator-controlled hostnames and IP addresses in randomized order with exponential backoff between reconnection attempts.

The implant provides interactive shell access, arbitrary command execution, bidirectional file transfer, and SOCKS5 proxy capability for tunneling TCP traffic (routing malicious traffic through other systems to hide its origin). Self-update and self-delete capabilities allow operators to replace or remove the implant without reinfection, supporting operational cleanup to hinder forensic investigation.

Java implant: A functionally equivalent client implemented in Java provides identical command-and-control capabilities. Built on GlassFish ecosystem libraries, it uses Grizzly for non-blocking I/O transport and Tyrus for WebSocket protocol communication. In simpler terms, Interlock built the same backdoor in two different programming languages, ensuring they maintain access even if defenders detect one version.

Sophisticated threat actors don’t attack from their own infrastructure, they build disposable relay networks to hide their tracks. Amazon threat intelligence teams identified a Bash script that configures Linux servers as HTTP reverse proxies (intermediary servers that forward traffic to hide the attacker’s true location). The script performs system updates, installs fail2ban with SSH brute-force protection, and compiles HAProxy 3.1.2 from source. The HAProxy instance listens on port 80 and forwards all inbound HTTP traffic to a hardcoded target IP, with systemd ensuring persistence across reboots.

A notable component is a log erasure routine running as a cron job every five minutes. The routine truncates all *.log files under /var/log and suppresses shell history by unsetting the HISTFILE variable. This aggressive evidence destruction, wiping logs every five minutes, combined with the purpose-built HTTP forwarding proxy, indicates the script establishes disposable traffic-laundering relay nodes. These nodes obscure exploit traffic origin, relay command-and-control communications, or proxy data exfiltration, making it nearly impossible to trace attacks back to their source.

Amazon threat intelligence teams observed a Java class file delivered as an alternative to the ELF binary drop. When loaded by the Java Virtual Machine (JVM), its static initializer registers a ServletRequestListener with the server’s StandardContext, essentially installing a persistent memory-resident backdoor that intercepts HTTP requests without writing files to disk. This “fileless” approach evades traditional antivirus scanning that looks for malicious files.

The listener inspects incoming requests for specially crafted parameters containing encrypted command payloads. Payloads are decrypted using AES-128 with a key derived from the MD5 hash of the hardcoded seed “geckoformboundary99fec155ea301140cbe26faf55ed2f40″ (using the first 16 characters: 09b1a8422e8faed0). Decrypted payloads are treated as compiled Java bytecode, dynamically loaded into the JVM, and executed—a technique designed to evade file-based detection by running malicious code entirely in memory.

Amazon threat intelligence teams recovered Java class files implementing a basic TCP server listening on port 45588 (encoded as Unicode character 넔 to obscure the port number from static analysis). The server accepts connections, logs connecting IP addresses, sends a greeting message, and immediately closes connections. This operational profile is consistent with a lightweight network beacon—essentially a “phone home” tool used to verify successful code execution or confirm network port reachability following initial exploitation.

Interlock deployed ConnectWise ScreenConnect, a legitimate commercial remote desktop tool, alongside custom implants. When ransomware operators deploy legitimate remote access tools alongside their custom malware, they’re buying insurance—if defenders find and remove one backdoor, they still have another way in. This indicates multiple redundant remote access mechanisms—a pattern consistent with ransomware operators seeking to maintain access even if individual footholds are removed. The tool’s legitimate network footprint helps blend with authorized remote administration traffic, making detection more challenging.

Amazon threat intelligence teams also recovered Volatility, an open-source memory forensics framework typically used by incident responders (the same tool defenders use to investigate attacks). While no artifacts indicated automated use, its presence alongside custom implants and reconnaissance scripts is consistent with advanced threat operations. Both ransomware groups and nation-state actors have been observed deploying Volatility during intrusions. The tool’s focus on parsing memory dumps provides access to sensitive data such as credentials stored in RAM, which can enable lateral movement (spreading through the network) and deeper environment compromise in support of ransom operations or espionage objectives.

Interlock also used Certify, an open source offensive security tool designed to exploit misconfigurations in Active Directory Certificate Services (AD CS). For ransomware operators, Certify provides a pathway to identify vulnerable certificate templates and enrollment permissions that allow requesting authentication-capable certificates. These certificates can be used to impersonate users, escalate privileges, or maintain persistent access. These capabilities directly support both initial compromise and long-term persistence objectives in ransomware operations.

The following indicators support defensive measures by organizations that may be affected. Due to Interlock’s use of content variation techniques, most file hashes are not included as reliable indicators. The threat actor modified most artifacts like scripts and binaries downloaded to different targets. This resulted in different file hashes for functionally identical tools. The customization allowed each attack to evade signature-based detection that looks for exact file matches.

206.251.239[.]164	Exploit source IP	Active Jan 2026
199.217.98[.]153	Exploit source IP	Active Mar 2026
89.46.237[.]33	Exploit source IP	Active Mar 2026
Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:136.0) Gecko/20100101 Firefox/136.0	Exploit HTTP User-Agent	Observed Jan 2026 and Mar 2026
b885946e72ad51dca6c70abc2f773506	Exploit TLS JA3	Observed Jan 2026 and Mar 2026
f80d3d09f61892c5846c854dd84ac403	Exploit TLS JA3	Observed Mar 2026
t13i1811h1_85036bcba153_b26ce05bbdd6	Exploit TLS JA4	Observed Jan 2026 and Mar 2026
t13i4311h1_c7886603b240_b26ce05bbdd6	Exploit TLS JA4	Observed Mar 2026
144.172.94[.]59	C2 Fallback IP	Active Mar 2026
199.217.99[.]121	C2 Fallback IP	Active Mar 2026
188.245.41[.]78	C2 Fallback IP	Active Mar 2026
144.172.110[.]106	Backend C2 IP	Active Mar 2026
95.217.22[.]175	Backend C2 IP	Active Mar 2026
37.27.244[.]222	Staging host IP	Active Mar 2026
hxxp://ebhmkoohccl45qesdbvrjqtyro2hmhkmh6vkyfyjjzfllm3ix72aqaid[.]onion/chat.php	Ransom negotiation portal	Active Mar 2026
cherryberry[.]click	Exploit Support Domain	Active Jan 2026
ms-server-default[.]com	Exploit Support Domain	Active Mar 2026
initialize-configs[.]com	Exploit Support Domain	Active Mar 2026
ms-global.first-update-server[.]com	Exploit Support Domain	Active Mar 2026
ms-sql-auth[.]com	Exploit Support Domain	Active Mar 2026
kolonialeru[.]com	Exploit Support Domain	Active Mar 2026
sclair.it[.]com	Exploit Support Domain	Active Mar 2026
browser-updater[.]com	C2 domain	Active Mar 2026
browser-updater[.]live	C2 domain	Active Mar 2026
os-update-server[.]com	C2 domain	Active Mar 2026
os-update-server[.]org	C2 domain	Active Mar 2026
os-update-server[.]live	C2 domain	Active Mar 2026
os-update-server[.]top	C2 domain	Active Mar 2026
d1caa376cb45b6a1eb3a45c5633c5ef75f7466b8601ed72c8022a8b3f6c1f3be	Offensive security tool (Certify)	Observed Mar 2026
6c8efbcef3af80a574cb2aa2224c145bb2e37c2f3d3f091571708288ceb22d5f	Screen locker	Observed Mar 2026

Organizations should take the following actions to protect against Interlock ransomware operations.

Immediate actions:

• Apply Cisco’s security patches for Cisco Secure Firewall Management Center
• Review logs for the indicators of compromise listed above
• Conduct security assessments to identify potential compromise
• Review ScreenConnect deployments for unauthorized installations

Detection opportunities:

• Monitor for PowerShell scripts staging data to network shares with hostname-based directory structures
• Detect Java ServletRequestListener registrations in web application contexts (unusual modifications to Java web applications)
• Identify HAProxy installations with aggressive log deletion cron jobs (proxy servers that erase their own logs every five minutes)
• Watch for TCP connections to unusual high-numbered ports (e.g., 45588)

Long-term measures:

• Implement defense-in-depth strategies with multiple layers of security controls
• Maintain continuous threat monitoring and hunting capabilities
• Ensure comprehensive logging with secure, centralized log storage (stored separately from systems that could be compromised)
• Regularly test incident response procedures for ransomware scenarios
• Educate security teams on Interlock’s tactics, techniques, and procedures

The real story here isn’t just about one vulnerability or one ransomware group—it’s about the fundamental challenge zero-day exploits pose to every security model. When attackers exploit vulnerabilities before patches exist, even the most diligent patching programs can’t protect you in that critical window. This is precisely why defense in depth is essential—layered security controls provide protection when any single control fails or hasn’t yet been deployed. Rapid patching remains foundational in vulnerability management, but defense in depth helps organizations not to be defenseless during the window between exploit and patch.

Amazon Threat Intelligence teams continue to monitor Interlock ransomware operations and will provide updates as additional information becomes available. The intelligence gathered from this campaign is being integrated into AWS security services to protect customers proactively.

Commercial AI services are enabling even unsophisticated threat actors to conduct cyberattacks at scale—a trend Amazon Threat Intelligence has been tracking closely. A recent investigation illustrates this shift: Amazon Threat Intelligence observed a Russian-speaking financially motivated threat actor leveraging multiple commercial generative AI services to compromise over 600 FortiGate devices across more than 55 countries from January 11 to February 18, 2026. No exploitation of FortiGate vulnerabilities was observed—instead, this campaign succeeded by exploiting exposed management ports and weak credentials with single-factor authentication, fundamental security gaps that AI helped an unsophisticated actor exploit at scale. This activity is distinguished by the threat actor’s use of multiple commercial GenAI services to implement and scale well-known attack techniques throughout every phase of their operations, despite their limited technical capabilities. AWS infrastructure was not observed to be involved in this campaign. Amazon Threat Intelligence is sharing these findings to help the broader security community defend against this activity.

This investigation highlights how commercial AI services can lower the technical barrier to entry for offensive cyber capabilities. The threat actor in this campaign is not known to be associated with any advanced persistent threat group with state-sponsored resources. They are likely a financially motivated individual or small group who, through AI augmentation, achieved an operational scale that would have previously required a significantly larger and more skilled team. Yet, based on our analysis of public sources, they successfully compromised multiple organizations’ Active Directory environments, extracted complete credential databases, and targeted backup infrastructure, a potential precursor to ransomware deployment. Notably, when this actor encountered hardened environments or more sophisticated defensive measures, they simply moved on to softer targets rather than persisting, underscoring that their advantage lies in AI-augmented efficiency and scale, not in deeper technical skill.

As we expect this trend to continue in 2026, organizations should anticipate that AI-augmented threat activity will continue to grow in volume from both skilled and unskilled adversaries. Strong defensive fundamentals remain the most effective countermeasure: patch management for perimeter devices, credential hygiene, network segmentation, and robust detection for post-exploitation indicators.

Through routine threat intelligence operations, Amazon Threat Intelligence identified infrastructure hosting malicious tooling associated with this campaign. The threat actor had staged additional operational files on the same publicly accessible infrastructure, including AI-generated attack plans, victim configurations, and source code for custom tooling. This inadequate operational security provided comprehensive visibility into the threat actor’s methodologies and the specific ways they leverage AI throughout their operations. It’s like an AI-powered assembly line for cybercrime, helping less skilled workers produce at scale.

The threat actor compromised globally dispersed FortiGate appliances, extracting full device configurations that yielded credentials, network topology information, and device configuration information. They then used these stolen credentials to connect to victim internal networks and conduct post-exploitation activities including Active Directory compromise, credential harvesting, and attempts to access backup infrastructure, consistent with pre-ransomware operations.

The threat actor’s initial access vector was credential-based access to FortiGate management interfaces exposed to the internet. Analysis of the actor’s tooling supported systematic scanning for management interfaces across ports 443, 8443, 10443, and 4443, followed by authentication attempts using commonly reused credentials.

FortiGate configuration files represent high-value targets because they contain:

• SSL-VPN user credentials with recoverable passwords
• Administrative credentials
• Complete network topology and routing information
• Firewall policies revealing internal architecture
• IPsec VPN peer configurations

The threat actor developed AI-assisted Python scripts to parse, decrypt, and organize these stolen configurations.

The campaign’s targeting appears opportunistic rather than sector-specific, consistent with automated mass scanning for vulnerable appliances. However, certain patterns suggest organizational-level compromise where multiple FortiGate devices belonging to the same entity were accessed. Amazon Threat Intelligence observed clusters where contiguous IP blocks or shared non-standard management ports indicated managed service provider deployments or large organizational networks. Concentrations of compromised devices were observed across South Asia, Latin America, the Caribbean, West Africa, Northern Europe, and Southeast Asia, among other regions.

Following VPN access to victim networks, the threat actor deploys a custom reconnaissance tool, with different versions written in both Go and Python. Analysis of the source code reveals clear indicators of AI-assisted development: redundant comments that merely restate function names, simplistic architecture with disproportionate investment in formatting over functionality, naive JSON parsing via string matching rather than proper deserialization, and compatibility shims for language built-ins with empty documentation stubs. While functional for the threat actor’s specific use case, the tooling lacks robustness and fails under edge cases—characteristics typical of AI-generated code used without significant refinement.

The tool automates the post-VPN reconnaissance workflow:

1. Ingesting target networks from VPN routing tables
2. Classifying networks by size
3. Running service discovery using gogo, an open-source port scanner
4. Automatically identifying SMB hosts and domain controllers
5. Integrating vulnerability scanning using Nuclei, an open-source vulnerability scanner, against discovered HTTP services to produce prioritized target lists.

Once inside victim networks, the threat actor follows a standard approach leveraging well-known open-source offensive tools.

Domain compromise: The threat actor’s operational documentation details the intended use of Meterpreter, an open-source post-exploitation toolkit, with the mimikatz module to perform DCSync attacks against domain controllers. This allowed the actor to extract NTLM password hashes from Active Directory. In confirmed compromises, the attacker obtained complete domain credential databases. In at least one case, the Domain Administrator account used a plaintext password that was either extracted from the FortiGate configuration through password reuse or was independently weak.

Lateral movement: Following domain compromise, the threat actor attempts to expand access through pass-the-hash/pass-the-ticket attacks against additional infrastructure, NTLM relay attacks using standard poisoning tools, and remote command execution on Windows hosts.

Backup infrastructure targeting: The threat actor specifically targeted Veeam Backup & Replication servers, deploying multiple tools for extracting credentials, including PowerShell scripts, compiled decryption tools, and exploitation attempts leveraging known Veeam vulnerabilities. Backup servers represent high-value targets because they typically store elevated credentials for backup operations, and compromising backup infrastructure positions an attacker to destroy recovery capabilities before deploying ransomware.

Limited exploitation success: The threat actor’s operational notes reference multiple CVEs across various targets (CVE-2019-7192, CVE-2023-27532, and CVE-2024-40711, among others). However, a critical finding from this analysis is that the threat actor largely failed when attempting to exploit anything beyond the most straightforward, automated attack paths. Their own documentation records repeated failures: targeted services were patched, required ports were closed, vulnerabilities didn’t apply to the target OS versions, . Their final operational assessment for one confirmed victim acknowledged that key infrastructure targets were “well-protected” with “no vulnerable exploitation vectors.”

Amazon Threat Intelligence analysis revealed that the actor uses at least two distinct commercial LLM providers throughout their operations.

AI-generated attack planning: The threat actor used AI to generate comprehensive attack methodologies complete with step-by-step exploitation instructions, expected success rates, time estimates, and prioritized task trees. These plans reference academic research on offensive AI agents, suggesting the actor follows emerging literature on AI-assisted penetration testing. The AI produces technically accurate command sequences, but the actor struggles to adapt when conditions differ from the plan. They cannot compile custom exploits, debug failed exploitation attempts, or creatively pivot when standard approaches fail.

Multi-model operational workflow: Amazon Threat Intelligence identified the actor using multiple AI services in complementary roles. One serves as the primary tool developer, attack planner, and operational assistant. A second is used as a supplementary attack planner when the actor needs help pivoting within a specific compromised network. In one observed instance, the actor submitted the complete internal topology of an active victim—IP addresses, hostnames, confirmed credentials, and identified services—and requested a step-by-step plan to compromise additional systems they could not access with their existing tools.

AI-generated tooling at scale: Beyond the reconnaissance framework, the actor’s infrastructure contains numerous scripts in multiple programming languages bearing hallmarks of AI generation, including configuration parsers, credential extraction tools, VPN connection automation, mass scanning orchestration, and result aggregation dashboards. The volume and variety of custom tooling would typically indicate a well-resourced development team. Instead, a single actor or very small group generated this entire toolkit through AI-assisted development.

Based on comprehensive analysis, Amazon Threat Intelligence assesses this threat actor as follows:

• Motivation: Suspected financially motivated, based on widespread, indiscriminate targeting and low sophistication
• Language: Russian-speaking, based on extensive Russian-language operational documentation
• Skill level: Low-to-medium baseline technical capability, significantly augmented by AI. The actor can run standard offensive tools and automate routine tasks but struggles with exploit compilation, custom development, and creative problem-solving during live operations
• AI dependency: Extensive reliance across all operational phases. AI is used for tool development, attack planning, command generation, and operational reporting across multiple commercial LLM providers
• Operational scale: Broad. Compromised devices across dozens of countries, with evidence of sustained operations over an extended period
• Post-exploitation depth: Shallow. Repeated failures against hardened or non-standard targets, with a pattern of moving on rather than persisting when automated approaches fail
• Operational security: Inadequate. Detailed operational plans, credentials, and victim data stored without encryption alongside tooling

Amazon Threat Intelligence remains committed to helping protect customers and the broader internet ecosystem by actively investigating and disrupting threat actors.

Upon discovering this campaign, Amazon Threat Intelligence took the following actions:

• Shared actionable intelligence, including indicators of compromise, with relevant partners
• Collaborated with industry partners to broaden visibility into the campaign and support coordinated defense efforts

Through these efforts, Amazon helped reduce the threat actor’s operational effectiveness and enabled organizations across multiple countries to take steps to disrupt the efficacy of the campaign.

This campaign succeeded through a combination of exposed management interfaces, weak credentials, and single-factor authentication—all fundamental security gaps that AI helped an unsophisticated actor exploit at scale. This underscores that strong security fundamentals are powerful defenses against AI-augmented threats. Organizations should review and implement the following.

Organizations running FortiGate appliances should take immediate action:

• Ensure management interfaces are not exposed to the internet. If remote administration is required, restrict access to known IP ranges and use a bastion host or out-of-band management network
• Change all default and common credentials on FortiGate appliances, including administrative and VPN user accounts
• Rotate all SSL-VPN user credentials, particularly for any appliance whose management interface was or may have been internet-accessible
• Implement multi-factor authentication for all administrative and VPN access
• Review FortiGate configurations for unauthorized administrative accounts or policy changes
• Audit VPN connection logs for connections from unexpected geographic locations

Given the extraction of credentials from FortiGate configurations:

• Audit for password reuse between FortiGate VPN credentials and Active Directory domain accounts
• Implement multi-factor authentication for all VPN access
• Enforce unique, complex passwords for all accounts, particularly Domain Administrator accounts
• Review and rotate service account credentials, especially those used in backup infrastructure

Organizations that may have been affected should monitor for:

• Unexpected DCSync operations (Event ID 4662 with replication-related GUIDs)
• New scheduled tasks named to mimic legitimate Windows services
• Unusual remote management connections from VPN address pools
• LLMNR/NBT-NS poisoning artifacts in network traffic
• Unauthorized access to backup credential stores
• New accounts with names designed to blend with legitimate service accounts

The threat actor’s focus on backup infrastructure highlights the importance of:

• Isolating backup servers from general network access
• Patching backup software against known credential extraction vulnerabilities
• Monitoring for unauthorized PowerShell module loading on backup servers
• Implementing immutable backup copies that cannot be modified even with administrative access

For organizations using AWS:

• Enable Amazon GuardDuty for threat detection, including monitoring for unusual API calls and credential usage patterns
• Use Amazon Inspector to automatically scan for software vulnerabilities and unintended network exposure
• Use AWS Security Hub to maintain continuous visibility into your security posture
• Use AWS Systems Manager Patch Manager to maintain patching compliance across EC2 instances running network appliances
• Review IAM access patterns for signs of credential replay following any suspected network device compromise

This campaign’s reliance on legitimate open-source tools—including Impacket, gogo, Nuclei, and others—means that traditional IOC-based detection has limited effectiveness. These tools are widely used by penetration testers and security professionals, and their presence alone is not indicative of compromise. Organizations should investigate context around matches, prioritizing behavioral detection (anomalous VPN authentication patterns, unexpected Active Directory replication, lateral movement from VPN address pools) over signature-based approaches.

IOC Value	IOC Type	First Seen	Last Seen	Annotation
212[.]11.64.250	IPv4	1/11/2026	2/18/2026	Threat actor infrastructure used for scanning and exploitation operations
185[.]196.11.225	IPv4	1/11/2026	2/18/2026	Threat actor infrastructure used for threat operations