October 3, 2023

Unveiling ViperSoftX: A Darktrace Investigation

Read about the ViperSoftX threat and how Darktrace's innovative detection methods exposed this cyber intrusion and its potential impacts.

Written by

Zoe Tilsiter

Cyber Analyst

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

Zoe Tilsiter

Cyber Analyst

Oct 2023

Fighting Info-Stealing Malware

The escalating threat posed by information-stealing malware designed to harvest and steal the sensitive data of individuals and organizations alike has become a paramount concern for security teams across the threat landscape. In direct response to security teams improving their threat detection and prevention capabilities, threat actors are forced to continually adapt and advance their techniques, striving for greater sophistication to ensure they can achieve the malicious goals.

What is ViperSoftX?

ViperSoftX is an information stealer and Remote Access Trojan (RAT) malware known to steal privileged information such as cryptocurrency wallet addresses and password information stored in browsers and password managers. It is commonly distributed via the download of cracked software from multiple sources such as suspicious domains, torrent downloads, and key generators (keygens) from third-party sites.

ViperSoftX was first observed in the wild in 2020 [1] but more recently, new strains were identified in 2022 and 2023 utilizing more sophisticated detection evasion techniques, making it more difficult for security teams to identify and analyze. This includes using more advanced encryption methods alongside monthly changes to command-and-control servers (C2) [2], using dynamic-link library (DLL) sideloading for execution techiques, and subsequently loading a malicious browser extension upon infection which works as an independent info-stealer named VenomSoftX [3].

Between February and June 2023, Darktrace detected activity related to the VipersoftX and VenomSoftX information stealers on the networks of more than 100 customers across its fleet. Darktrace DETECT™ was able to successfully identify the anomalous network activity surrounding these emerging information stealer infections and bring them to the attention of the customers, while Darktrace RESPOND™, when enabled in autonomous response mode, was able to quickly intervene and shut down malicious downloads and data exfiltration attempts.

ViperSoftX Attack & Darktrace Coverage

In cases of ViperSoftX information stealer activity observed by Darktrace, the initial infection was caused through the download of malicious files from multimedia sites, endpoints of cracked software like Adobe Illustrator, and torrent sites. Endpoint users typically unknowingly download the malware from these endpoints with a sideloaded DLL, posing as legitimate software executables.

Darktrace detected multiple downloads from such multimedia sites and endpoints related to cracked software and BitTorrent, which were likely representative of the initial source of ViperSoftX infection. Darktrace DETECT models such as ‘Anomalous File / Anomalous Octet Stream (No User Agent)’ breached in response to this activity and were brought to the immediate attention of customer security teams. In instances where Darktrace RESPOND was configured in autonomous response mode, Darktrace was able to enforce a pattern of life on offending devices, preventing them from downloading malicious files. This ensures that devices are limited to conducting only their pre-established expected activit, minimizing disruption to the business whilst targetedly mitigating suspicious file downloads.

The downloads are then extracted, decrypted and begin to run on the device. The now compromised device will then proceed to make external connections to C2 servers to retrieve secondary PowerShell executable. Darktrace identified that infected devices using PowerShell user agents whilst making HTTP GET requests to domain generation algorithm (DGA) ViperSoftX domains represented new, and therefore unusual, activity in a large number of cases.

For example, Darktrace detected one customer device making an HTTP GET request to the endpoint ‘chatgigi2[.]com’, using the PowerShell user agent ‘Mozilla/5.0 (Windows NT; Windows NT 10.0; en-US) WindowsPowerShell/5.1.19041.2364’. This new activity triggered a number of DETECT models, including ‘Anomalous Connection / PowerShell to Rare External’ and ‘Device / New PowerShell User Agent’. Repeated connections to these endpoints also triggered C2 beaconing models including:

Compromise / Agent Beacon (Short Period)
Compromise / Agent Beacon (Medium Period)
Compromise / Agent Beacon (Long Period)
Compromise / Quick and Regular Windows HTTP Beaconing
Compromise / SSL or HTTP Beacon

Although a large number of different DGA domains were detected, commonalities in URI formats were seen across affected customers which matched formats previously identified as ViperSoftX C2 communication by open-source intelligence (OSINT), and in other Darktrace investigations.

URI paths for example, were always of the format /api/, /api/v1/, /v2/, or /v3/, appearing to detail version number, as can be seen in Figure 1.

*Figure 1: A Packet Capture (PCAP) taken from Darktrace showing a connection made to a ViperSoftX C2 endpoint containing versioning information, consistent with ViperSoftX pattern of communication.*

Before the secondary PowerShell executables are loaded, ViperSoftX takes a digital fingerprint of the infected machine to gather its configuration details, and exfiltrates them to the C2 server. These include the computer name, username, Operating System (OS), and ensures there are no anti-virus or montoring tools on the device. If no security tool are detected, ViperSoftX then downloads, decrypts and executes the PowerShell file.

Following the GET requests Darktrace observed numerous devices performing HTTP POST requests and beaconing connections to ViperSoftX endpoints with varying globally unique identifiers (GUIDs) within the URIs. These connections represented the exfiltration of device configuration details, such as “anti-virus detected”, “app used”, and “device name”. As seen on another customer’s deployment, this caused the model ‘Anomalous Connection / Multiple HTTP POSTs to Rare Hostname’ to breach, which was also detected by Cyber AI Analyst as seen in Figure 2.

*Figure 2: Cyber AI Analyst’s detection of HTTP POSTs being made to apibiling[.]com, a ViperSoftX C2 endpoint.*

The malicious PowerShell download then crawls the infected device’s systems and directories looking for any cryptocurrency wallet information and password managers, and exfiltrates harvest data to the C2 infrastructure. The C2 server then provides further browser extensions to Chromium browsers to be downloaded and act as a separate stand-alone information stealer, also known as VenomSoftX.

Similar to the initial download of ViperSoftX, these malicious extensions are disguised as legitimate browser extensions to evade the detection of security teams. VenomSoft X, in turn, searches through and attempts to gather sensitive data from password managers and crypto wallets stored in user browsers. Using this information, VenomSoftX is able to redirect crypocurrency transactions by intercepting and manipulating API requests between the sender and the intended recipient, directing the cryptocurrency to the attacker instead [3].

Following investigation into VipersoftX activity across the customer base, Darktrace notified all affected customers and opened Ask the Expert (ATE) tickets through which customer’s could directly contact the analyst team for support and guidance in the face on the information stealer infection.

How did the attack bypass the rest of the security stack?

As previously mentioned, both the initial download of ViperSoftX and the subsequent download of the VenomX browser extension are disguised as legitimate software or browser downloads. This is a common technique employed by threat actors to infect target devices with malicious software, while going unnoticed by security teams traditional security measures. Furthermore, by masquerading as a legitimate piece of software endpoint users are more likely to trust and therefore download the malware, increasing the likelihood of threat actor’s successfully carrying out their objectives. Additionally, post-infection analysis of shellcode, the executable code used as the payload, is made significantly more difficult by VenomSoftX’s use of bytemapping. Bytemapping prevents the encryption of shellcodes without its corresponding byte map, meaning that the payloads cannot easily be decrypted and analysed by security researchers. [3]

ViperSoftX also takes numerous attempts to prevent their C2 infrastructure from being identified by blocking access to it on browsers, and using multiple DGA domains, thus renderring defunct traditional security measures that rely on threat intelligence and static lists of indicators of compromise (IoCs).

Fortunately for Darktrace customers, Darktrace’s anomaly-based approach to threat detection means that it was able to detect and alert customers to this suspicious activity that may have gone unnoticed by other security tools.

Insights/Conclusion

Faced with the challenge of increasingly competent and capable security teams, malicious actors are having to adopt more sophisticated techniques to successfully compromise target systems and achieve their nefarious goals.

ViperSoftX information stealer makes use of numerous tactics, techniques and procedures (TTPs) designed to fly under the radar and carry out their objectives without being detected. ViperSoftX does not rely on just one information stealing malware, but two with the subsequent injection of the VenomSoftX browser extension, adding an additional layer of sophistication to the informational stealing operation and increasing the potential yield of sensitive data. Furthermore, the use of evasion techniques like disguising malicious file downloads as legitimate software and frequently changing DGA domains means that ViperSoftX is well equipped to infiltrate target systems and exfiltrate confidential information without being detected.

However, the anomaly-based detection capabilities of Darktrace DETECT allows it to identify subtle changes in a device’s behavior, that could be indicative of an emerging compromise, and bring it to the customer’s security team. Darktrace RESPOND is then autonomously able to take action against suspicious activity and shut it down without latency, minimizing disruption to the business and preventing potentially significant financial losses.

Credit to: Zoe Tilsiter, Senior Cyber Analyst, Nathan Lorenzo, Cyber Analyst.

Appendices

References

[1] https://www.fortinet.com/blog/threat-research/vipersoftx-new-javascript-threat

[2] https://www.trendmicro.com/en_us/research/23/d/vipersoftx-updates-encryption-steals-data.html

[3] https://decoded.avast.io/janrubin/vipersoftx-hiding-in-system-logs-and-spreading-venomsoftx/

Darktrace DETECT Model Detections

· Anomalous File / Anomalous Octet Stream (No User Agent)

· Anomalous Connection / PowerShell to Rare External

· Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

· Anomalous Connection / Lots of New Connections

· Anomalous Connection / Multiple Failed Connections to Rare Endpoint

· Anomalous Server Activity / Outgoing from Server

· Compromise / Large DNS Volume for Suspicious Domain

· Compromise / Quick and Regular Windows HTTP Beaconing

· Compromise / Beacon for 4 Days

· Compromise / Suspicious Beaconing Behaviour

· Compromise / Large Number of Suspicious Failed Connections

· Compromise / Large Number of Suspicious Successful Connections

· Compromise / POST and Beacon to Rare External

· Compromise / DGA Beacon

· Compromise / Agent Beacon (Long Period)

· Compromise / Agent Beacon (Medium Period)

· Compromise / Agent Beacon (Short Period)

· Compromise / Fast Beaconing to DGA

· Compromise / SSL or HTTP Beacon

· Compromise / Slow Beaconing Activity To External Rare

· Compromise / Beaconing Activity To External Rare

· Compromise / Excessive Posts to Root

· Compromise / Connections with Suspicious DNS

· Compromise / HTTP Beaconing to Rare Destination

· Compromise / High Volume of Connections with Beacon Score

· Compromise / Sustained SSL or HTTP Increase

· Device / New PowerShell User Agent

· Device / New User Agent and New IP

Darktrace RESPOND Model Detections

· Antigena / Network / External Threat / Antigena Suspicious File Block

· Antigena / Network / External Threat / Antigena File then New Outbound Block

· Antigena / Network / External Threat / Antigena Watched Domain Block

· Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

· Antigena / Network / External Threat / Antigena Suspicious Activity Block

· Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block

· Antigena / Network / Insider Threat / Antigena Large Data Volume Outbound Block

· Antigena / Network / External Threat / Antigena Suspicious File Pattern of Life Block

· Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach

List of IoCs

Indicator - Type - Description

ahoravideo-blog[.]com - Hostname - ViperSoftX C2 endpoint

ahoravideo-blog[.]xyz - Hostname - ViperSoftX C2 endpoint

ahoravideo-cdn[.]com - Hostname - ViperSoftX C2 endpoint

ahoravideo-cdn[.]xyz - Hostname - ViperSoftX C2 endpoint

ahoravideo-chat[.]com - Hostname - ViperSoftX C2 endpoint

ahoravideo-chat[.]xyz - Hostname - ViperSoftX C2 endpoint

ahoravideo-endpoint[.]xyz - Hostname - ViperSoftX C2 endpoint

ahoravideo-schnellvpn[.]com - Hostname - ViperSoftX C2 endpoint

ahoravideo-schnellvpn[.]xyz - Hostname - ViperSoftX C2 endpoint

apibilng[.]com - Hostname - ViperSoftX C2 endpoint

arrowlchat[.]com - Hostname - ViperSoftX C2 endpoint

bideo-blog[.]com - Hostname - ViperSoftX C2 endpoint

bideo-blog[.]xyz - Hostname - ViperSoftX C2 endpoint

bideo-cdn[.]com - Hostname - ViperSoftX C2 endpoint

bideo-cdn[.]xyz - Hostname - ViperSoftX C2 endpoint

bideo-chat[.]com - Hostname - ViperSoftX C2 endpoint

bideo-chat[.]xyz - Hostname - ViperSoftX C2 endpoint

bideo-endpoint[.]com - Hostname - ViperSoftX C2 endpoint

bideo-endpoint[.]xyz - Hostname - ViperSoftX C2 endpoint

bideo-schnellvpn[.]com - Hostname - ViperSoftX C2 endpoint

chatgigi2[.]com - Hostname - ViperSoftX C2 endpoint

counter[.]wmail-service[.]com - Hostname - ViperSoftX C2 endpoint

fairu-cdn[.]xyz - Hostname - ViperSoftX C2 endpoint

fairu-chat[.]xyz - Hostname - ViperSoftX C2 endpoint

fairu-endpoint[.]com - Hostname - ViperSoftX C2 endpoint

fairu-schnellvpn[.]com - Hostname - ViperSoftX C2 endpoint

fairu-schnellvpn[.]xyz - Hostname - ViperSoftX C2 endpoint

privatproxy-blog[.]com - Hostname - ViperSoftX C2 endpoint

privatproxy-blog[.]xyz - Hostname - ViperSoftX C2 endpoint

privatproxy-cdn[.]com - Hostname - ViperSoftX C2 endpoint

privatproxy-cdn[.]xyz - Hostname - ViperSoftX C2 endpoint

privatproxy-endpoint[.]xyz - Hostname - ViperSoftX C2 endpoint

privatproxy-schnellvpn[.]com - Hostname - ViperSoftX C2 endpoint

privatproxy-schnellvpn[.]xyz - Hostname - ViperSoftX C2 endpoint

static-cdn-349[.]net - Hostname - ViperSoftX C2 endpoint

wmail-blog[.]com - Hostname - ViperSoftX C2 endpoint

wmail-cdn[.]xyz - Hostname - ViperSoftX C2 endpoint

wmail-chat[.]com - Hostname - ViperSoftX C2 endpoint

wmail-schnellvpn[.]com - Hostname - ViperSoftX C2 endpoint

wmail-schnellvpn[.]xyz - Hostname - ViperSoftX C2 endpoint

Mozilla/5.0 (Windows NT; Windows NT 10.0; en-US) WindowsPowerShell/5.1.19041.2364 - User Agent -PowerShell User Agent

MITRE ATT&CK Mapping

‍Tactic - Technique - Notes

Command and Control - T1568.002 Dynamic Resolution: Domain Generation Algorithms

Command and Control - T1321 Data Encoding

Credential Access - T1555.005 Credentials from Password Stores: Password Managers

Defense Evasion - T1027 Obfuscated Files or Information

Execution - T1059.001 Command and Scripting Interpreter: PowerShell

Execution - T1204 User Execution T1204.002 Malicious File

Persistence - T1176 Browser Extensions - VenomSoftX specific

Persistence, Privilege Escalation, Defense Evasion - T1574.002 Hijack Execution Flow: DLL Side-Loading

Written by

Zoe Tilsiter

Cyber Analyst

Inside the SOC

Written by

Zoe Tilsiter

Cyber Analyst

Product Marketing Manager, OT Security & Compliance

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OT

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 detection

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.

About the author

Pallavi Singh

Product Marketing Manager, OT Security & Compliance

Blog

Network

November 20, 2025

Xillen Stealer Updates to Version 5 to Evade AI Detection

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.

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

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.

Figure 3: Screenshot of dead code function.

AI Evasion

‘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

Figure 6: Screenshot of Kubernetes inject function.

Biometric Collector

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

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

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 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

About the author

Tara Gould

Threat Researcher

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Insights/Conclusion

Appendices

References

Darktrace DETECT Model Detections

Darktrace RESPOND Model Detections

List of IoCs

MITRE ATT&CK Mapping

Xillen Stealer Updates to Version 5 to Evade AI Detection

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

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Indicators of Compromise (IoCs)