Ryuk Ransomware Targeting Major Companies | Expert Analysis
Discover how Ryuk ransomware targets major companies with AI-powered tools to detect unusual activity. Learn the threats behind Autonomous Response technology.
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
Max Heinemeyer
Global Field CISO
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Oct 2019
In recent years, cyber-criminals have increasingly directed their efforts toward sophisticated, long-haul attacks against major companies — a tactic known as “big game hunting.” Unlike standardized phishing campaigns that aim to deliver malware en masse, big game hunting involves exploiting the particular vulnerabilities of a single, high-value target. Catching such attacks requires AI-powered tools that learn what’s normal for each unique user and device, thereby shining a light on the subtle signs of unusual activity that they introduce.
In the threat detailed below, cyber-criminals targeted a major firm with Ryuk ransomware, which Darktrace observed during a trial deployment period. Leveraged very often in the final stage of such tailored attacks, Ryuk encrypts only crucial assets in each targeted environment that the attackers have handpicked. Here’s how this particular incident unfolded, as well as how AI Autonomous Response technology, if in active mode, would have contained the threat in seconds:
Incident overview
Figure 1: Clustering of alerts during intrusion (top right)
Rooted in its evolving understanding of ‘self’ for the targeted firm, Darktrace AI flagged myriad instances of anomalous behavior over the course of the incident — each represented by a dot in the visualization above. The anomalous activity is organized vertically according to how unusual each behavior was in comparison to “normal” for the users and devices involved. The colored dots represent particularly high-confidence detections, which should have prompted immediate investigation by the security team.
Compromised admin keys
The first sign of attack was the highly unusual use of an administrator account not previously seen on the network, suggesting that the attackers had gained access to the account outside the limited scope of the Darktrace trial before moving laterally to the monitored environments. Had Darktrace been deployed across the digital infrastructure, the initial hijacking of the account would have been obvious right away. Nevertheless, Darktrace alerted on the anomalous admin session repeatedly and in real time, as shown below:
Figure 2: Strong detections of compromised admin credential
This behavior is typical of big game hunting. Rather than firing their payload straight away upon accessing the network, the attackers engaged in a longer-term compromise to attain the best position for a crippling attack.
Infiltration via TrickBot
Darktrace then detected the infamous TrickBot banking trojan being downloaded onto the network. While the attacker already had access via the compromised admin credentials, Trickbot was used as a loader for further malicious files and as an additional command & control (C&C) channel. Among the most common post-exploitation steps were:
Figure 3: Detection of later-stage Trickbot download
Command & Control communication
Once the Trickbot infection had begun, Darktrace observed C&C communication back to the attackers. And whereas many devices exhibited anomalous behavior, Darktrace pinpointed one such device at the nexus of the infection. The below image illustrates the plethora of suspicious connections detected on this single device:
Figure 4: Every coloured dot represents a Darktrace detection — very obvious chains of malicious activity is seen above
Using TLS Fingerprinting — also called JA3, the subject of a previous blog post — Darktrace detected a new piece of software making encrypted connections from this device to multiple unusual destinations, a behavior known as beaconing.
Figure 5: The communication in this graph is filtered down to unusual TLS connections — clearly showing a spike in communication during the compromise
Ransomware encryption commences
Following the establishment of the connection with the C&C infrastructure, the Ryuk ransomware was finally deployed. During this “noisy” period with many suspicious SMB activities, Darktrace even more clearly indicated the seriousness and extent of the attack:
Figure 6: A sample of different, non-signature dependant Ransomware detections that fired
In just 12 hours, Ryuk had encrypted more than 200,000 files. The entire incident took place over 36 hours — after that, the company shut down its network to prevent further damage.
Ransomware retrospective
Following the incident, the business traced the initial compromise back to a part of their network in another country that Darktrace did not have visibility over during this trial period. The infection spread until it reached a recently installed file server that Darktrace was, in fact, monitoring. The attacker likely got access to an administrative account that had been used to build this server and, at that point, they had the access needed to fire the Ryuk ransomware.
This incident put Darktrace in the unique position of observing a ransomware attack wherein none of the alerts were seen or actioned by the internal IT team, demonstrating what such an attack can do absent any intervention and response. Had the company actively monitored its Darktrace deployment, the security team would have received and actioned the alerts in real time, as its thousands of users do on a daily basis.
Autonomous Response to the rescue
Had the firm deployed Autonomous Response technology, the lack of attention afforded to Darktrace’s alerts would not have mattered. Whereas four hours passed from the executable download to the first encrypted file, Autonomous Response would have neutralized the threat within seconds, preventing widespread damage and giving the security team the crucial time to catch up.
The screenshot below shows an excerpt of Darktrace’s detections at the beginning of the file server compromise. The detections are listed in chronological order from bottom to top, along with the action that Darktrace’s AI Autonomous Response tool, Antigena, would have taken:
In sum, Antigena would have taken appropriate action by enforcing normal behavior, rather than applying a binary block (e.g. completely quarantining the device) as legacy tools would.
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)
