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April 20, 2022

Email Compromise To Mass Phishing Campaign

Read Darktrace's in-depth analysis on the shift from business email compromise to mass phishing campaigns. Gain the knowledge to safeguard your business.
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
Shuh Chin Goh
Written by
Sam Lister
Specialist Security Researcher
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20
Apr 2022

It is common for attackers to send large volumes of malicious emails from the email accounts which they compromise. Before carrying out this mass-mailing activity, there are predictable, preparatory steps which attackers take, such as registering mass-mailing applications and creating new inbox rules. In this blog, we will provide details of an attack observed in February 2022 in which a threat actor conducted a successful mass-mailing attack at a financial company based in Africa.

Attack summary

In February 2022, an attacker attempted to infiltrate the email environment of a financial services company based in Africa. At the beginning of February, the attacker likely gained a foothold in the company’s email environment by tricking an internal user into entering the credentials of their corporate email account into a phishing page. Over the following week, the attacker used the compromised account credentials to conduct a variety of activities, such as registering a mass-mailing application and creating a new inbox rule.

After taking these preparatory steps, the attacker went on to send out large volumes of phishing emails from the internal user’s email account. The attacker consequently obtained the credentials of several further internal corporate accounts. They used the credentials of one of these accounts to carry out similar preparatory steps (registering a mass-mailing application and creating a new inbox rule). After taking these steps, the attacker again sent large volumes of phishing emails from the account. At this point, the customer requested assistance from Darktrace’s SOC to aid investigation, and the intrusion was consequently contained by the company.

Since the attacker carried out their activities using a VPN and an Amazon cloud service, the endpoints from which the activities took place did not serve as particularly helpful indicators of an attack. However, prior to sending out phishing emails from internal users’ accounts, the attacker did carry out other predictable, preparatory activities. One of the main goals of this blog is to highlight that these behaviors serve as valuable signs of preparation for mass-mailing activity.

Attack timeline

Figure 1: Timeline of the intrusion

On February 3, the attacker sent a phishing email to the corporate account of an employee. The email was sent from the corporate account of an employee at a company with business ties to the victim enterprise. It is likely that the attacker had compromised this account prior to sending the phishing email from it. The phishing email in question claimed to be an overdue payment reminder. Within the email, there was a link hidden behind the display text “view invoice”. The hostname of the phishing link’s URL was a subdomain of questionpro[.]eu — an online survey platform. The page referred to by the URL was a fake Microsoft Outlook login page.

Figure 2: Destination of phishing link within the email sent by the attacker

Antigena Email, Darktrace’s email security solution, identified the highly unusual linguistic structure of the email, given its understanding of ‘normal’ for that sender. This was reflected in an inducement shift score of 100. However, in this case, the original URL of the phishing link was rewritten by Mimecast’s URL protection service in a way which made the full URL impossible to extract. Consequently, Antigena Email did not know what the original URL of the link was. Since the link was rewritten by Mimecast’s URL protection service, the email’s recipient will have received a warning notification in their browser upon clicking the link. It seems that the recipient ignored the warning, and consequently divulged their email account credentials to the attacker.

For Antigena Email to hold an email from a user’s mailbox, it must judge with high confidence that the email is malicious. In cases where the email contains no suspicious attachments or links, it is difficult for Antigena Email to obtain such high degrees of confidence, unless the email displays clear payload-independent malicious indicators, such as indicators of spoofing or indicators of extortion. In this case, the email, as seen by Antigena Email, didn’t contain any suspicious links or attachments (since Mimecast had rewritten the suspicious link) and the email didn’t contain any indicators of spoofing or extortion.

Figure 3: The email’s high inducement shift score highlights that the email’s linguistic content and structure were unusual for the email’s sender

Shortly after receiving the email, the internal user’s corporate device was observed making SSL connections to the questionpro[.]eu phishing endpoint. It is likely that the user divulged their email account credentials during these connections.

Figure 4: The above screenshot — obtained from Advanced Search — depicts the connections made by the account owner’s device on February 3

Between February 3 and February 7, the attacker logged into the user’s email account several times. Since these logins were carried out using a common VPN service, they were not identified as particularly unusual by Darktrace. However, during their login sessions, the attacker exhibited behavior which was highly unusual for the email account’s owner. The attacker was observed creating an inbox rule called “ _ ” on the user’s email account,[1] as well as registering and granting permissions to a mass-mailing application called Newsletter Software SuperMailer. These steps were taken by the attacker in preparation for their subsequent mass-mailing activity.

On February 7, the attacker sent out phishing emails from the user’s account. The emails were sent to hundreds of internal and external mailboxes. The email claimed to be an overdue payment reminder and it contained a questionpro[.]eu link hidden behind the display text “view invoice”. It is likely that the inbox rule created by the attacker caused all responses to this phishing email to be deleted. Attackers regularly create inbox rules on the email accounts which they compromise to ensure that responses to the malicious emails which they distribute are hidden from the accounts’ owners.[2]

Since Antigena Email does not have visibility of internal-to-internal emails, the phishing email was delivered fully weaponized to hundreds of internal mailboxes. On February 7, after the phishing email was sent from the compromised internal account, more than twenty internal devices were observed making SSL connections to the relevant questionpro[.]eu endpoint, indicating that many internal users had clicked the phishing link and possibly revealed their account credentials to the attacker.

Figure 5: The above screenshot — obtained from Advanced Search — depicts the large volume of connections made by internal devices to the phishing endpoint

Over the next five days, the attacker was observed logging into the corporate email accounts of at least six internal users. These logins were carried out from the same VPN endpoints as the attacker’s original logins. On February 11, the attacker was observed creating an inbox rule named “ , ” on one of these accounts. Shortly after, the attacker went on to register and grant permissions to the same mass-mailing application, Newsletter Software SuperMailer. As with the other account, these steps were taken by the attacker in preparation for subsequent mass-mailing activity.

Figure 6: The above screenshot — obtained from Advanced Search — outlines all of the actions involving the mass-mailing application that were taken by the attacker (accounts have been redacted)

On February 11, shortly after 08:30 (UTC), the attacker widely distributed a phishing email from this second user’s account. The phishing email was distributed to hundreds of internal and external mailboxes. Unlike the other phishing emails used by the attacker, this one claimed to be a purchase order notification, and it contained an HTML file named PurchaseOrder.html. Within this file, there was a link to a suspicious page on the public relations (PR) news site, everything-pr[.]com. After the phishing email was sent from the compromised internal account, more than twenty internal devices were observed making SSL connections to the relevant everything-pr[.]com endpoint, indicating that many internal users had opened the malicious attachment.

Figure 7: The above screenshot — obtained from Advanced Search — depicts the connections made by internal devices to the endpoint referenced in the malicious attachment

On February 11, the customer submitted an Ask the Expert (ATE) request to Darktrace’s SOC team. The guidance provided by the SOC helped the security team to contain the intrusion. The attacker managed to maintain a presence within the organization’s email environment for eight days. During these eight days, the attacker sent out large volumes of phishing emails from two corporate accounts. Before sending out these phishing emails, the attacker carried out predictable, preparatory actions. These actions included registering a mass-mailing application with Azure AD and creating an inbox rule.

Darktrace guidance

There are many learning points for this particular intrusion. First, it is important to be mindful of signs of preparation for malicious mass-mailing activity. After an attacker compromises an email account, there are several actions which they will likely perform before they send out large volumes of malicious emails. For example, they may create an inbox rule on the account, and they may register a mass-mailing application with Azure AD. The Darktrace models SaaS / Compliance / New Email Rule and SaaS / Admin / OAuth Permission Grant are designed to pick up on these behaviors.

Second, in cases where an attacker succeeds in sending out phishing emails from an internal, corporate account, it is advised that customers make use of Darktrace’s Advanced Search to identify users that may have divulged account credentials to the attacker. The phishing email sent from the compromised account will likely contain a suspicious link. Once the hostname of the link has been identified, it is possible to ask Advanced Search to display all HTTP or SSL connections to the host in question. If the hostname is www.example.com, you can get Advanced Search to display all SSL connections to the host by using the Advanced Search query, @fields.server_name:"www.example.com", and you can get Advanced Search to display all HTTP connections to the host by using the query, @fields.host:"www.example.com".

Third, it is advised that customers make use of Darktrace’s ‘watched domains’ feature[3] in cases where an attacker succeeds in sending out malicious emails from the accounts they compromise. If a hostname is added to the watched domains list, then a model named Compromise / Watched Domain will breach whenever an internal device is observed connecting to it. If Antigena Network is configured, then observed attempts to connect to the relevant host will be blocked if the hostname is added to the watched domains list with the ‘flag for Antigena’ toggle switched on. If an attacker succeeds in sending out a malicious email from an internal, corporate account, it is advised that customers add hostnames of phishing links within the email to the watched domains list and enable the Antigena flag. Doing so will cause Darktrace to identify and thwart any attempts to connect to the relevant phishing endpoints.

Figure 8: The above screenshot — obtained from the Model Editor — shows that Antigena Network prevented ten internal devices from connecting to phishing endpoints after the relevant phishing hostnames were added to the watched domains list on February 11

For Darktrace customers who want to find out more about phishing detection, refer here for an exclusive supplement to this blog.

MITRE ATT&CK techniques observed

Thanks to Paul Jennings for his contributions.

Footnotes

1. https://docs.microsoft.com/en-us/powershell/module/exchange/new-inboxrule?view=exchange-ps

2. https://www.fireeye.com/current-threats/threat-intelligence-reports/rpt-fin4.html

3. https://customerportal.darktrace.com/product-guides/main/watched-domains

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
Shuh Chin Goh
Written by
Sam Lister
Specialist Security Researcher

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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

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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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