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April 8, 2024

Balada Injector: Darktrace’s Investigation into the Malware Exploiting WordPress Vulnerabilities

This blog explores Darktrace’s detection of Balada Injector, a malware known to exploit vulnerabilities in WordPress to gain unauthorized access to networks. Darktrace was able to define numerous use-cases within customer environments which followed previously identified patterns of activity spikes across multiple weeks.
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 Torres
Cyber Analyst
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08
Apr 2024

Introduction

With millions of users relying on digital platforms in their day-to-day lives, and organizations across the world depending on them for their business operations, they have inevitably also become a prime target for threat actors. The widespread exploitation of popular services, websites and platforms in cyber-attacks highlights the pervasive nature of malicious actors in today’s threat landscape.

A prime illustration can be seen within the content management system WordPress. Its widespread use and extensive plug-in ecosystem make it an attractive target for attackers aiming to breach networks and access sensitive data, thus leading to routine exploitation attempts. In the End of Year Threat Report for 2023, for example, Darktrace reported that a vulnerability in one WordPress plug-in, namely an authentication bypass vulnerability in miniOrange's Social Login and Register. Darktrace observed it as one of the most exploited vulnerabilities observed across its customer base in the latter half of 2023.

Between September and October 2023, Darktrace observed a string of campaign-like activity associated with Balada Injector, a malware strain known to exploit vulnerabilities in popular plug-ins and themes on the WordPress platform in order to inject a backdoor to provide further access to affected devices and networks. Thanks to its anomaly-based detection, Darktrace DETECT™ was able to promptly identify suspicious connections associated with the Balada Injector, ensuring that security teams had full visibility over potential post-compromise activity and allowing them to act against offending devices.

What is Balada Injector?

The earliest signs of the Balada Injector campaign date back to 2017; however, it was not designated the name Balada Injector until December 2022 [1]. The malware utilizes plug-ins and themes in WordPress to inject a backdoor that redirects end users to malicious and fake sites. It then exfiltrates sensitive information, such as database credentials, archive files, access logs and other valuable information which may not be properly secured [1]. Balada Injector compromise activity is also reported to arise in spikes of activity that emerge every couple of weeks [4].

In its most recent attack activity patterns, specifically in September 2023, Balada Injector exploited a cross-site scripting (XSS) vulnerability in CVE-2023-3169 associated with the tagDiv composer plug-in. Some of the injection methods observed included HTML injections, database injections, and arbitrary file injections. In late September 2023, a similar pattern of behavior was observed, with the ability to plant a backdoor that could execute PHP code and install a malicious WordPress plug-in, namely ‘wp-zexit’.

According to external security researchers [2], the most recent infection activity spikes for Balada Injector include the following:

Pattern 1: ‘stay.decentralappps[.]com’ injections

Pattern 2: Autogenerated malicious WordPress users

Pattern 3: Backdoors in the Newspaper theme’s 404.php file

Pattern 4: Malicious ‘wp-zexit’ plug-in installation

Pattern 5: Three new Balada Injector domains (statisticscripts[.]com, dataofpages[.]com, and listwithstats[.]com)

Pattern 6: Promsmotion[.]com domain

Darktrace’s Coverage of Balada Injector

Darktrace detected devices across multiple customer environments making external connections to the malicious Balada Injector domains, including those associated with aforementioned six infection activity patterns. Across the incidents investigated by Darktrace, much of the activity appeared to be associated with TLS/SSL connectivity, related to Balada Injector endpoints, which correlated with the reported infection patterns of this malware. The observed hostnames were all recently registered and, in most cases, had IP geolocations in either the Netherlands or Ukraine.

In the observed cases of Balada Injector across the Darktrace fleet, Darktrace RESPOND™ was not active on the affected customer environments. If RESPOND had been active and enabled in autonomous response mode at the time of these attacks, it would have been able to quickly block connections to malicious Balada Injector endpoints as soon as they were identified by DETECT, thereby containing the threat.

Looking within the aforementioned activity patterns, Darktrace identified a Balada Injector activity within a customer’s environment on October 16, 2023, when a device was observed making a total of 9 connection attempts to ‘sleep[.]stratosbody[.]com’, a domain that had previously been associated with the malware [2]. Darktrace recognized that the endpoint had never been seen on the network, with no other devices having connected to it previously, thus treated it as suspicious.

Figure 1: The connection details above demonstrate 100% rare external connections were made from the internal device to the ‘sleep[.]stratosbody[.]com’ endpoint.

Similarly, on September 21, 2023, Darktrace observed a device on another customer network connecting to an external IP that had never previously been observed on the environment, 111.90.141[.]193. The associated server name was a known malicious endpoint, ‘stay.decentralappps[.]com’, known to be utilized by Balada Injector to host malicious scripts used to compromise WordPress sites. Although the ‘stay.decentralappps[.]com’ domain was only registered in September 2023, it was reportedly used in the redirect chain of the aforementioned stratosbody[.com] domain [2]. Such scripts can be used to upload backdoors, including malicious plug-ins, and create blog administrators who can perform administrative tasks without having to authenticate [2].

Figure 2: Advance Search results displaying the metadata logs surrounding the unusual connections to ‘stay.decentralappps[.]com’. A total of nine HTTP CONNECT requests were observed, with status messages “Proxy Authorization Required” and “Connection established”.

Darktrace observed additional connections within the same customer’s environment on October 10 and October 18, specifically SSL connections from two distinct source devices to the ‘stay.decentralappps[.]com’ endpoint. Within these connections, Darktrace observed the normalized JA3 fingerprints, “473f0e7c0b6a0f7b049072f4e683068b” and “aa56c057ad164ec4fdcb7a5a283be9fc”, the latter of which corresponds to GitHub results mentioning a Python client (curl_cffi) that is able to impersonate the TLS signatures of browsers or JA3 fingerprints [8].

Figure 3: Advanced Search query results showcasing Darktrace’s detection of SSL connections to ‘stay.decentralappps[.]com over port 443.

On September 29, 2023, a device on a separate customer’s network was observed connecting to the hostname ‘cdn[.]dataofpages[.]com’, one of the three new Balada Injector domains identified as part of the fifth pattern of activity outlined above, using a new SSL certificate via port 443. Multiple open-source intelligence (OSINT) vendors flagged this domain as malicious and associated with Balada Injector malware [9].

Figure 4: The Model Breach Event Log detailing the Balada Injector-related connections observed causing the ‘Anomalous External Activity from Critical Network Device’ DETECT model to breach.

On October 2, 2023, Darktrace observed the device of another customer connecting to the rare hostname, ‘js.statisticscripts[.]com’ with the IP address 185.39.206[.]161, both of which had only been registered in late September and are known to be associated with the Balada Injector.

Figure 5: Model Breach Event Log detailing connections to the hostname ‘js.statisticscripts[.]com’ over port 137.

On September 13, 2023, Darktrace identified a device on another customer’s network connecting to the Balada Injector endpoint ‘stay.decentralappps[.]com’ endpoint, with the destination IP 1.1.1[.]1, using the SSL protocol. This time, however, Darktrace also observed the device making subsequent connections to ‘get.promsmotion[.]com’ a subdomain of the ‘promsmotion[.]com’ domain. This domain is known to be used by Balada Injector actors to host malicious scripts that can be injected into the WordPress Newspaper theme as potential backdoors to be leveraged by attackers.

In a separate case observed on September 14, Darktrace identified a device on another environment connecting to the domain ‘collect[.]getmygateway[.]com’ with the IP 88.151.192[.]254. No other device on the customer’s network had visited this endpoint previously, and the device in question was observed repeatedly connecting to it via port 443 over the course of four days. While this specific hostname had not been linked with a specific activity pattern of Balada Injector, it was reported as previously associated with the malware in September 2023 [2].

Figure 6: Model Breach Event Log displaying a customer device making repeated connections to the endpoint ‘collect[.]getmygateway[.]com’, breaching the DETECT model ‘Repeating Connections Over 4 Days’.

In addition to DETECT’s identification of this suspicious activity, Darktrace’s Cyber AI Analyst™ also launched its own autonomous investigation into the connections. AI Analyst was able to recognize that these separate connections that took place over several days were, in fact, connected and likely represented command-and-control (C2) beaconing activity that had been taking place on the customer networks.

By analyzing the large number of external connections taking place on a customer’s network at any one time, AI Analyst is able to view seemingly isolated events as components of a wider incident, ensuring that customers maintain full visibility over their environments and any emerging malicious activity.

Figure 7: Cyber AI Analyst investigation detailing the SSL connectivity observed, including endpoint details and overall summary of the beaconing activity.

Conclusion

While Balada Injector’s tendency to interchange C2 infrastructure and utilize newly registered domains may have been able to bypass signature-based security measures, Darktrace’s anomaly-based approach enabled it to swiftly identify affected devices across multiple customer environments, without needing to update or retrain its models to keep pace with the evolving iterations of WordPress vulnerabilities.

Unlike traditional measures, Darktrace DETECT’s Self-Learning AI focusses on behavioral analysis, crucial for identifying emerging threats like those exploiting commonly used platforms such as WordPress. Rather than relying on historical threat intelligence or static indicators of compromise (IoC) lists, Darktrace identifies the subtle deviations in device behavior, such as unusual connections to newly registered domains, that are indicative of network compromise.

Darktrace’s suite of products, including DETECT+RESPOND, is uniquely positioned to proactively identify and contain network compromises from the onset, offering vital protection against disruptive cyber-attacks.

Credit to: Justin Torres, Cyber Analyst, Nahisha Nobregas, Senior Cyber Analyst

Appendices

Darktrace DETECT Model Coverage

  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device
  • Anomalous Connection / Anomalous SSL without SNI to New External
  • Anomalous Connection / Rare External SSL Self-Signed
  • Compliance / Possible DNS Over HTTPS/TLS
  • Compliance / External Windows Communications
  • Compromise / Repeating Connections Over 4 Days
  • Compromise / Beaconing Activity To External Rare
  • Compromise / SSL Beaconing to Rare Destination
  • Compromise / HTTP Beaconing to Rare Destination
  • Compromise / Suspicious TLS Beaconing To Rare External
  • Compromise / Large DNS Volume for Suspicious Domain
  • Anomalous Server Activity / Outgoing from Server
  • Anomalous Server Activity / Rare External from Server
  • Device / Suspicious Domain

List of IoCs

IoC - Type - Description + Confidence

collect[.]getmygateway[.]com - Hostname - Balada C2 Endpoint

cdn[.]dataofpages[.]com - Hostname - Balada C2 Endpoint

stay[.]decentralappps[.]com - Hostname - Balada C2 Endpoint

get[.]promsmotion[.]com - Hostname - Balada C2 Endpoint

js[.]statisticscripts[.]com - Hostname - Balada C2 Endpoint

sleep[.]stratosbody[.]com - Hostname - Balada C2 Endpoint

trend[.]stablelightway[.]com - Hostname - Balada C2 Endpoint

cdn[.]specialtaskevents[.]com - Hostname - Balada C2 Endpoint

88.151.192[.]254 - IP Address - Balada C2 Endpoint

185.39.206[.]160 - IP Address - Balada C2 Endpoint

111.90.141[.]193 - IP Address - Balada C2 Endpoint

185.39.206[.]161 - IP Address - Balada C2 Endpoint

2.59.222[.]121 - IP Address - Balada C2 Endpoint

80.66.79[.]253 - IP Address - Balada C2 Endpoint

Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:68.0) - User Agent - Observed User Agent in Balada C2 Connections

Gecko/20100101 Firefox/68.0 - User Agent - Observed User Agent in Balada C2 Connections

Mozilla/5.0 (Windows NT 10.0; Win64; x64) - User Agent - Observed User Agent in Balada C2 Connections

AppleWebKit/537.36 (KHTML, like Gecko) - User Agent - Observed User Agent in Balada C2 Connections

Chrome/117.0.0.0 - User Agent - Observed User Agent in Balada C2 Connections

Safari/537.36 - User Agent - Observed User Agent in Balada C2 Connections

Edge/117.0.2045.36 - User Agent - Observed User Agent in Balada C2 Connections

MITRE ATT&CK Mapping

Technique - Tactic - ID - Sub Technique

Exploit Public-Facing Application

INITIAL ACCESS

T1190

Web Protocols

COMMAND AND CONTROL

T1071.001

T1071

Protocol Tunneling

COMMAND AND CONTROL

T1572


Default Accounts

DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS

T1078.001

T1078

Domain Accounts

DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS

T1078.002

T1078

External Remote Services

PERSISTENCE, INITIAL ACCESS

T1133

NA

Local Accounts

DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS

T1078.003

T1078

Application Layer Protocol

COMMAND AND CONTROL

T1071

NA

Browser Extensions

PERSISTENCE

T1176

NA

Encrypted Channel

COMMAND AND CONTROL

T1573

Fallback Channels

COMMAND AND CONTROL

T1008

Multi-Stage Channels

COMMAND AND CONTROL

T1104

Non-Standard Port

COMMAND AND CONTROL

T1571

Supply Chain Compromise

INITIAL ACCESS ICS

T0862

Commonly Used Port

COMMAND AND CONTROL ICS

T0885

References

[1] https://blog.sucuri.net/2023/04/balada-injector-synopsis-of-a-massive-ongoing-wordpress-malware-campaign.html

[2] https://blog.sucuri.net/2023/10/balada-injector-targets-unpatched-tagdiv-plugin-newspaper-theme-wordpress-admins.html

[3] https://securityboulevard.com/2021/05/wordpress-websites-redirecting-to-outlook-phishing-pages-travelinskydream-ga-track-lowerskyactive/

[4] https://thehackernews.com/2023/10/over-17000-wordpress-sites-compromised.html

[5] https://www.bleepingcomputer.com/news/security/over-17-000-wordpress-sites-hacked-in-balada-injector-attacks-last-month/

[6]https://nvd.nist.gov/vuln/detail/CVE-2023-3169

[7] https://www.geoedge.com/balda-injectors-2-0-evading-detection-gaining-persistence/

[8] https[:]//github[.]com/yifeikong/curl_cffi/blob/master/README.md

[9] https://www.virustotal.com/gui/domain/cdn.dataofpages.com

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 Torres
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 21, 2025

Xillen Stealer Updates to Version 5 to Evade AI Detection

xillen stealer updates to version 5 to evade ai detectionDefault blog imageDefault blog image

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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