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August 3, 2022

The Risks of Remote Access Tools

Discover how remote access tools in exploitations across OT/ICS and corporate environments benefit from Darktrace's product suite.
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
Dylan Hinz
Cyber Analyst
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03
Aug 2022

Understanding remote access tools

In 2022, remote access tools continue to provide versatile support to organizations. By controlling devices remotely from across the globe, IT teams save on response costs, travel times, and can receive remote support from external parties like contractors [1 & 2]. This is particularly relevant in cases involving specialty machines such as OT/ICS systems where physical access is sometimes limited. These tools, however, come with their own risks. The following blog will discuss these risks and how they can be addressed (particularly in OT environments) by looking at two exploit examples from the popular sphere and within the Darktrace customer base. 

What are remote access tools?

One of the most popular remote tools is TeamViewer, a comprehensive videoconferencing and remote management tool which can be used on both desktop and handheld devices[3]. Like other sophisticated tools, when it works as intended, it can seem like magic. However, remote access tools can be exploited and may grant privileged network access to potential threat actors. Although TeamViewer needs to be installed on both perpetrator and victim devices, if an attacker has access to a misconfigured TeamViewer device, it becomes trivial to establish a foothold and deploy malware. 

How secure is remote access?

Security vulnerabilities in remote access tools

In early 2021, remote access tooling was seen on a new scale against the City of Oldsmar’s water treatment plant [4] (Figure 1). Oldsmar manages chemical concentration levels in the water for a 15,000-person city. The water treatment plant had been using TeamViewer to allow employees to share screens and work through IT issues. However, in February an employee noticed he had lost control of his mouse cursor. Initially he was unconcerned; the employee assumed that the cursor was being controlled by his boss, who regularly connected to the computer to monitor the facility’s systems. A few hours later though, the employee again saw his cursor moving out of his control and this time noticed that it was attempting to change levels of sodium hydroxide in the water supply (which is extremely dangerous for human consumption). Thankfully, the employee was able to quickly spot the changes and return them to their normal level. When looking back at the event, the key question posed by officials was where exactly the vulnerability was located in their security stack. [5]. The answer was unclear.

Photograph of compromised water plant in Florida 
Figure 1: Photograph of compromised water plant in Florida 

Tactics and strategies

When attackers get initial network access, the primary challenge for any enterprise is identifying a) that a device compromise has happened and b) how it happened. These were the same challenges seen in the Oldsmar attack. When the first physical signs of compromise occurred (cursor movement), the impacted user was still unsure whether the activity was malicious. A detailed investigation from Dragos revealed the how: evidence of a watering hole, reconnaissance activity a month prior, a targeted variant of the Tofsee botnet, and the potential presence of two separate threat actors [6 & 7]. The answer to both questions pointed to a complex attack. However, with Darktrace these questions become less important. 

How Darktrace stops compromised remote access

Darktrace does not rely on signatures but instead has AI-based models for live detection of these tools and anomalies within the wider network. Regardless of the security ‘hole’, live detection gives security teams the potential to respond in near-live time.

According to Darktrace’s Chief Product Officer, Max Heinemeyer, the Oldsmar attack was possible because it “Abused off-the-shelf tools that were already used by the client, specifically TeamViewer. This tactic, which targeted the domain controller as the initial vector, made the malware deployment easy and effective.” [8]. 

Darktrace has multiple DETECT models to provide visibility over anomalous TeamViewer or remote access tool usage:

·      Compliance / Incoming Remote Access Tool

·      Compliance / Remote Management Tool On Client

·      Compliance / Remote Management Tool On Server

·      Device / Activity Identifier / Teamviewer 

General incoming privileged connections:

·      Compliance / Incoming Remote Desktop

·      Compliance / Incoming SSH

Industrial DETECT can also highlight any new or unusual changes in ICS/OT systems:

·      ICS / Incoming ICS Command

·      ICS / Incoming RDP And ICS Commands

·      ICS / Uncommon ICS Error

Darktrace gives security teams the opportunity for a proactive response, and it is up to those teams to utilize that opportunity. In recent months our SOC Team have also seen remote access controls being abused for high-profile threats. In one example, Darktrace detected a ransomware attack supported by the installation of AnyDesk. 

Initial detection of compromise

In May a company’s mail server was detected making multiple external requests for an unusual file ‘106.exe’ using a PowerShell agent (6b79549200af33bf0322164f8a4d56a0fa08a5a62ab6a5c93a6eeef2065430ce). Although some requests were directed to sinkholes, many were otherwise successful. Subsequently a DDL file with hash f126ce9014ee87de92e734c509e1b5ab71ffb2d5a8b27171da111f96f3ba0e75 (marked by VirusTotal as malicious) was downloaded. This was followed by the installation of AnyDesk: a remote access tool likely deployed for backdoor purposes during further compromises. It is clear the threat actor then moved on to reconnaissance, with new Mimikatz use and a large volume of ICMP and SMBv.1 scanning sessions using a default credential. DCE-RPC calls were also made to the Netlogon service, suggesting a possible attempt to exploit 2020’s Zerologon vulnerability (CVE-2020-1472) [9]. When the customer then discovered a ransom note pertaining to LV (repurposed REvil), Darktrace analysts helped them to re-configure Darktrace RESPOND and turn it to active rather than human confirmation mode (Figure 2). 

Figure 2: Capture of LV ransom note provided by customer

Whilst in this instance the tool was not used for initial access, it was still an important contingency tool to ensure the threat actor’s persistency as the customer tried to respond to the ongoing breach. Yet it was the visibility provided by Darktrace model detection and changes to RESPOND configuration which ensured the customer kept up with this actor and reduced the impact of the attack. 

Looking back at Oldsmar, it is clear that being aware of remote access tools is only half the battle. More importantly, most organizations are asking if their use in attacks can be prevented in the first place. As an off-the-shelf tool, restricting TeamViewer use seems like an easy solution but such tools are often essential for maintenance and support operations. Even if limited to privileged users, these accounts are also subject to potential compromise. Instead, companies can take a large-scale view and consider the environment in which the Oldsmar attack occurred. 

How IT & OT convergence complicated this attack

In this context, the separation of OT and IT systems is a potential solution - if attackers cannot access at-risk systems, then they also cannot attack those systems. However, with recent discourse around the IT-OT convergence and increased use of IoT devices, this separation is increasingly challenging to implement [10]. Complex networking designs, stringent patching requirements and ever-changing business/operational needs are all big considerations when establishing industrial security. In fact, Tenable’s CEO Amit Yoran encouraged less separation following Oldsmar: “There’s business reasons and efficiency reasons that you might want to connect those to be able to predict when parts are going to fail or when outages are going to occur [sic].” [11]. 

When neither addressing remote access use or industrial set-up provides a quick solution, then security teams need to look to third-party support to stop similar attacks. In addition to Darktrace DETECT, our Darktrace PREVENT range with PREVENT/Attack Surface Management (ASM) can also alert security teams to internet-facing devices at risk of remote access exploitation. ASM actively queries the Shodan API for open ports on company websites and exposed servers. This highlights those assets which might be vulnerable to this type of remote access.   

Conclusion

In conclusion, TeamViewer and other remote access tools offer a lot of convenience for security teams but also for attackers. Attackers can remotely access important systems including those in the industrial network and install malware using remote access tools as leverage. Security teams need to know both their normal authorized activities and how to enforce them. With Darktrace DETECT, the tools are given transparency, with Darktrace RESPOND they can be blocked, and now Darktrace PREVENT/ASM helps to mitigate the risk of attack before it happens. As the professional world continues to embrace hybrid working, it becomes increasingly crucial to embrace these types of products and ensure protection against the dangers of unwanted remote access. 

Thanks to Connor Mooney for his contributions to this blog.

Appendices

References 

[1] https://goabacus.com/advantages-and-disadvantages-of-remote-access-service/ 

[2] https://blog.ericom.com/advantages-of-remote-access/ 

[3] https://www.teamviewer.com/en/documents/ 

[4] https://www.wired.com/story/oldsmar-florida-water-utility-hack/ 

[5 & 11] https://www.bankinfosecurity.com/ot-it-integration-raises-risk-for-water-providers-experts-say-a-18841 

[6] https://www.dragos.com/blog/industry-news/a-new-water-watering-hole/ 

[7] https://www.dragos.com/blog/industry-news/recommendations-following-the-oldsmar-water-treatment-facility-cyber-attack/

[8] https://customerportal.darktrace.com/darktrace-blogs/get-blog/53  

[9] https://www.crowdstrike.com/blog/cve-2020-1472-zerologon-security-advisory/

[10] https://www.mckinsey.com/business-functions/operations/our-insights/converge-it-and-ot-to-turbocharge-business-operations-scaling-power

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
Dylan Hinz
Cyber Analyst

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November 20, 2025

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

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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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