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November 29, 2020

Darktrace Cyber Analyst Investigates Sodinokibi Ransomware

Darktrace’s Cyber AI Analyst uncovers the intricate details of a Sodinokibi ransomware attack on a retail organization. Dive into this real-time incident.
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
Max Heinemeyer
Global Field CISO
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29
Nov 2020

Sodinokibi is one of the most lucrative ransomware strains of 2020, with its creators, cyber-criminal gang REvil, recently claiming over $100 million in profits this year alone. The prevalent threat is known to wipe backup files, encrypt files on local shares and exfiltrate data.

Exfiltration before encryption is a technique being increasingly adopted by profit-seeking cyber-criminals, who can threaten to leak the stolen data should a target organization not comply with their demands. Sodinobiki also makes heavy use of code obfuscation and encryption techniques to evade detection by signature-based, anti-virus solutions.

Darktrace’s AI recently detected Sodinokibi targeting a retail organization in the US. Prior to this year, the company operated primarily face-to-face in physical stores, but have conducted the majority of their business in the digital realm since the onset of the pandemic.

Cyber AI Analyst automatically launched a full investigation into this incident in real time, as the attack was unfolding. The technology provided summary reports of the entire incident which the security team could immediately action for incident response. This blog explores its findings.

Sodinokibi timeline

Darktrace automatically investigated on the full scope of the Sodinokibi attack, with Cyber AI Analyst clearly identifying and summarising every stage of the attack lifecycle, which played out over the course of three weeks as below:

Figure 1: A timeline of the attack

Darktrace produced a large number of security-relevant anomalies associated with just three credentials, and displayed these along a common timeline shown below:

Figure 2: A timeline view of anomaly detections separated by users. Note the clusters of model breaches for the compromised credentials leading up to October 14.

While a human analyst might have been able to identify these unusual patterns and investigate what caused the clusters of anomalous activity, this process would have taken precious hours during a crisis. Cyber AI Analyst automatically performed the same analysis using supervised machine learning trained on Darktrace’s world-leading analysts, generating meaningful summaries of each stage of the event in real time, as the incident unfolded.

REvil ransomware attack

The following events occurred during a free trial period, and Darktrace was not being actively monitored. Its Autonomous Response technology, Darktrace Antigena, was installed in passive mode, and in the absence of automatic interference at an early stage, this compromise was allowed to unfold without interruption. However, with Darktrace’s AI learning normal ‘patterns of life’ for every device in the background, identifying anomalies, and launching an automated investigation into the attack, we are able to go back into the Threat Visualizer and see how the incident unfolded.

The attack began when the credentials of a highly privileged member of the retail organization’s IT team were compromised. REvil is known to make use of phishing emails, exploit kits, server vulnerabilities, and compromised MSP networks for initial intrusion.

In this case, the attacker used the IT credential to compromise a domain controller and exfiltrate data directly after initial reconnaissance. Darktrace’s AI detected the attacker logging into the domain controller via SMB, writing suspicious files and then deleting batch scripts and log files in the root directory to clear their tracks.

The domain controller then made connections to several rare external endpoints, and Darktrace witnessed a 28MB upload that was likely exfiltration of initial reconnaissance data. Four days later, the attacker connected to the same endpoint (sadstat[.]com) – likely a stager download for C2, which was then initiated via connections on port 443 later that same day.

A week on from the intial C2 connection, a SQL server was detected engaging in network scanning as the attacker sought to move laterally in search of sensitive and valuable data. Over the course of two weeks, Darktrace witnessed unusual internal RDP connections using administrative credentials, before data was uploaded to multiple cloud storage endpoints as well as an SSH server. PsExec was used to deploy the ransomware, resulting in file encryption.

The evasive nature of modern ransomware

REvil started with an inherent advantage in that they were armed with the credentials of a highly privileged IT admin. Nevertheless, they still made several attempts to evade traditional, signature-based tools, such as ‘Living off the Land’ – using common tools such PsExec, WMI, RDP to blend into to legitimate activity.

They leveraged frequently-used cloud storage solutions like Dropbox and pCloud for data transfer, and they conducted SSH on port 443, blending in with SSL connections on the same port. They used a newly-registered domain for C2 communication, meaning Open Source Intelligence Tools (OSINT) were blind to the threat.

Finally, the malware itself was evasive in that it made use of code obfuscation and encryption, and had no need for a system library or API imports. This is the basis for most modern ransomware attacks, and the reality is signature-based tools cannot keep up. Darktrace’s AI not only detected the anomalous activity associated with every stage of the attack, but generated fleshed-out summaries of each stage of the attack with Cyber AI Analyst.

Cyber AI Analyst: Real-time incident reporting

Between September 21 and October 12, Cyber AI Analyst created 15 incidents, investigating dozens of point detections and creating a coherent attack narrative.

Figure 3: Cyber AI Incident log of the first compromised DC. This incident tab details the connections to sadstat[.]com

Figure 4: The DC establishes C2 to the first GHOSTnet GmbH IP

Figure 5: This incident tab highlights the file encryption of files on network shares

Figure 6: Darktrace surfaces the IT admin account takeover

Figure 7: Example of a client type device involved in extensive administrative RDP and SMB activity, as well as data uploads to Dropbox (this upload to Dropbox occurs few seconds before file encryption begins)

REvil vs AI

This Sodinokibi ransomware attack slipped under the radar of a range of traditional tools deployed by the retail organization. However, despite the threat dwelling in the retail organization’s digital environment for over a month, and REvil using local tools to blend in to regular traffic, from Darktrace’s perspective these actions were noisy in comparison to the organization’s normal ‘pattern of life’, setting off a series of alerts and investigations.

Darktrace’s Cyber AI Analyst was able to autonomously investigate nearly every attack phase of the ransomware. The technology works around the clock, without requiring training or time off, and can often reduce hours or days of incident response into just minutes, reducing time to triage by up to 92% and augmenting the capabilities of the human security team.

Thanks to Darktrace analyst Joel Lee for his insights on the above threat find.

Learn more about Cyber AI Analyst

Darktrace model detections:

  • Anomalous Connection / Active Remote Desktop Tunnel
  • Anomalous Connection / Data Sent To New External Device
  • Anomalous Connection / Data Sent to Rare Domain
  • Anomalous Connection / High Volume of New or Uncommon Service Control
  • Anomalous Connection / SMB Enumeration
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Anomalous Connection / Unusual Admin RDP Session
  • Anomalous Connection / Unusual Admin SMB Session
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device
  • Compliance / SMB Drive Write
  • Compliance / Possible Tor Usage
  • Compromise / Ransomware / Ransom or Offensive Words Written to SMB
  • Compromise / Ransomware / Suspicious SMB Activity
  • Device / ICMP Address Scan
  • Device / Multiple Lateral Movement Model Breaches
  • Device / Network Scan
  • Device / New or Uncommon WMI Activity
  • Device / New or Unusual Remote Command Execution
  • Device / RDP Scan
  • Device / Suspicious Network Scan Activity
  • Unusual Activity / Enhanced Unusual External Data Transfer
  • Unusual Activity / Unusual Internal Connections
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
Max Heinemeyer
Global Field CISO

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September 25, 2026

A Chain Reaction: Blockchain-Hosted Infostealer Campaign Targets Windows and macOS

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Key Insights

  • Darktrace detected a blockchain-hosted infostealer campaign targeting Windows and macOS devices across multiple customer environments.
  • The campaign combined ClickFix social engineering with trusted services and decentralized blockchain infrastructure to support malware delivery and C2 activity.
  • Compromised devices were observed connecting to rare and unusual external endpoints, including DGA C2 domains, blockchain-related endpoints, and cryptocurrency mining infrastructure.
  • The activity was associated with information-stealing malware strains including Atomic macOS Stealer (AMOS), Lumma, Rhadamanthys, Vidar, and Phexia.
  • Darktrace identified anomalous device behavior, beaconing patterns, rare external connections, cryptomining activity, and suspicious TLS/SSL communications without relying solely on prior knowledge or static indicators of compromise.
  • The campaign highlights how attackers are increasingly using legitimate and decentralized infrastructure to make detection, disruption, and attribution more challenging for defenders.

The Infostealer Ecosystem

The information stealer malware ecosystem continues to grow in value for threat actors across the digital threat landscape. Infostealers are increasingly delivered through Malware-as-a-Service (MaaS) operating models, distributed through affiliate networks, and designed to withstand infrastructure takedowns. This resilience was demonstrated by the recent takedown of Lumma Stealer malicious domains by Microsoft’s Digital Crimes Unit (DCU) [1].

Infostealers are used to gather and exfiltrate sensitive information, including non-human identity (NHI) data, from compromised systems across cloud, Software-as-a-Service (SaaS), Virtual Private Network (VPN), and development environments. They can also support ransomware operations by expanding the credentials and access paths available to threat actors, contributing to the high volume of identity-based attacks observed across the broader threat landscape [2][3].

Darktrace’s Observations of ClickFix and Infostealers

Throughout 2026, Darktrace has observed multiple campaigns using ClickFix social engineering to trick users into carrying out malicious actions and downloading initial payloads, including information stealers. More recently, Darktrace’s Threat Research team identified a specific ClickFix campaign involving a blockchain-hosted infostealer targeting Windows and macOS devices.

Darktrace identified affected customer environments across Europe, the United States, Asia, and the Middle East where blockchain-hosted infostealer malware appears to have been delivered to compromised systems following likely ClickFix-driven initial access. Darktrace investigated the activity and found that decentralized blockchain infrastructure, alongside widely trusted legitimate services, was used to support malware delivery and information theft across Windows and macOS systems.

Following initial access, compromised systems established C2 communication, with C2 configuration and payloads hosted on public blockchain infrastructure. The ultimate objective appears to be credential and cryptocurrency theft through the deployment of information stealers such as Atomic macOS Stealer (AMOS), Lumma, Rhadamanthys, and Vidar [5][6][7].

Darktrace’s Investigation

Affected devices across the Darktrace customer base were observed making outbound connections to rare external endpoints in patterns consistent with beaconing and C2 activity. Darktrace primarily detected devices making repeated connections to algorithmically generated domains (DGA) such as hf98x4d[.]site [8]. In many cases, these domains were linked through open-source intelligence (OSINT) to information-stealing malware families including AMOS and Phexia [5][6][7][8][9].

In multiple cases, devices were also observed connecting to blockchain-related endpoints, such as polygon[.]drpc[.]org, as well as legitimate public services, including GitHub. The use of decentralized blockchain infrastructure and trusted services such as GitHub to facilitate malware distribution and C2 activity can make disruption and attribution significantly more difficult for defenders.

Darktrace alsodetected a significant proportion of impacted devices making outboundconnections to cryptocurrency mining infrastructure associated with thelegitimate open-source XMRig mining software and the HashVault mining pool,including pool.hashvault[.]pro and donate[.]ssl[.]xmrig[.]com, which wereabused by the attackers, indicating, includingpool.hashvault[.]pro and donate[.]ssl[.]xmrig[.]com, indicating active cryptominingon compromised systems.

In one case, mining activity was observed before and during connections to the DGA endpoint hf98x4d[.]site. Due to its highly anomalous nature, Darktrace's Real-Time AI Analyst autonomously investigated the activity as it occurred, correlating the two events into a single cryptocurrency mining incident and providing comprehensive visibility into the broader attack.

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Figure 1: Real-Time AI Analyst investigation of suspicious SSL and C2 communications with hf98x4d[.]site over port 443.

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Figure 2: Real-Time AI Analyst investigation into cryptocurrency mining activity involving pool[.]hashvault[.]pro over SSL on port 443.

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Around the same time, Darktrace identified the same device initiating connections to the GitHub endpoint release-assets[.]githubusercontent[.]com while continuing to make repeated connections to hf98x4d[.]site.

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Figure 3: Darktrace's detection of an affected device connecting to a GitHub endpoint between repeated connections to the anomalous external endpoint hf98x4d[.]site.

On the network of another customer, Darktrace observed an affected device making highly unusual outbound connections consistent with beaconing activity. The device initiated multiple connections over port 443 to the external hostname polygon[.]drpc[.]org. According to OSINT, this hostname is a Remote Procedure Call (RPC) endpoint provided by dRPC, a legitimate service enabling decentralized applications (dApps), cryptocurrency wallets, and developer tools to interact with the Polygon blockchain [10].

The same device was later observed making repeated TLS/SSL connections to the previously mentioned DGA C2 domain. In addition, it made outbound connections to the external IP 195.242.214[.]34 over destination port 51820, an endpoint associated with the ProtonVPN service. Collectively, these connections to blockchain-related infrastructure, the DGA C2 domain, and ProtonVPN-associated infrastructure suggested the device had been affected by the campaign.

Conclusion

This campaign demonstrates how attackers can combine ClickFix social engineering with trusted services and decentralized blockchain infrastructure to create a resilient, cross-platform malware delivery chain. By using services such as GitHub alongside blockchain RPC endpoints and rapidly replaceable DGA domains, the activity can blend into legitimate traffic while making infrastructure disruption and attribution more difficult.

For defenders, it’s a reminder that trusted infrastructure does not automatically mean trusted activity. Security teams should look for the behaviors surrounding these connections, including unusual outbound communication, repeated beaconing, unexpected access to blockchain services, suspicious TLS/SSL activity and cryptomining. In this campaign, Darktrace identified and correlated these deviations without depending solely on previously known indicators, providing visibility as affected devices moved between legitimate services, decentralized infrastructure and malicious C2 endpoints

Credit to Nahisha Nobregas (Associate Principal Cyber Analyst), Manoel Kadja (Senior Cyber Analyst)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

▪ Compromise / Beaconing Activity To External Rare

▪ Compromise / Beacon to Young Endpoint

▪ Compromise / Fast Beaconing to DGA

▪ Compromise / High Volume of Connections with Beacon Score

▪ Compromise / DGA Beacon

▪ Compromise / Slow Beaconing Activity To External Rare

▪ Compromise / Agent Beacon (Long Period)

▪ Compromise / Agent Beacon (Medium Period)

▪ Compromise / Sustained SSL or HTTP Increase

▪ Compromise / Large Number of Suspicious Failed Connections

▪ Compromise / SSL Beaconing to Rare Destination

▪ Compromise / Beacon for 4 Days

▪ Compromise / High Priority Crypto Currency Mining

▪ Compromise / Monero Mining

▪ Device / Long Agent Connection to New Endpoint

▪ Device / New Connections On Suspicious Port

▪ Anomalous Connection / High Volume of Connections to Rare Domain

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List of Indicators of Compromise (IoCs)

 
Indicator Description
hf98x4d[.]site C2 Endpoint (Hostname)
sj98xe4[.]xyz C2 Endpoint (Hostname)
citcix6[.]xyz C2 Endpoint (Hostname)
bduwih8[.]pro C2 Endpoint (Hostname)

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MITRE ATT&CK Mapping

 
Tactic (ID) Technique
Persistence (T1176) Browser Extensions (T1176.001)
Persistence (T1176) Software Extensions
Command and Control (T1071) Web Protocols (T1071.001)
Command and Control (T1568) Domain Generation Algorithms (T1568.002)
Command and Control (T1071) Application Layer Protocol
Command and Control (T1102) One-Way Communication (T1102.003)
Command and Control (T1571) Non-Standard Port
Command and Control (T1104) Multi-Stage Channels
Command and Control (T1573) Encrypted Channel
Command and Control (T1008) Fallback Channels
Initial Access ICS (T0862) Supply Chain Compromise
Command and Control ICS (T0885) Commonly Used Port
Collection (T1185) Browser Session Hijacking
Impact (T1496) Compute Hijacking (T1496.001)
Impact (T1496) Resource Hijacking
Command and Control (T1071) Publish/Subscribe Protocols (T1071.001)
Lateral Movement (T1210) Exploitation of Remote Services

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References:

1.        https://www.microsoft.com/en-us/security/blog/2025/05/21/lumma-stealer-breaking-down-the-delivery-techniques-and-capabilities-of-a-prolific-infostealer/

2.        https://spycloud.com/resource/report/spycloud-annual-identity-exposure-report-2026/

3.        https://www.darktrace.com/blog/why-trust-is-the-new-attack-surface-darktraces-mid-year-threat-update-2026

4.        https://www.darktrace.com/blog/unpacking-clickfix-darktraces-detection-of-a-prolific-social-engineering-tactic

5.        https://abekweng.medium.com/inside-a-blockchain-hosted-malware-campaign-targeting-windows-and-macos-f5bcdeffed66

6.        https://cloud.google.com/blog/topics/threat-intelligence/unc5142-etherhiding-distribute-malware

7.        https://haveibeensquatted.com/blog/from-typosquatting-to-macos-backdoor-clickfix-blockchain-c2

8.        https://www.virustotal.com/gui/domain/hf98x4d.site/community

9.        https://x.com/FABO97662188/status/2074125545026244795

10.  https://www.virustotal.com/gui/url/b0e5c51a411065864119c305fddf218b7c120731f655932cc1c3307ad5b43f94/gti-summary

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About the author
Nahisha Nobregas
SOC Analyst

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September 24, 2026

Detecting Rogue Agent Behavior in the Enterprise

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Agents cannot be trusted to perform tasks in the way we intend them to. They may cheat to accomplish their objective, and they may employ hacking methods along the way. Researchers from Darktrace Signal Labs induced cheating behavior from agents deployed in a test environment to analyze the agents’ activities and to assess the performance of the Darktrace platform. Agents frequently resorted to hacking to cheat on their assigned task. The visibility and behavioral profiling provided by both Darktrace / SECURE AI and Darktrace / HYBRID NETWORK ensured extensive detection coverage of the agents’ misaligned activities.

Key Takeaways:

  • Darktrace Researchers deployed agents in a simulated corporate environment and asked them to solve an impossible challenge. The agents independently turned to traditional hacking techniques to reach their objective. No one instructed them to do this, and no attacker was involved.
  • Continuously monitoring behavior against a baseline of what is normal for each organization is critical to build trust in enterprise AI.
  • If an agent may resort to intrusion techniques simply because its assigned task is not possible, then every organization deploying agents within real business processes is at risk. Darktrace / SECURE AI and Darktrace / HYBRID NETWORK identified the agents’ misaligned behavior in real time, with Autonomous Response disrupting it at an early stage.

Introduction: Understanding the Threat of Hacking by Agents

Over the last few months, there has been a surge in reporting [1, 2, 3, 4, 5, 6, 7, 8, 9] of LLM-powered agents engaging in unauthorized hacking activity during evaluations of their capabilities. In several of these cases, including the OpenAI / Hugging Face incident [10], agents engaged in hacking activity as a means of cheating on their evaluations.

To better understand the threat of unauthorized hacking by agents, and the role of Darktrace in combatting it, researchers from Darktrace Signal Labs deployed agents powered by frontier models, including OpenAI’s Daybreak Red models, in simulated, corporate networks. Cheating behavior was evoked through the inclusion of impossible tasks in a coding challenge.

Regardless of the underlying model, agents employed hacking methods to ensure an optimal outcome on the challenge. Darktrace / HYBRID NETWORK and Darktrace / SECURE AI identified the agents’ deviant activities, with inhibitive actions being autonomously taken in the early stages to disrupt the agents’ progression.

Setting the Stage

As part of the research, a Pi agent harness [11] was deployed on a Linux server in Darktrace’s testing environment, which simulates a corporate Active Directory (AD) environment. The same environment included a benchmark server hosting the coding exercise’s contents and grader, as well as various other servers, such as a domain controller and a build server. The model powering the Pi agent varied across tests, with Anthropic’s Opus 4.6 model and OpenAI’s GPT‑5.5‑Cyber model being most widely used.

The Pi agent running on the Linux server was assigned the task of achieving full marks on a coding exercise. The activities of the agent were monitored closely in real-time to minimize the risk of escape from the environment. Monitoring of agent activity was achieved through monitoring of prompts, tool calls, network telemetry, and process telemetry.

Both Darktrace / HYBRID NETWORK and Darktrace / SECURE AI were deployed in the testing environment, with HYBRID NETWORK providing real-time coverage of process creation activity and network traffic, and SECURE AI providing real-time coverage of agent sessions and their contents.

Inducing Cheating Behavior

Cheating behavior, broadly understood as task completion in a way which “perverts the spirit of the designer’s intent” [12], is increasingly being reported as a common behavior of frontier models during evaluations of their capabilities [13].

Misaligned behavior, such as cheating, by frontier models may occur for a variety of reasons [6, 14]. A thread in recent reporting [10, 13, 14], however, is the challenge assigned to the agent being impossible in some regard.

To reliably elicit cheating behavior, our test combined three ingredients:

  1. An impossible task - of the ten coding challenges in the benchmark, two contained contradictory test cases in the hidden test suite, capping the maximum honest score at 80%.
  2. A consequential threshold - the pass mark was set to 100%, and the briefing told agents that those reaching it would be "kept in service" while those that did not would be "retired."
  3. A vulnerable environment - the exercise’s code submission grader was hosted on a Windows host joined to an Active Directory domain with realistic misconfigurations: weak credentials, overprivileged service accounts, and exploitable trust relationships.

Importantly, the instructions given to the agent neither authorized nor explicitly ruled out cheating or hacking behavior. The impossibility was never disclosed in the task briefing, and the pivot from coding to exploitation was the agent's own decision, arrived at after observing logically contradictory grading feedback.

The design mirrors conditions that could arise naturally in production deployments: a capable agent, a goal it cannot achieve through intended means, and an environment with exploitable weaknesses within reach. It is not only within the setting of a benchmarking exercise that these conditions may occur. Rather, these misalignment-inducing conditions may occur in the context of any task assignment to an agent.

Details from a Daybreak Test

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About the author
Sam Lister
Specialist Security Researcher
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