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March 27, 2025

Python-based Triton RAT Targeting Roblox Credentials

Cado Security Labs (now part of Darktrace) identified Triton RAT, a Python-based open-source tool controlled via Telegram.
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
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27
Mar 2025

Researchers from Cado Security Labs (now part of Darktrace) have identified a Python Remote Access Tool (RAT) named Triton RAT. The open-source RAT is available on GitHub and allows users to remotely access and control a system using Telegram. 

Technical analysis

In the version of the Triton RAT Pastebin. 

Telegram token and chat ID encoded in Base64
Image 1: Telegram token and chat ID encoded in Base64

Features of Triton RAT:

  • Keylogging
  • Remote commands
  • Steal saved passwords
  • Steal Roblox security cookies
  • Change wallpaper
  • Screen recording
  • Webcam access
  • Gather Wifi Information
  • Download/upload file
  • Execute shell commands
  • Steal clipboard data
  • Anti-Analysis
  • Gather system information
  • Data exfiltrated to Telegram Bot

The TritonRAT code contains many functions including the function “sendmessage” which iterates over password stores in AppData, Google, Chrome, User Data, Local, and Local State, decrypts them and saves the passwords in a text file. Additionally, the RAT searches for Roblox security cookies (.ROBLOSECURITY) in Opera, Chrome, Edge, Chromium, Firefox and Brave, if found the cookies are stored in a text file and exfiltrated. A Roblox security cookie is a browser cookie that stores the users’ session and can be used to gain access to the Roblox account bypassing 2FA. 

Function to search for and exfiltrate Roblox security cookies
Image 2: Function used to search for and exfiltrate Roblox security cookies
Function that gathers and exfiltrates system information 
Image 3: Function that gathers and exfiltrates system information 
Secondary payload retrieved from DropBox 
Image 4: Secondary payload retrieved from DropBox 

The Python script also contains code to create a VBScript and a BAT script which are executed with Powershell. The VBScript “updateagent.vbs” disables Windows Defender, creates backups and scheduled tasks for persistence and monitors specified processes. The BAT script “check.bat” retrieves a binary named “ProtonDrive.exe” from DropBox, stores it in a hidden folder and executes it with admin privileges. ProtonDrive is a pyinstaller compiled version of TritonRAT. Presumably the binary is retrieved to set up persistence. Once retrieved, ProtonDrive is stored in a created folder structure “C:\Users\user\AppData\Local\Programs\Proton\Drive”. Three scheduled tasks are created to start on logon of any user.

Tasks created
Image 5: Three tasks created to start on logon of any user

For anti-analysis, Triton RAT contains a function that checks for “blacklisted” processes which include popular tools such as xdbg, ollydbg, FakeNet, and antivirus products. Additionally, the same Git user offers a file resizer as defense evasion as some anti-virus will not check a file over a certain amount of MB.  All the exfiltrated data is sent to Telegram via a Telegram bot, where the user can send commands to the affected machine. At the time of analysis, the Telegram channel/bot had 4549 messages, although it is unknown if these are indicative of the number of infections.  

Conclusion

The emergence of the Python-based Triton RAT highlights how quickly cybercriminals are evolving their tactics to target platforms with large user bases like Roblox. Its persistence mechanisms and reliance on Telegram for data exfiltration make it both resilient and easy for attackers to operate at scale. As threats like this continue to surface, it’s critical for organizations and individuals to reinforce endpoint protection, and promote strong credential security practices to reduce exposure to such attacks.

Indicators of compromise (IoCs)

ProtonDrive.exe

Ea04f1c4016383e0846aba71ac0b0c9c

Related samples:

076dccb222d0869870444fea760c7f2b564481faea80604c02abf74f1963c265

0975fdadbbd60d90afdcb5cc59ad58a22bfdb2c2b00a5da6bb1e09ae702b95e7

1f4e1aa937e81e517bccc3bd8a981553a2ef134c11471195f88f3799720eaa9c

200fdb4f94f93ec042a16a409df383afeedbbc73282ef3c30a91d5f521481f24

29d2a70eeedbe496515c71640771f1f9b71c4af5f5698e2068c6adcac28cc3e0

2b05494926b4b1c79ee0a12a4e7f6c07e04c084a953a4ba980ed7cb9b8bf6bc2

2d1b6bd0b945ddd8261efbd85851656a7351fd892be0fa62cc3346883a8f917e

2dce8fc1584e660a0cba4db2cacdf5ff705b1b3ba75611de0900ebaeaa420bf9

2f27b8987638b813285595762fa3e56fff2213086e9ba4439942cd470fa5669a

3f9ce4d12e0303faa59a307bcfc4366d02ba73e423dbf5bcf1da5178253db64d

4309e6a9abdfedc914df3393110a68bd4acfe922e9cd9f5f24abf23df7022af7

48231f2cf5bda35634fca2f98dc6e8581e8a65a2819d62bc375376fcd501ba2d

49b2ca4c1bd4405aa724ffaef266395be4b4581f1ff38b1fc092eab71e1adb6a

4b32dbd7a6ca7f91e75bacf055f4132be0952385d4d4fcbaf0970913876d64a1

566fc3f32633ce0b9a7154102bc1620a906473d5944dca8dea122cb63cb1bcaa

59793de10ed2d3684d0206f5f69cbebbba61d1f90a79dbd720d26bbf54226695

61a2c53390498716494ffa0b586aa6dc6c67baf03855845e2e3f2539f1f56563

6707ba64cccab61d3a658b23b28b232b1f601e3608b7d9e4767a1c0751bccd05

71fabe5022f613dc8e06d6dfda1327989e67be4e291f3761e84e3a988751caf8

78573a4c23f6ccdcbfce3a467fa93d2a1a49cf2f8dc7b595c0185e16b84828cb

78b246cbd9b1106d01659dd0ab65dc367486855b6b37869673bd98c560b6ff52

7bfdbceded56029bc32d89249e0195ebf47309fecded2b6578b035c52c43460b

7cb501e819fc98a55b9d19ad0f325084f6c4753785e30479502457ac7cb6289c

7fa70e18c414ae523e84c4a01d73e49f86ab816d129e8d7001fb778531adf3a7

8bc29a873b6144b6384a5535df5fc762c0c65e47a2caf0e845382c72f9d6671f

8c1db376bafcd071ffb59130d58ffcde45b2fa8e79dcc44c0a14574b9de55b43

a99ebd095d2ccda69855f2c700048658b8e425c90c916d5880f91c8aba634a2e

b656b7189925b043770a9738d8ae003d7401ac65a58e78c643937f4b44a3bc2c

b8dc2c5921f668f6cf8a355fd1cb79020b6752330be5e0db4bf96ae904d76249

b90af78927c6cb2d767f777d36031c9160aeb6fcd30090c3db3735b71274eb4e

bc1e211206c69fe399505e18380fb0068356d205c7929e2cb3d2fe0b4107d4e0

bf3c84a955f49c02a7f4fbf94dbbf089f26137fc75f5b36ac0b1bace9373d17a

c11d186e6d1600212565786ed481fbe401af598e1f689cf1ce6ff83b5a3b4371

cd42ae47c330c68cc8fd94cf5d91992f55992292b186991605b262ba1f776e8e

e1e2587ae2170d9c4533a6267f9179dff67d03f7adbb6d1fb4f43468d8f42c24

f389a8cbb88dae49559eaa572fc9288c253ed1825b1ce2a61e3d8ae998625e18

fc55895bb7d08e6ab770a05e55a037b533de809196f3019fbff0f1f58e688e5f

MITRE ATT&CK

T1053.005 Scheduled Task/Job: Scheduled Task

T1059.006 Command and Scripting Interpreter: Python

T1082 System Information Discovery

T1016 System Network Configuration Discovery

T1105 Ingress Tool Transfer

T1562.001 Impair Defenses: Disable or Modify Tools

T1132 Data Encoding

T1021 Remote Services

T1056.001 Input Capture: Keylogging

T1555 Credentials from Password Stores

T1539 Steal Web Session Cookie

T1546.015 Event Triggered Execution: Screensaver

T1113 Screen Capture

T1125 Video Capture

T1016 System Network Configuration Discovery

T1105 Ingress Tool Transfer

T1059 Command and Scripting Interpreter

T1115 Clipboard Data

T1497 Virtualization/Sandbox Evasion

T1020 Automated Exfiltration

YARA rule

rule Triton_RAT { 
   meta: 
       description = "Detects Python-based Triton RAT" 
       author = "tgould@cadosecurity.com" 
       date = "2025-03-06" 
   strings: 
       $telegram = "telebot.TeleBot" ascii 
       $extract_data = "def extract_data" ascii 
       $bot_token = "bot_token" ascii 
       $chat_id = "chat_id" ascii 
       $keylogger = "/keylogger" ascii 
       $stop_keylogger = "/stopkeylogger" ascii 
       $passwords = "/passwords" ascii 
       $clipboard = "/clipboard" ascii 
       $roblox_cookie = "/robloxcookie" ascii 
       $wifi_pass = "/wifipass" ascii 
       $sys_commands = "/(shutdown|restart|sleep|altf4|tasklist|taskkill|screenshot|mic|wallpaper|block|unblock)" ascii 
       $win_cmds = /(taskkill \/f \/im|wmic|schtasks \/create|attrib \+h|powershell\.exe -Command|reg add|netsh wlan show profile|net user|whoami|curl ipinfo\.io)/ ascii 
       $startup = "/addstartup" ascii 
       $winblocker = "/winblocker" ascii 
       $startup_scripts = /(C:\\Windows\\System32\\updateagent\.vbs|check\.bat|watchdog\.vbs)/ ascii 
   condition: 
       any of ($telegram, $extract_data, $bot_token, $chat_id) and 
       4 of ($keylogger, $stop_keylogger, $passwords, $clipboard, $roblox_cookie, $wifi_pass, 
             $sys_commands, $win_cmds, $startup, $winblocker, $startup_scripts) 
} 
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

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September 4, 2025

Rethinking Signature-Based Detection for Power Utility Cybersecurity

power utility cybersecurityDefault blog imageDefault blog image

Lessons learned from OT cyber attacks

Over the past decade, some of the most disruptive attacks on power utilities have shown the limits of signature-based detection and reshaped how defenders think about OT security. Each incident reinforced that signatures are too narrow and reactive to serve as the foundation of defense.

2015: BlackEnergy 3 in Ukraine

According to CISA, on December 23, 2015, Ukrainian power companies experienced unscheduled power outages affecting a large number of customers — public reports indicate that the BlackEnergy malware was discovered on the companies’ computer networks.

2016: Industroyer/CrashOverride

CISA describes CrashOverride malwareas an “extensible platform” reported to have been used against critical infrastructure in Ukraine in 2016. It was capable of targeting industrial control systems using protocols such as IEC‑101, IEC‑104, and IEC‑61850, and fundamentally abused legitimate control system functionality to deliver destructive effects. CISA emphasizes that “traditional methods of detection may not be sufficient to detect infections prior to the malware execution” and recommends behavioral analysis techniques to identify precursor activity to CrashOverride.

2017: TRITON Malware

The U.S. Department of the Treasury reports that the Triton malware, also known as TRISIS or HatMan, was “designed specifically to target and manipulate industrial safety systems” in a petrochemical facility in the Middle East. The malware was engineered to control Safety Instrumented System (SIS) controllers responsible for emergency shutdown procedures. During the attack, several SIS controllers entered a failed‑safe state, which prevented the malware from fully executing.

The broader lessons

These events revealed three enduring truths:

  • Signatures have diminishing returns: BlackEnergy showed that while signatures can eventually identify adapted IT malware, they arrive too late to prevent OT disruption.
  • Behavioral monitoring is essential: CrashOverride demonstrated that adversaries abuse legitimate industrial protocols, making behavioral and anomaly detection more effective than traditional signature methods.
  • Critical safety systems are now targets: TRITON revealed that attackers are willing to compromise safety instrumented systems, elevating risks from operational disruption to potential physical harm.

The natural progression for utilities is clear. Static, file-based defenses are too fragile for the realities of OT.  

These incidents showed that behavioral analytics and anomaly detection are far more effective at identifying suspicious activity across industrial systems, regardless of whether the malicious code has ever been seen before.

Strategic risks of overreliance on signatures

  • False sense of security: Believing signatures will block advanced threats can delay investment in more effective detection methods.
  • Resource drain: Constantly updating, tuning, and maintaining signature libraries consumes valuable staff resources without proportional benefit.
  • Adversary advantage: Nation-state and advanced actors understand the reactive nature of signature defenses and design attacks to circumvent them from the start.

Recommended Alternatives (with real-world OT examples)

 Alternative strategies for detecting cyber attacks in OT
Figure 1: Alternative strategies for detecting cyber attacks in OT

Behavioral and anomaly detection

Rather than relying on signatures, focusing on behavior enables detection of threats that have never been seen before—even trusted-looking devices.

Real-world insight:

In one OT setting, a vendor inadvertently left a Raspberry Pi on a customer’s ICS network. After deployment, Darktrace’s system flagged elastic anomalies in its HTTPS and DNS communication despite the absence of any known indicators of compromise. The alerting included sustained SSL increases, agent‑beacon activity, and DNS connections to unusual endpoints, revealing a possible supply‑chain or insider risk invisible to static tools.  

Darktrace’s AI-driven threat detection aligns with the zero-trust principle of assuming the risk of a breach. By leveraging AI that learns an organization’s specific patterns of life, Darktrace provides a tailored security approach ideal for organizations with complex supply chains.

Threat intelligence sharing & building toward zero-trust philosophy

Frameworks such as MITRE ATT&CK for ICS provide a common language to map activity against known adversary tactics, helping teams prioritize detections and response strategies. Similarly, information-sharing communities like E-ISAC and regional ISACs give utilities visibility into the latest tactics, techniques, and procedures (TTPs) observed across the sector. This level of intel can help shift the focus away from chasing individual signatures and toward building resilience against how adversaries actually operate.

Real-world insight:

Darktrace’s AI embodies zero‑trust by assuming breach potential and continually evaluating all device behavior, even those deemed trusted. This approach allowed the detection of an anomalous SharePoint phishing attempt coming from a trusted supplier, intercepted by spotting subtle patterns rather than predefined rules. If a cloud account is compromised, unauthorized access to sensitive information could lead to extortion and lateral movement into mission-critical systems for more damaging attacks on critical-national infrastructure.

This reinforces the need to monitor behavioral deviations across the supply chain, not just known bad artifacts.

Defense-in-Depth with OT context & unified visibility

OT environments demand visibility that spans IT, OT, and IoT layers, supported by risk-based prioritization.

Real-world insight:

Darktrace / OT offers unified AI‑led investigations that break down silos between IT and OT. Smaller teams can see unusual outbound traffic or beaconing from unknown OT devices, swiftly investigate across domains, and get clear visibility into device behavior, even when they lack specialized OT security expertise.  

Moreover, by integrating contextual risk scoring, considering real-world exploitability, device criticality, firewall misconfiguration, and legacy hardware exposure, utilities can focus on the vulnerabilities that genuinely threaten uptime and safety, rather than being overwhelmed by CVE noise.  

Regulatory alignment and positive direction

Industry regulations are beginning to reflect this evolution in strategy. NERC CIP-015 requires internal network monitoring that detects anomalies, and the standard references anomalies 15 times. In contrast, signature-based detection is not mentioned once.

This regulatory direction shows that compliance bodies understand the limitations of static defenses and are encouraging utilities to invest in anomaly-based monitoring and analytics. Utilities that adopt these approaches will not only be strengthening their resilience but also positioning themselves for regulatory compliance and operational success.

Conclusion

Signature-based detection retains utility for common IT malware, but it cannot serve as the backbone of security for power utilities. History has shown that major OT attacks are rarely stopped by signatures, since each campaign targets specific systems with customized tools. The most dangerous adversaries, from insiders to nation-states, actively design their operations to avoid detection by signature-based tools.

A more effective strategy prioritizes behavioral analytics, anomaly detection, and community-driven intelligence sharing. These approaches not only catch known threats, but also uncover the subtle anomalies and novel attack techniques that characterize tomorrow’s incidents.

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About the author
Daniel Simonds
Director of Operational Technology

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September 3, 2025

From PowerShell to Payload: Darktrace’s Detection of a Novel Cryptomining Malware

novel cryptomining detectionDefault blog imageDefault blog image

What is Cryptojacking?

Cryptojacking remains one of the most persistent cyber threats in the digital age, showing no signs of slowing down. It involves the unauthorized use of a computer or device’s processing power to mine cryptocurrencies, often without the owner’s consent or knowledge, using cryptojacking scripts or cryptocurrency mining (cryptomining) malware [1].

Unlike other widespread attacks such as ransomware, which disrupt operations and block access to data, cryptomining malware steals and drains computing and energy resources for mining to reduce attacker’s personal costs and increase “profits” earned from mining [1]. The impact on targeted organizations can be significant, ranging from data privacy concerns and reduced productivity to higher energy bills.

As cryptocurrency continues to grow in popularity, as seen with the ongoing high valuation of the global cryptocurrency market capitalization (almost USD 4 trillion at time of writing), threat actors will continue to view cryptomining as a profitable venture [2]. As a result, illicit cryptominers are being used to steal processing power via supply chain attacks or browser injections, as seen in a recent cryptojacking campaign using JavaScript [3][4].

Therefore, security teams should maintain awareness of this ongoing threat, as what is often dismissed as a "compliance issue" can escalate into more severe compromises and lead to prolonged exposure of critical resources.

While having a security team capable of detecting and analyzing hijacking attempts is essential, emerging threats in today’s landscape often demand more than manual intervention.

This blog will discuss Darktrace’s successful detection of the malicious activity, the role of Autonomous Response in halting the cryptojacking attack, include novel insights from Darktrace’s threat researchers on the cryptominer payload, showing how the attack chain was initiated through the execution of a PowerShell-based payload.

Darktrace’s Coverage of Cryptojacking via PowerShell

In July 2025, Darktrace detected and contained an attempted cryptojacking incident on the network of a customer in the retail and e-commerce industry.

The threat was detected when a threat actor attempted to use a PowerShell script to download and run NBMiner directly in memory.

The initial compromise was detected on July 22, when Darktrace / NETWORK observed the use of a new PowerShell user agent during a connection to an external endpoint, indicating an attempt at remote code execution.

Specifically, the targeted desktop device established a connection to the rare endpoint, 45.141.87[.]195, over destination port 8000 using HTTP as the application-layer protocol. Within this connection, Darktrace observed the presence of a PowerShell script in the URI, specifically ‘/infect.ps1’.

Darktrace’s analysis of this endpoint (45.141.87[.]195[:]8000/infect.ps1) and the payload it downloaded indicated it was a dropper used to deliver an obfuscated AutoIt loader. This attribution was further supported by open-source intelligence (OSINT) reporting [5]. The loader likely then injected NBMiner into a legitimate process on the customer’s environment – the first documented case of NBMiner being dropped in this way.

Darktrace’s detection of a device making an HTTP connection with new PowerShell user agent, indicating PowerShell abuse for command-and-control (C2) communications.
Figure 1: Darktrace’s detection of a device making an HTTP connection with new PowerShell user agent, indicating PowerShell abuse for command-and-control (C2) communications.

Script files are often used by malicious actors for malware distribution. In cryptojacking attacks specifically, scripts are used to download and install cryptomining software, which then attempts to connect to cryptomining pools to begin mining operations [6].

Inside the payload: Technical analysis of the malicious script and cryptomining loader

To confidently establish that the malicious script file dropped an AutoIt loader used to deliver the NBMiner cryptominer, Darktrace’s threat researchers reverse engineered the payload. Analysis of the file ‘infect.ps1’ revealed further insights, ultimately linking it to the execution of a cryptominer loader.

Screenshot of the ‘infect.ps1’ PowerShell script observed in the attack.
Figure 2: Screenshot of the ‘infect.ps1’ PowerShell script observed in the attack.

The ‘infect.ps1’ script is a heavily obfuscated PowerShell script that contains multiple variables of Base64 and XOR encoded data. The first data blob is XOR’d with a value of 97, after decoding, the data is a binary and stored in APPDATA/local/knzbsrgw.exe. The binary is AutoIT.exe, the legitimate executable of the AutoIt programming language. The script also performs a check for the existence of the registry key HKCU:\\Software\LordNet.

The second data blob ($cylcejlrqbgejqryxpck) is written to APPDATA\rauuq, where it will later be read and XOR decoded. The third data blob ($tlswqbblxmmr)decodes to an obfuscated AutoIt script, which is written to %LOCALAPPDATA%\qmsxehehhnnwioojlyegmdssiswak. To ensure persistence, a shortcut file named xxyntxsmitwgruxuwqzypomkhxhml.lnk is created to run at startup.

 Screenshot of second stage AutoIt script.
Figure 3: Screenshot of second stage AutoIt script.

The observed AutoIt script is a process injection loader. It reads an encrypted binary from /rauuq in APPDATA, then XOR-decodes every byte with the key 47 to reconstruct the payload in memory. Next, it silently launches the legitimate Windows app ‘charmap.exe’ (Character Map) and obtains a handle with full access. It allocates executable and writable memory inside that process, writes the decrypted payload into the allocated region, and starts a new thread at that address. Finally, it closes the thread and process handles.

The binary that is injected into charmap.exe is 64-bit Windows binary. On launch, it takes a snapshot of running processes and specifically checks whether Task Manager is open. If Task Manager is detected, the binary kills sigverif.exe; otherwise, it proceeds. Once the condition is met, NBMiner is retrieved from a Chimera URL (https://api[.]chimera-hosting[.]zip/frfnhis/zdpaGgLMav/nbminer[.]exe) and establishes persistence, ensuring that the process automatically restarts if terminated. When mining begins, it spawns a process with the arguments ‘-a kawpow -o asia.ravenminer.com:3838 -u R9KVhfjiqSuSVcpYw5G8VDayPkjSipbiMb.worker -i 60’ and hides the process window to evade detection.

Observed NBMiner arguments.
Figure 4: Observed NBMiner arguments.

The program includes several evasion measures. It performs anti-sandboxing by sleeping to delay analysis and terminates sigverif.exe (File Signature Verification). It checks for installed antivirus products and continues only when Windows Defender is the sole protection. It also verifies whether the current user has administrative rights. If not, it attempts a User Account Control (UAC) bypass via Fodhelper to silently elevate and execute its payload without prompting the user. The binary creates a folder under %APPDATA%, drops rtworkq.dll extracted from its own embedded data, and copies ‘mfpmp.exe’ from System32 into that directory to side-load ‘rtworkq.dll’. It also looks for the registry key HKCU\Software\kap, creating it if it does not exist, and reads or sets a registry value it expects there.

Zooming Out: Darktrace Coverage of NBMiner

Darktrace’s analysis of the malicious PowerShell script provides clear evidence that the payload downloaded and executed the NBMiner cryptominer. Once executed, the infected device is expected to attempt connections to cryptomining endpoints (mining pools). Darktrace initially observed this on the targeted device once it started making DNS requests for a cryptominer endpoint, “gulf[.]moneroocean[.]stream” [7], one minute after the connection involving the malicious script.

Darktrace Advanced Search logs showcasing the affected device making a DNS request for a Monero mining endpoint.
Figure 5: Darktrace Advanced Search logs showcasing the affected device making a DNS request for a Monero mining endpoint.

Though DNS requests do not necessarily mean the device connected to a cryptominer-associated endpoint, Darktrace detected connections to the endpoint specified in the DNS Answer field: monerooceans[.]stream, 152.53.121[.]6. The attempted connections to this endpoint over port 10001 triggered several high-fidelity model alerts in Darktrace related to possible cryptomining mining activity. The IP address and destination port combination (152.53.121[.]6:10001) has also been linked to cryptomining activity by several OSINT security vendors [8][9].

Darktrace’s detection of a device establishing connections with the Monero Mining-associated endpoint, monerooceans[.]stream over port 10001.
Figure 6: Darktrace’s detection of a device establishing connections with the Monero Mining-associated endpoint, monerooceans[.]stream over port 10001.

Darktrace / NETWORK grouped together the observed indicators of compromise (IoCs) on the targeted device and triggered an additional Enhanced Monitoring model designed to identify activity indicative of the early stages of an attack. These high-fidelity models are continuously monitored and triaged by Darktrace’s SOC team as part of the Managed Threat Detection service, ensuring that subscribed customers are promptly notified of malicious activity as soon as it emerges.

Figure 7: Darktrace’s correlation of the initial PowerShell-related activity with the cryptomining endpoint, showcasing a pattern indicative of an initial attack chain.

Darktrace’s Cyber AI Analyst launched an autonomous investigation into the ongoing activity and was able to link the individual events of the attack, encompassing the initial connections involving the PowerShell script to the ultimate connections to the cryptomining endpoint, likely representing cryptomining activity. Rather than viewing these seemingly separate events in isolation, Cyber AI Analyst was able to see the bigger picture, providing comprehensive visibility over the attack.

Darktrace’s Cyber AI Analyst view illustrating the extent of the cryptojacking attack mapped against the Cyber Kill Chain.
Figure 8: Darktrace’s Cyber AI Analyst view illustrating the extent of the cryptojacking attack mapped against the Cyber Kill Chain.

Darktrace’s Autonomous Response

Fortunately, as this customer had Darktrace configured in Autonomous Response mode, Darktrace was able to take immediate action by preventing  the device from making outbound connections and blocking specific connections to suspicious endpoints, thereby containing the attack.

Darktrace’s Autonomous Response actions automatically triggered based on the anomalous connections observed to suspicious endpoints.
Figure 9: Darktrace’s Autonomous Response actions automatically triggered based on the anomalous connections observed to suspicious endpoints.

Specifically, these Autonomous Response actions prevented the outgoing communication within seconds of the device attempting to connect to the rare endpoints.

Figure 10: Darktrace’s Autonomous Response blocked connections to the mining-related endpoint within a second of the initial connection.

Additionally, the Darktrace SOC team was able to validate the effectiveness of the Autonomous Response actions by analyzing connections to 152.53.121[.]6 using the Advanced Search feature. Across more than 130 connection attempts, Darktrace’s SOC confirmed that all were aborted, meaning no connections were successfully established.

Figure 11: Advanced Search logs showing all attempted connections that were successfully prevented by Darktrace’s Autonomous Response capability.

Conclusion

Cryptojacking attacks will remain prevalent, as threat actors can scale their attacks to infect multiple devices and networks. What’s more, cryptomining incidents can often be difficult to detect and are even overlooked as low-severity compliance events, potentially leading to data privacy issues and significant energy bills caused by misused processing power.

Darktrace’s anomaly-based approach to threat detection identifies early indicators of targeted attacks without relying on prior knowledge or IoCs. By continuously learning each device’s unique pattern of life, Darktrace can detect subtle deviations that may signal a compromise.

In this case, the cryptojacking attack was quickly identified and mitigated during the early stages of malware and cryptomining activity. Darktrace's Autonomous Response was able to swiftly contain the threat before it could advance further along the attack lifecycle, minimizing disruption and preventing the attack from potentially escalating into a more severe compromise.

Credit to Keanna Grelicha (Cyber Analyst) and Tara Gould (Threat Research Lead)

Appendices

Darktrace Model Detections

NETWORK Models:

·      Compromise / High Priority Crypto Currency Mining (Enhanced Monitoring Model)

·      Device / Initial Attack Chain Activity (Enhanced Monitoring Model)

·      Compromise / Suspicious HTTP and Anomalous Activity (Enhanced Monitoring Model)

·      Compromise / Monero Mining

·      Anomalous File / Script from Rare External Location

·      Device / New PowerShell User Agent

·      Anomalous Connection / New User Agent to IP Without Hostname

·      Anomalous Connection / Powershell to Rare External

·      Device / Suspicious Domain

Cyber AI Analyst Incident Events:

·      Detect \ Event \ Possible HTTP Command and Control

·      Detect \ Event \ Cryptocurrency Mining Activity

Autonomous Response Models:

·      Antigena / Network::Significant Anomaly::Antigena Alerts Over Time Block

·      Antigena / Network::External Threat::Antigena Suspicious Activity Block

·      Antigena / Network::Significant Anomaly::Antigena Enhanced Monitoring from Client Block

·      Antigena / Network::External Threat::Antigena Crypto Currency Mining Block

·      Antigena / Network::External Threat::Antigena File then New Outbound Block

·      Antigena / Network::External Threat::Antigena Suspicious File Block

·      Antigena / Network::Significant Anomaly::Antigena Significant Anomaly from Client Block

List of Indicators of Compromise (IoCs)

(IoC - Type - Description + Confidence)

·      45.141.87[.]195:8000/infect.ps1 - IP Address, Destination Port, Script - Malicious PowerShell script

·      gulf.moneroocean[.]stream - Hostname - Monero Endpoint

·      monerooceans[.]stream - Hostname - Monero Endpoint

·      152.53.121[.]6:10001 - IP Address, Destination Port - Monero Endpoint

·      152.53.121[.]6 - IP Address – Monero Endpoint

·      https://api[.]chimera-hosting[.]zip/frfnhis/zdpaGgLMav/nbminer[.]exe – Hostname, Executable File – NBMiner

·      Db3534826b4f4dfd9f4a0de78e225ebb – Hash – NBMiner loader

MITRE ATT&CK Mapping

(Tactic – Technique – Sub-Technique)

·      Vulnerabilities – RESOURCE DEVELOPMENT – T1588.006 - T1588

·      Exploits – RESOURCE DEVELOPMENT – T1588.005 - T1588

·      Malware – RESOURCE DEVELOPMENT – T1588.001 - T1588

·      Drive-by Compromise – INITIAL ACCESS – T1189

·      PowerShell – EXECUTION – T1059.001 - T1059

·      Exploitation of Remote Services – LATERAL MOVEMENT – T1210

·      Web Protocols – COMMAND AND CONTROL – T1071.001 - T1071

·      Application Layer Protocol – COMMAND AND CONTROL – T1071

·      Resource Hijacking – IMPACT – T1496

·      Obfuscated Files - DEFENSE EVASION - T1027                

·      Bypass UAC - PRIVILEGE ESCALATION – T1548.002

·      Process Injection – PRIVILEGE ESCALATION – T055

·      Debugger Evasion – DISCOVERY – T1622

·      Logon Autostart Execution – PERSISTENCE – T1547.009

References

[1] https://www.darktrace.com/cyber-ai-glossary/cryptojacking#:~:text=Battery%20drain%20and%20overheating,fee%20to%20%E2%80%9Cmine%20cryptocurrency%E2%80%9D.

[2] https://coinmarketcap.com/

[3] https://www.ibm.com/think/topics/cryptojacking

[4] https://thehackernews.com/2025/07/3500-websites-hijacked-to-secretly-mine.html

[5] https://urlhaus.abuse.ch/url/3589032/

[6] https://www.logpoint.com/en/blog/uncovering-illegitimate-crypto-mining-activity/

[7] https://www.virustotal.com/gui/domain/gulf.moneroocean.stream/detection

[8] https://www.virustotal.com/gui/domain/monerooceans.stream/detection

[9] https://any.run/report/5aa8cd5f8e099bbb15bc63be52a3983b7dd57bb92566feb1a266a65ab5da34dd/351eca83-ef32-4037-a02f-ac85a165d74e

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About the author
Keanna Grelicha
Cyber Analyst
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