Blog
/
/
October 10, 2021

AI Uncovered Outlaw's Crypto Mining Operation

Discover how Darktrace AI technology exposed a hidden cryptocurrency mining scheme. Learn about the power of Darktrace AI in cybersecurity.
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
Oakley Cox
Director of Product
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
10
Oct 2021

Infamy is a paradoxical calling for cyber-criminals. While for some, bragging rights are a motivation for cyber-crime in and of themselves, notoriety is usually not a sensible goal for those hoping to avoid detection. This is what threat actors behind the prolific Emotet botnet learned earlier in 2021, for instance, when a coordinated effort was launched by eight national law enforcement agencies to take down their operation. There are, however, certain names which appear again and again in cyber security media and consistently avoid detection – names like Outlaw.

How Outlaw plans an ambush

Despite being active since 2018, very little is known about the hacking group Outlaw, which has staged numerous botnet and crypto-jacking attacks in China and internationally. The group is recognized by a variety of calling cards, be they repeated filenames or a tendency to illicitly mine Monero cryptocurrency, but its success ultimately lies in its tendency to adapt and evolve during months of dormancy between attacks.

Outlaw’s attacks are marked by constant changes and updates, which they work on in relative silence, before targeting security systems which are too-often defeated by the unfamiliarity of the threat.

In 2020, Outlaw gained attention when they updated their botnet toolset to find and eradicate other criminals’ crypto-jacking software, maximizing their own payout from infected devices. While it might come as no surprise that there’s no honor among cyber-thieves, this update also implemented more troubling changes which allowed Outlaw’s malware to evade traditional security defenses.

By switching disguises between each big robbery, and laying low with the loot, Outlaw ensures that traditional security systems which rely on historical attack data will never be ready for them, no matter how much notoriety is attached to their name. When organizations move beyond these systems’ rules-based approaches, however, adopting Self-Learning AI to protect their digital estates, they can begin to turn the tables on groups like Outlaw.

This blog explores how two pre-infected zombie devices in two very different parts of the world were activated by Outlaw’s botnet in the summer of 2021, and how Darktrace was able to detect the activity despite the devices being pre-infected.

Bounty hunting: First signs of attack

Figure 1: Timeline of the attack.

When a new device was added to the network of a Central American telecomms company in July, Darktrace detected a series of regular connections to two suspicious endpoints which it identified as beaconing behavior. The same behavior was noticed independently, but almost simultaneously, at a financial company in the APAC region, which was implementing Darktrace for the first time. Darktrace’s Self-Learning AI was able to identify the pre-infected devices by clustering similarly-behaving devices into peer groups within the local digital estates and therefore recognize that both were acting unusually based on a range of behaviors.

The first sign that the zombie devices had been activated by Outlaw was the initiation of cryptocurrency mining. Both devices, despite their geographical distance, were discovered to be connected to a single crypto-account, exemplifying the indiscriminate and exponential nature by which a botnet grows.

Outlaw has in the past restricted its activities to devices within China in what was assumed to be a show of caution, but recent activities like this one speak to a growing confidence.

The botnet recruitment process

The subsequent initiation of Internet Relay Chat (IRC) connections across port 443, a port more often associated with HTTPS activity, was perfectly characteristic of the Outlaw botnet’s earlier activity in 2020. IRC is a tool regularly used for communication between botmasters and zombie devices, but by using port 443 the attacker was attempting to blend into normal Internet traffic.

Soon after this exchange, the devices downloaded a shell script. Darktrace’s Cyber AI Analyst was able to intercept and recreate this shell script as it passed through the network, revealing its full function. Intriguingly, the script identified and excluded devices utilizing ARM architecture from the botnet. Due to its notably low battery consumption, ARM architecture is used primarily by portable mobile devices.

This selectivity is evidence that malicious crypto-mining remains Outlaw’s primary objective. By circumventing smaller devices which offer limited crypto-mining capabilities, this shell script focuses the botnet on the most high-powered, and therefore profitable, devices, such as desktop computers and servers. In this way, it reduces the Indicators of Compromise (IOCs) left behind by the wider botnet without greatly affecting the scale of its crypto-mining operation.

The two devices in question did not employ ARM architecture, and minutes later received a secondary payload containing a file named dota3[.]tar[.]gz, a sequel of sorts to the previous incarnation of the Outlaw botnet, ‘dota2’, which itself referenced a popular video game of the same name. With the arrival of this file, the devices appear to have been updated with the latest version of Outlaw’s world-spanning botnet.

This download was made possible in part by the attacker’s use of ‘Living off the Land’ tactics. By using only common Linux programs already present on the devices (‘curl’ and ‘Wget’ respectively), Outlaw had avoided having its activity flagged by traditional security systems. Wget, for instance, is ostensibly a reputable program used for retrieving content from web servers, and was never previously recorded as part of Outlaw’s TTPs (Tactics, Techniques, and Procedures).

By evolving and adapting its approach, Outlaw is continually able to outsmart and outrun rules-based security. Darktrace’s Self-Learning AI, however, kept pace, immediately identifying this Wget connection as suspicious and advising further investigation.

Figure 2: Cyber AI Analyst identifies Wget use on the morning of July 15 as suspicious and begins investigating potentially related HTTP connections made on the morning of July 14. In this way, it builds a complete picture of the attack.

The botnet unchained

In the following 36 hours, Darktrace detected over 6 million TCP and SSH connections directed to rare external IP addresses using ports often associated with SSH, such as 22, 2222, and 2022.

Exactly what the botnet was undertaking with these connections can only be speculated on. The devices may have been made part of a DDoS (Distributed Denial of Service) attack, bruteforce attempts on targeted SSH accounts, or simply have taken up the task of seeking and infecting new targets, further expanding the botnet. Darktrace recognized that neither device had made SSH connections prior to this event and, had Antigena been in active mode, would have enacted measures to stop them.

Figure 3: The behavior on the device before and after the bot was activated on July 14, 2021. The large spike in model breaches shows clear deviation from the established ‘pattern of life’.

Thankfully, the owners of both devices responded to Darktrace’s detection alerts soon enough to prevent any serious damage to their own digital estates. Had these devices remained under the influence of the botnet, the ramifications may have been far graver.

The use of SSH protocol would have allowed Outlaw to pivot into any number of activities, potentially compromising each device’s network further and causing data or monetary loss to their respective organizations.

Call the sheriff: Self-Learning AI

Rules-based security solutions operate much like the ‘wanted’ posters of the old west, looking out for the criminals who came through town last week without preparing for those riding over the hill today. When black hats and outlaws are adopting new looks and employing new techniques with every attack, a new way of responding to threats is needed.

Darktrace doesn’t need to know the name ‘Outlaw’, or the group’s history of evolving attacks, in order to stop them. With its fundamental self-learning approach, Darktrace learns its surroundings from the ground up, and identifies subtle deviations indicative of a cyber-threat. And with Autonomous Response, it will even take targeted action to neutralize the threat at machine speed, without the need for human intervention.

Thanks to Darktrace analyst Jun Qi Wong for his insights on the above threat find.

Learn more about how Cyber AI Analyst sheds light on complex attacks

Technical details

Darktrace model detections

  • Compliance / Crypto Currency Mining Activity
  • Compromise / High Priority Crypto Currency Mining [Enhanced Monitoring]
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / Zip or Gzip from Rare External Location
  • Anomalous Connection / Application Protocol on Uncommon Port
  • Device / Increased External Connectivity
  • Unusual Activity / Unusual External Activity
  • Compromise / SSH Beacon
  • Compromise / High Frequency SSH Beacon
  • Anomalous Connection / Multiple Connections to New External TCP Port

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
Oakley Cox
Director of Product

More in this series

No items found.

Blog

/

Network

/

May 16, 2025

Catching a RAT: How Darktrace neutralized AsyncRAT

woman working on laptopDefault blog imageDefault blog image

What is a RAT?

As the proliferation of new and more advanced cyber threats continues, the Remote Access Trojan (RAT) remains a classic tool in a threat actor's arsenal. RATs, whether standardized or custom-built, enable attackers to remotely control compromised devices, facilitating a range of malicious activities.

What is AsyncRAT?

Since its first appearance in 2019, AsyncRAT has become increasingly popular among a wide range of threat actors, including cybercriminals and advanced persistent threat (APT) groups.

Originally available on GitHub as a legitimate tool, its open-source nature has led to widespread exploitation. AsyncRAT has been used in numerous campaigns, including prolonged attacks on essential US infrastructure, and has even reportedly penetrated the Chinese cybercriminal underground market [1] [2].

How does AsyncRAT work?

Original source code analysis of AsyncRAT demonstrates that once installed, it establishes persistence via techniques such as creating scheduled tasks or registry keys and uses SeDebugPrivilege to gain elevated privileges [3].

Its key features include:

  • Keylogging
  • File search
  • Remote audio and camera access
  • Exfiltration techniques
  • Staging for final payload delivery

These are generally typical functions found in traditional RATs. However, it also boasts interesting anti-detection capabilities. Due to the popularity of Virtual Machines (VM) and sandboxes for dynamic analysis, this RAT checks for the manufacturer via the WMI query 'Select * from Win32_ComputerSystem' and looks for strings containing 'VMware' and 'VirtualBox' [4].

Darktrace’s coverage of AsyncRAT

In late 2024 and early 2025, Darktrace observed a spike in AsyncRAT activity across various customer environments. Multiple indicators of post-compromise were detected, including devices attempting or successfully connecting to endpoints associated with AsyncRAT.

On several occasions, Darktrace identified a clear association with AsyncRAT through the digital certificates of the highlighted SSL endpoints. Darktrace’s Real-time Detection effectively identified and alerted on suspicious activities related to AsyncRAT. In one notable incident, Darktrace’s Autonomous Response promptly took action to contain the emerging threat posed by AsyncRAT.

AsyncRAT attack overview

On December 20, 2024, Darktrace first identified the use of AsyncRAT, noting a device successfully establishing SSL connections to the uncommon external IP 185.49.126[.]50 (AS199654 Oxide Group Limited) via port 6606. The IP address appears to be associated with AsyncRAT as flagged by open-source intelligence (OSINT) sources [5]. This activity triggered the device to alert the ‘Anomalous Connection / Rare External SSL Self-Signed' model.

Model alert in Darktrace / NETWORK showing the repeated SSL connections to a rare external Self-Signed endpoint, 185.49.126[.]50.
Figure 1: Model alert in Darktrace / NETWORK showing the repeated SSL connections to a rare external Self-Signed endpoint, 185.49.126[.]50.

Following these initial connections, the device was observed making a significantly higher number of connections to the same endpoint 185.49.126[.]50 via port 6606 over an extended period. This pattern suggested beaconing activity and triggered the 'Compromise/Beaconing Activity to External Rare' model alert.

Further analysis of the original source code, available publicly, outlines the default ports used by AsyncRAT clients for command-and-control (C2) communications [6]. It reveals that port 6606 is the default port for creating a new AsyncRAT client. Darktrace identified both the Certificate Issuer and the Certificate Subject as "CN=AsyncRAT Server". This SSL certificate encrypts the packets between the compromised system and the server. These indicators of compromise (IoCs) detected by Darktrace further suggest that the device was successfully connecting to a server associated with AsyncRAT.

Model alert in Darktrace / NETWORK displaying the Digital Certificate attributes, IP address and port number associated with AsyncRAT.
Figure 2: Model alert in Darktrace / NETWORK displaying the Digital Certificate attributes, IP address and port number associated with AsyncRAT.
Darktrace’s detection of repeated connections to the suspicious IP address 185.49.126[.]50 over port 6606, indicative of beaconing behavior.
Figure 3: Darktrace’s detection of repeated connections to the suspicious IP address 185.49.126[.]50 over port 6606, indicative of beaconing behavior.
Darktrace's Autonomous Response actions blocking the suspicious IP address,185.49.126[.]50.
Figure 4: Darktrace's Autonomous Response actions blocking the suspicious IP address,185.49.126[.]50.

A few days later, the same device was detected making numerous connections to a different IP address, 195.26.255[.]81 (AS40021 NL-811-40021), via various ports including 2106, 6606, 7707, and 8808. Notably, ports 7707 and 8808 are also default ports specified in the original AsyncRAT source code [6].

Darktrace’s detection of connections to the suspicious endpoint 195.26.255[.]81, where the default ports (6606, 7707, and 8808) for AsyncRAT were observed.
Figure 5: Darktrace’s detection of connections to the suspicious endpoint 195.26.255[.]81, where the default ports (6606, 7707, and 8808) for AsyncRAT were observed.

Similar to the activity observed with the first endpoint, 185.49.126[.]50, the Certificate Issuer for the connections to 195.26.255[.]81 was identified as "CN=AsyncRAT Server". Further OSINT investigation confirmed associations between the IP address 195.26.255[.]81 and AsyncRAT [7].

Darktrace's detection of a connection to the suspicious IP address 195.26.255[.]81 and the domain name identified under the common name (CN) of a certificate as AsyncRAT Server
Figure 6: Darktrace's detection of a connection to the suspicious IP address 195.26.255[.]81 and the domain name identified under the common name (CN) of a certificate as AsyncRAT Server.

Once again, Darktrace's Autonomous Response acted swiftly, blocking the connections to 195.26.255[.]81 throughout the observed AsyncRAT activity.

Figure 7: Darktrace's Autonomous Response actions were applied against the suspicious IP address 195.26.255[.]81.

A day later, Darktrace again alerted to further suspicious activity from the device. This time, connections to the suspicious endpoint 'kashuub[.]com' and IP address 191.96.207[.]246 via port 8041 were observed. Further analysis of port 8041 suggests it is commonly associated with ScreenConnect or Xcorpeon ASIC Carrier Ethernet Transport [8]. ScreenConnect has been observed in recent campaign’s where AsyncRAT has been utilized [9]. Additionally, one of the ASN’s observed, namely ‘ASN Oxide Group Limited’, was seen in both connections to kashuub[.]com and 185.49.126[.]50.

This could suggest a parallel between the two endpoints, indicating they might be hosting AsyncRAT C2 servers, as inferred from our previous analysis of the endpoint 185.49.126[.]50 and its association with AsyncRAT [5]. OSINT reporting suggests that the “kashuub[.]com” endpoint may be associated with ScreenConnect scam domains, further supporting the assumption that the endpoint could be a C2 server.

Darktrace’s Autonomous Response technology was once again able to support the customer here, blocking connections to “kashuub[.]com”. Ultimately, this intervention halted the compromise and prevented the attack from escalating or any sensitive data from being exfiltrated from the customer’s network into the hands of the threat actors.

Darktrace’s Autonomous Response applied a total of nine actions against the IP address 191.96.207[.]246 and the domain 'kashuub[.]com', successfully blocking the connections.
Figure 8: Darktrace’s Autonomous Response applied a total of nine actions against the IP address 191.96.207[.]246 and the domain 'kashuub[.]com', successfully blocking the connections.

Due to the popularity of this RAT, it is difficult to determine the motive behind the attack; however, from existing knowledge of what the RAT does, we can assume accessing and exfiltrating sensitive customer data may have been a factor.

Conclusion

While some cybercriminals seek stability and simplicity, openly available RATs like AsyncRAT provide the infrastructure and open the door for even the most amateur threat actors to compromise sensitive networks. As the cyber landscape continually shifts, RATs are now being used in all types of attacks.

Darktrace’s suite of AI-driven tools provides organizations with the infrastructure to achieve complete visibility and control over emerging threats within their network environment. Although AsyncRAT’s lack of concealment allowed Darktrace to quickly detect the developing threat and alert on unusual behaviors, it was ultimately Darktrace Autonomous Response's consistent blocking of suspicious connections that prevented a more disruptive attack.

Credit to Isabel Evans (Cyber Analyst), Priya Thapa (Cyber Analyst) and Ryan Traill (Analyst Content Lead)

Appendices

  • Real-time Detection Models
       
    • Compromise / Suspicious SSL Activity
    •  
    • Compromise / Beaconing Activity To      External Rare
    •  
    • Compromise / High Volume of      Connections with Beacon Score
    •  
    • Anomalous Connection / Suspicious      Self-Signed SSL
    •  
    • Compromise / Sustained SSL or HTTP      Increase
    •  
    • Compromise / SSL Beaconing to Rare      Destination
    •  
    • Compromise / Suspicious Beaconing      Behaviour
    •  
    • Compromise / Large Number of      Suspicious Failed Connections
  •  
  • Autonomous     Response Models
       
    • Antigena / Network / Significant      Anomaly / Antigena Controlled and Model Alert
    •  
    • Antigena / Network / Significant      Anomaly / Antigena Enhanced Monitoring from Client Block

List of IoCs

·     185.49.126[.]50 - IP – AsyncRAT C2 Endpoint

·     195.26.255[.]81 – IP - AsyncRAT C2 Endpoint

·      191.96.207[.]246 – IP – Likely AsyncRAT C2 Endpoint

·     CN=AsyncRAT Server - SSL certificate - AsyncRATC2 Infrastructure

·      Kashuub[.]com– Hostname – Likely AsyncRAT C2 Endpoint

MITRE ATT&CK Mapping:

Tactic –Technique – Sub-Technique  

 

Execution– T1053 - Scheduled Task/Job: Scheduled Task

DefenceEvasion – T1497 - Virtualization/Sandbox Evasion: System Checks

Discovery– T1057 – Process Discovery

Discovery– T1082 – System Information Discovery

LateralMovement - T1021.001 - Remote Services: Remote Desktop Protocol

Collection/ Credential Access – T1056 – Input Capture: Keylogging

Collection– T1125 – Video Capture

Commandand Control – T1105 - Ingress Tool Transfer

Commandand Control – T1219 - Remote Access Software

Exfiltration– T1041 - Exfiltration Over C2 Channel

 

References

[1]  https://blog.talosintelligence.com/operation-layover-how-we-tracked-attack/

[2] https://intel471.com/blog/china-cybercrime-undergrond-deepmix-tea-horse-road-great-firewall

[3] https://www.attackiq.com/2024/08/01/emulate-asyncrat/

[4] https://www.fortinet.com/blog/threat-research/spear-phishing-campaign-with-new-techniques-aimed-at-aviation-companies

[5] https://www.virustotal.com/gui/ip-address/185.49.126[.]50/community

[6] https://dfir.ch/posts/asyncrat_quasarrat/

[7] https://www.virustotal.com/gui/ip-address/195.26.255[.]81

[8] https://www.speedguide.net/port.php?port=8041

[9] https://www.esentire.com/blog/exploring-the-infection-chain-screenconnects-link-to-asyncrat-deployment

[10] https://scammer.info/t/taking-out-connectwise-sites/153479/518?page=26

Continue reading
About the author
Isabel Evans
Cyber Analyst

Blog

/

OT

/

May 13, 2025

Revolutionizing OT Risk Prioritization with Darktrace 6.3

man in hard hat on tabletDefault blog imageDefault blog image

Powering smarter protection for industrial systems

In industrial environments, security challenges are deeply operational. Whether you’re running a manufacturing line, a power grid, or a semiconductor fabrication facility (fab), you need to know: What risks can truly disrupt my operations, and what should I focus on first?

Teams need the right tools to shift from reactive defense, constantly putting out fires, to proactively thinking about their security posture. However, most OT teams are stuck using IT-centric tools that don’t speak the language of industrial systems, are consistently overwhelmed with static CVE lists, and offer no understanding of OT-specific protocols. The result? Compliance gaps, siloed insights, and risk models that don’t reflect real-world exposure, making risk prioritization seem like a luxury.

Darktrace / OT 6.3 was built in direct response to these challenges. Developed in close collaboration with OT operators and engineers, this release introduces powerful upgrades that deliver the context, visibility, and automation security teams need, without adding complexity. It’s everything OT defenders need to protect critical operations in one platform that understands the language of industrial systems.

additions to darktrace / ot 6/3

Contextual risk modeling with smarter Risk Scoring

Darktrace / OT 6.3 introduces major upgrades to OT Risk Management, helping teams move beyond generic CVE lists with AI-driven risk scoring and attack path modeling.

By factoring in real-world exploitability, asset criticality, and operational context, this release delivers a more accurate view of what truly puts critical systems at risk.

The platform now integrates:

  • CISA’s Known Exploited Vulnerabilities (KEV) database
  • End-of-life status for legacy OT devices
  • Firewall misconfiguration analysis
  • Incident response plan alignment

Most OT environments are flooded with vulnerability data that lacks context. CVE scores often misrepresent risk by ignoring how threats move through the environment or whether assets are even reachable. Firewalls are frequently misconfigured or undocumented, and EOL (End of Life) devices, some of the most vulnerable, often go untracked.

Legacy tools treat these inputs in isolation. Darktrace unifies them, showing teams exactly which attack paths adversaries could exploit, mapped to the MITRE ATT&CK framework, with visibility into where legacy tech increases exposure.

The result: teams can finally focus on the risks that matter most to uptime, safety, and resilience without wasting resources on noise.

Automating compliance with dynamic IEC-62443 reporting

Darktrace / OT now includes a purpose-built IEC-62443-3-3 compliance module, giving industrial teams real-time visibility into their alignment with regulatory standards. No spreadsheets required!

Industrial environments are among the most heavily regulated. However, for many OT teams, staying compliant is still a manual, time-consuming process.

Darktrace / OT introduces a dedicated IEC-62443-3-3 module designed specifically for industrial environments. Security and operations teams can now map their security posture to IEC standards in real time, directly within the platform. The module automatically gathers evidence across all four security levels, flags non-compliance, and generates structured reports to support audit preparation, all in just a few clicks.Most organizations rely on spreadsheets or static tools to track compliance, without clear visibility into which controls meet standards like IEC-62443. The result is hidden gaps, resource-heavy audits, and slow remediation cycles.

Even dedicated compliance tools are often built for IT, require complex setup, and overlook the unique devices found in OT environments. This leaves teams stuck with fragmented reporting and limited assurance that their controls are actually aligned with regulatory expectations.

By automating compliance tracking, surfacing what matters most, and being purpose built for industrial environments, Darktrace / OT empowers organizations to reduce audit fatigue, eliminate blind spots, and focus resources where they’re needed most.

Expanding protocol visibility with deep insights for specialized OT operations

Darktrace has expanded its Deep Packet Inspection (DPI) capabilities to support five industry-specific protocols, across healthcare, semiconductor manufacturing, and ABB control systems.

The new protocols build on existing capabilities across all OT industry verticals and protocol types to ensure the Darktrace Self-Learning AI TM can learn intelligently about even more assets in complex industrial environments. By enabling native, AI-driven inspection of these protocols, Darktrace can identify both security threats and operational issues without relying on additional appliances or complex integrations.

Most security platforms lack native support for industry-specific protocols, creating critical visibility gaps in customer environments like healthcare, semiconductor manufacturing, and ABB-heavy industrial automation. Without deep protocol awareness, organizations struggle to accurately identify specialized OT and IoT assets, detect malicious activity concealed within proprietary protocol traffic, and generate reliable device risk profiles due to insufficient telemetry.

These blind spots result in incomplete asset inventories, and ultimately, flawed risk posture assessments which over-index for CVE patching and legacy equipment.

By combining protocol-aware detection with full-stack visibility across IT, OT, and IoT, Darktrace’s AI can correlate anomalies across domains. For example, connecting an anomaly from a Medical IoT (MIoT) device with suspicious behavior in IT systems, providing actionable, contextual insights other solutions often miss.

Conclusion

Together, these capabilities take OT security beyond alert noise and basic CVE matching, delivering continuous compliance, protocol-aware visibility, and actionable, prioritized risk insights, all inside a single, unified platform built for the realities of industrial environments.

[related-resource]

Continue reading
About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance
Your data. Our AI.
Elevate your network security with Darktrace AI