Prevent Brand Abuse with Darktrace | Protect Your Brand
Prevent brand abuse with Darktrace's AI-powered solution. Detect and stop impersonation attacks before they harm your reputation. Read to learn more here.
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
Elliot Stocker
Product SME
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Nov 2022
Brand abuse refers to the unauthorized imitation of an organization's brand. Its discovery is often a reminder to organizations that they need to protect more than just their data and IP – their reputation is at stake. But brand impersonation can also be used to launch a direct attack against the organization – and those around it.
During a first demonstration meeting recently, Darktrace PREVENT discovered a website deploying a classic trick: the letters ‘rn’ were used in sequence in an attempt to imitate the letter ‘m’ in the company’s name (e.g. “exarnple-brand.com”). Whilst obvious when you’re looking out for it, for an unsuspecting employee this goes easily unnoticed.
This website was set up by an attacker two weeks before the PREVENT demo. The website was taken down immediately, and the company was also advised to launch an internal investigation to find out if somebody had received an email from this address. The company also launched an information campaign informing their supply chain of this attack, and this last activity resulted in the discovery that one of their suppliers had been scammed through the same email domain and had transferred a large sum of money towards a shell company that was not related to the main brand. By alerting that supplier, additional money transfers were prevented.
This example is part of a broader trend being seen across the industry. ZDNET’s Fraud Trends Report found that roughly 250,000 attacks in Q2 of 2021 involved some form of brand abuse. These attacks harm companies by inflicting reputational damage, incurring financial losses from fraudulent competition, or serving as steppingstones for larger threats like supply chain attacks.
Organizations work hard to cultivate brand identities that differentiate themselves from competitors and build relationships with consumers. Yet, the stronger and more recognizable a brand is, the more often it is targeted for abuse as malicious actors take advantage of their success to reach more victims. Companies with greater online presences or international operations across multiple channels are also at higher risk.
Brand abuse takes many forms. It can be a website designed to look like it belongs to the brand to collect personal information such as email addresses and passwords. It can be an invoice sent by a vendor with a slight typo in its name. It can be an unauthorized branded webshop that never ships products to buyers. It can be a fake social media account directing customers to malicious websites that distribute malware or spreading fake news. It can be as simple as copyright or trademark infringement.
Figure 1: The general pattern malicious actors use for brand abuse.
Responding to Brand Abuse
Reconquering brand reputation after a brand abuse incident can prove to be much more difficult and costly than investing beforehand to help secure the brand. Risk detection and monitoring require a holistic approach to cover the diverse forms of brand abuse, and requires patrolling the internet for copycats, typo squatters, and other malicious appropriations.
Figure 2: Mapping to the stages of brand abusein Figure 1, the security team has a set of signals to look for and actions totake to stop brand abuse before it is too late.
Protecting the brand identity and external attack surface can seem like a daunting task for security teams, especially in an age where monitoring internal systems proves enough of a challenge itself. Moreover, how often should the team perform this brand abuse monitoring? Companies can try to search every six months, every quarter, even every month, however there would still be gaps between when a threat actor launches an attack and when the security team discovers it. This is when AI becomes a tremendous ally, as it works at a speed and scale that human teams cannot.
The Power of PREVENT
PREVENT/Attack Surface ManagementTM works autonomously and continuously to uncover instances of brand abuse, and proactively hardens defenses against any attack that might be launched as a result.
It uses AI to distinguish a company’s external assets from the rest of the global internet. Its processing features learn brand-related assets such as logos and domain names. It also leverages natural language processing and image classification algorithms to tackle even the most ambiguous and error-prone assets encountered to identify and stop copycats and typosquatters.
PREVENT/ASM carries out this comprehensive level of monitoring continuously, closing the gap between when an attacker spins up malicious infrastructure and when the security team identifies it. With PREVENT, should an attacker create a malicious website tomorrow morning, the security team will be alerted tomorrow morning.
In addition to identifying brand abuse, PREVENT/ASM helps the team to collect all the relevant data needed to support a Notice and Takedown procedure. It also integrates with the rest of Darktrace’s security ecosystem to ensure that cyber defense is hardened ahead of time, should malicious assets discovered by PREVENT/ASM be used to launch an attack.
For example, identifying a webpage impersonating a brand is useful data for email security. PREVENT forewarns Darktrace/Email of malicious domains, which in turn heightens its sensitivity against emails sent from this site. The same is true with regards to network traffic as well as endpoint security: an endpoint device visiting this host will have Darktrace DETECTTM + Darktrace RESPONDTM on higher alert – ready to immediately neutralize threatening activity when it occurs.
This is the power of the Cyber AI Loop, a virtuous feedback cycle in which AI engines continuously feed into and strengthen one another.
And PREVENT not only identifies instances of brand abuse (along with Shadow IT, misconfigurations, supply chain risk, and other vulnerabilities), but it also prioritizes these risks according to exposure and potential damage and impact. With PREVENT/End-to-EndTM using Darktrace’s understanding of every device and connection inside an organization – every user and their interactions, every possible attack path – insights from the internal and external attack surface combine to give security teams a fully informed understanding of how they can spend their time most effectively to reduce cyber risk.
In these ways, PREVENT not only monitors for brand abuse at a scope and scale far beyond the capabilities of human security teams, but it also integrates with DETECT + RESPOND to harden a company’s cyber security.
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.
Managing OT Remote Access with Zero Trust Control & 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.
Product Marketing Manager, OT Security & Compliance
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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
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:
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
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:
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
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
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
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)
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