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June 24, 2020

Ekans Ransomware: Insights on OT Cyber Attacks

Uncover the impacts of the Ekans ransomware attack on operational technology and what organizations can do to enhance their cybersecurity posture.
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
David Masson
VP, Field CISO
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24
Jun 2020

In recent weeks, the security industry has become acutely aware of the challenges surrounding OT protection, with the EKANS ransomware attacks on Honda and the Enel Group demonstrating how novel threats continue to slip through the cracks of security systems in ICS environments. What’s more, with such attacks resulting in loss of productivity and damage to critical infrastructure, the need for a cyber security strategy that bridges both OT and IT technology is increasingly urgent.

The recent EKANS ransomware has been making waves in security circles because of its ability to target 64 specific ICS mechanisms in its ‘kill chain’. Standard attacks target ICS environments through vulnerabilities in IT infrastructure, pivoting through unpatched software to reach OT machinery, rather than heading straight for the jugular. The EKANS ransomware targeted ICS vulnerabilities directly and can be considered the first of its kind – marking a significant evolution in attacker techniques. Before now, ICS machinery-specific ransomware had either been an academic theory or a marketing tool.

Technical analysis

Written in the Go programming language, EKANS has additional obfuscation abilities compared to other ransomware strains, which enable it to better evade detection. As will be seen in this analysis, the power of EKANS ransomware is two-fold – it is able to disguise its attack in the beginning stages, and when it does strike, it is targeted at industrial pain points.

The ransomware’s first port of call is to check if the victim has already been encrypted. If not, standard encryption library functions ensue. These involve both the execution of encryption operations and the deletion of Volume Shadow Copy back-ups – meaning the victim cannot simply retrieve duplicated data copies and circumvent the ransom.

Before the relevant files are encrypted, EKANS ransomware kills various ICS processes listed in a pre-programmed, hard-coded list. The affected applications include GE’s Proficy data historian, GE Fanuc automation software, FLEXNet licensing server instance, Thingworx monitoring and management software, and Honeywell’s HMIWeb application – all specific to ICS environments.

proficyclient.exe
vmacthlp.exe
msdtssrvr.exe
sqlservr.exe
msmdsrv.exe
reportingservicesservice.exe
dsmcsvc.exe
winvnc4.exe
client.exe
collwrap.exe
bluestripecollector.exe

Figure 1: A small excerpt of the ICS-related processes targeted in the EKANS ‘kill list’

While stalling these processes doesn’t necessarily bring industrial plants crashing to a halt, it does reduce visibility and potentially make machine operations unpredictable. In the case of Honda’s attack, manufacturing operations across the US, the UK, and Turkey were suspended. With a workforce of 220,000 people worldwide, shutting down several factories and sending employees home results in a dramatic loss of production hours and employee salaries – not to mention the costs of getting systems up and running without giving in to ransom demands.

EKANS then goes one stage further. Once this initial kill chain has been executed, the ransomware starts encrypting data. Five randomly generated letters are added at the end of each original file extension. This in itself is unusual, as most ransomware encrypts data with a specific key.

Figure 2: Encryption results of EKANS ransomware

Rather than targeting specific devices or systems, EKANS ransomware looks to take down the entire network, which is part of what makes it such an aggressive style of ransomware. However, it lacks a self-propagating mechanism, so it has to be manually introduced to ICS environments. Malicious payloads hidden in links and attachments within emails are the primary mechanism used to introduce the ransomware. From there, EKANS exploits vulnerable and unpatched services, seeding itself across the entire business via script.

When the encryption process has been completed, a ransom note is displayed, requesting a covert financial exchange for a decryption key over the encrypted email platform CTemplar. In the case of both Honda and the Enel Group, they were told to contact CarrolBidell@tutanota[.]com for further information. The attackers also offered to send several decrypted files to prove the legitimacy of the encryption key.

| What happened to your files?
--------------------------------------------
We breached your corporate network and encrypted the data on your computers. The encrypted data includes documents, databases, photos and more –
all were encrypted using a military grade encryption algorithms (AES-256 and RSA-2048). You cannot access those files right now. But dont worry!
You can still get those files back and be up and running again in no time.
--------------------------------------------
| How to contact us to get your files back?
--------------------------------------------
The only way to restore your files is by purchasing a decryption tool loaded with a private key we created specifically for your network.
Once run on an effected computer, the tool will decrypt all encrypted files – and you resume day-to-day operations, preferably with
better cyber security in mind. If you are interested in purchasing the decryption tool contact us at %s
--------------------------------------------
| How can you be certain we have the decryption tool?
--------------------------------------------
In your mail to us attach up to 3 files (up to 3MB, no databases or spreadsheets).

Figure 3: Partial view of EKANS ransomware note

Honda has refrained from stating what specific plant capabilities were affected by the EKANS attack, however it has publicly affirmed that production operations have been affected in multiple factories across the world. Their visibility and control systems were disrupted significantly enough to suspend manufacturing.

Becoming immune to ransomware

While the EKANS ransomware leverages fairly crude techniques and is only able to halt processes rather than control ICS mechanisms, it represents a new frontier in OT cyber-attacks. ICS offensives will continue to evolve – with greater control over machinery a likely avenue of exploration for cyber-criminals.

What is clear from the Honda attack is that even some of the world’s largest global conglomerates are susceptible to these kind of ransomware attacks. What is needed to protect factory floors from such attacks is a cyber security solution that can detect the most subtle signals of threat, learning on the job to understand what is ‘normal’ for each unique ICS environment.

Darktrace’s AI learns the normal ‘patterns of life’ for every user, device, and controller across both OT and IT. By continuously analyzing data across organizations’ systems, the AI’s unique understanding of how each facet of a business and a dynamic workforce interacts ensures that any malicious activity is detected seconds after it emerges. In the case of EKANS, this self-learning approach would have identified a number of anomalous behaviors pertaining to the originally infected device, including beaconing to a rare destination and the unusual connections to encryption software.

Complementing Darktrace’s threat detection is the AI’s Autonomous Response abilities, which neutralize threats with surgical precision – allowing business activity to continue as normal. Autonomous Response has already proven itself successful in stopping ransomware attacks, preventing damaging operational outages at manufacturing facilities, hospitals, and municipalities around the world.

Conclusion

EKANS revealed that attackers are beginning to successfully target both IT and OT systems with one attack, making the need for security programs that can bridge this gap more urgent than ever. The ability to defend both environments with a single security solution ensures holistic protection for the entire organization. By correlating disparate data points across SaaS, email, cloud, traditional network, and OT environments, Cyber AI can identify and stop even the most sophisticated attacks.

The reality is that threats in the OT sphere will continue to evolve, becoming faster and more furious than ever. Given the potential damage ransomware can cause, security that can defend industrial systems along with dynamic workforces – detecting and stopping fast-acting threats across a complex business – has become more important than ever. The functionality of industrial systems depends on it.

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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
David Masson
VP, 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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