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July 27, 2023

Revealing Outlaw's Returning Features & New Tactics

Darktrace's investigation of the latest Outlaw crypto-mining operation, covering the resurgence of old tactics along with the emergence of new ones.
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
Adam Potter
Senior Cyber Analyst
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27
Jul 2023

What is Outlaw Cryptocurrency Mining Operation?

The cybersecurity community has been aware of the threat of Outlaw cryptocurrency mining operation, and its affiliated activities since as early as 2018. Despite its prominence, Outlaw remains largely elusive to researchers and analysts due to its ability to adapt its tactics, procedures, and payloads.

Outlaw gained notoriety in 2018 as security researchers began observing the creation of affiliated botnets.[1][2]  Researchers gave Outlaw  its name based on the English translation of the “Haiduc” tool observed during their initial activity on compromised devices.[3],[4] By 2019, much of the initial Outlaw activity  focused on the targeting of Internet of Things (IoT) devices and other internet facing servers, reportedly focusing operations in China and on Chinese devices.[5],[6]  From the outset, mining operations featured as a core element of botnets created by the group.[7] This initial focus may have been a sign of caution by threat actors or a preliminary means of testing procedures and operation efficacy. Regardless, Outlaw actors inevitably expanded scope, targeting larger organizations and a wider range of internet facing devices across geographic scope.

Following a short period of inactivity, security researchers began to observe new Outlaw activity, showcasing additional capabilities such as the ability to kill existing crypto-mining processes on devices, thereby reclaiming devices already compromised by crypto-jacking. [8],[9]

Latest News on Outlaw

Although the more recently observed incidents of Outlaw did demonstrate some new tactics, many of its procedures remained the same, including its unique bundling of payloads that combine crypto-mining and botnet capabilities. [10] In conjunction, the continued use of mining-specific payloads and growth of affiliated botnets has bolstered the belief that Outlaw actors historically prioritizes financial gain, in lieu of overt political objectives.

Given the tendency for malicious actors to share tools and capabilities, true attribution of threat or threat group is extremely difficult in the wild. As such, a genuine survey of activity from the group across a customer base has not always been possible. Therefore, we will present an updated look into more recent activity associated with Outlaw detected across the Darktrace customer base.  

Darktrace vs Outlaw

Since late 2022, Darktrace has observed a rise in probable cyber incidents involving indicators of compromise (IoCs) associated with Outlaw. Given its continued prevalence and relative dearth of information, it is essential to take a renewed look at the latest campaign activity associated with threats like Outlaw to avoid making erroneous assumptions and to ensure the threat posed is correctly characterized.

While being aware of previous IoCs and tactics known to be employed in previous campaigns will go some way to protecting against future Outlaw attacks, it is paramount for organizations to arm themselves with an autonomous intelligent decision maker that can identify malicious activity, based on recognizing deviations from expected patterns of behavior, and take preventative action to effectively defend against such a versatile threat.

Darktrace’s anomaly-based approach to threat detection means it is uniquely positioned to detect novel campaign activity by recognizing subtle deviations in affected devices’ behavior that would have gone unnoticed by traditional security tools relying on rules, signatures and known IoCs.

Outlaw Attack Overview & Darktrace Coverage

From late 2022 through early 2023, Darktrace identified multiple cyber events involving IP addresses, domains, and payloads associated with Outlaw on customer networks. In this recent re-emergence of campaign activity, Darktrace identified numerous attack vectors and IoCs that had previously been associated with Outlaw, however it also observed significant deviations from previous campaigns.

Returning Features

As outlined in a previous blog, past iterations of Outlaw compromises include four identified, distinct phases:

1. Targeting of internet facing devices via SSH brute-forcing

2. Initiation of crypto-mining operations

3. Download of shell script and/or botnet malware payloads

4. Outgoing external SSH scanning to propagate the botnet

Nearly all affected devices analyzed by Darktrace were tagged as internet facing, as identified in previous campaigns, supporting the notion that Outlaw continues to focus on easily exposed devices. In addition to this, Darktrace observed three other core returning features from previous Outlaw campaigns in affected devices between late 2022 and early 2023:

1. Gzip and/or Script Download

2. Beaconing Activity (Command and Control)

3. Crypto-mining

Gzip and/or Script Download

Darktrace observed numerous devices downloading the Dota malware, a strain that is previously known to have been associated with the Outlaw botnet, as either a gzip file or a shell script from rare external hosts.

In some examples, IP addresses that provided the payload were flagged by open-source intelligence (OSINT) sources as having engaged in widespread SSH brute-forcing activities. While the timing of the payload transfer to the device was not consistent, download of gzip files featured prominently during directly observed or potentially affiliated activity. Moreover, Darktrace detected multiple devices performing HTTP requests for shell scripts (.sh) according to detected connection URIs. Darktrace DETECT was able to identify these anomalous connections due to the rarity of the endpoint, payloads, and connectivity for the devices.

Figure 1: Darktrace Cyber AI Analyst technical details summary from an incident during the analysis timeframe that highlights a breach device retrieving the anomalous shell scripts using wget.

Beaconing Activity – Command and Control (C2) Endpoint

Across all Outlaw activity identified by Darktrace, devices engaged in some form of beaconing behavior, rather than one-off connections to IPs associated with Outlaw. While the use of application protocol was not uniform, repeated connectivity to rare external IP addresses related to Outlaw occurred across many analyzed incidents. Darktrace’s Self-Learning AI understood that this beaconing activity represented devices deviating from their expected patterns of life and was able to bring it to the immediate attention of customer security teams.

Figure 2: Model breach log details showing sustained, repeated connectivity to Outlaw affiliated endpoint over port 443, indicating potential C2 activity.

Crypto-mining

In almost every incident of Outlaw identified across the fleet, Darktrace detected some form of cryptocurrency mining activity. Devices affected by Outlaw were consistently observed making anomalous connections to external endpoints associated with crypto-mining operations. Furthermore, the Minergate protocol appeared consistently across hosts; even when devices did not make direct crypto-mining commands, such hosts attempted connections to external entities that were known to support crypto-mining operations.

Figure 3: Advanced Search results showing a sudden spike in mining activity from a device observed connecting to Outlaw-affiliated IP addresses. Such crypto-mining activity was observed consistently across analyzed incidents.

Is Outlaw Using New Tactics?

While in the past, Outlaw activity was identified through a systematic kill chain, recent investigations conducted by Darktrace show significant deviations from this.

For instance, affected devices do not necessarily follow the previously outlined kill chain directly as they did previously. Instead, Darktrace observed affected devices exhibiting these phases in differing orders, repeating steps, or missing out attack phases entirely.

It is essential to study such variation in the kill chain to learn more about the threat of Outlaw and how threat actors are continuing to use it is varying ways. These discrepancies in kill chain elements are likely impacted by visibility into the networks and devices of Darktrace customers, with some relevant activity falling outside of Darktrace’s purview. This is particularly true for internet-exposed devices and hosts that repeatedly performed the same anomalous activity (such as making Minergate requests). Moreover, some devices involved in Outlaw activity may have already been compromised prior to Darktrace’s visibility into the network. As such, these conclusions must be evaluated with a degree of uncertainty.

SSH Activity

Although external SSH connectivity was apparent in some of the incidents detected by Darktrace, it was not directly related to brute-forcing activity. Affected devices did receive anomalous incoming SSH connections, however, wide ranging SSH failed connectivity following the initiation of mining operations by compromised devices was not readily apparent across analyzed compromises. Connections over port 22 were more frequently associated with beaconing and/or C2 activity to endpoints associated with Outlaw, than with potential brute-forcing. As such, Darktrace could not, with high confidence correlate such SSH activity to brute-forcing. This could suggest that threat actors are now portioning or rotation of botnet devices for different operations, for example dividing between botnet expansion and mining operations.

Command line tools

In cases of Outlaw investigated by Darktrace, there was also a degree of variability involving the tools used to retrieve payloads. On the networks of customers affected by Outlaw, Darktrace DETECT identified the use of user agents and command line tools that it considered to be out of character for the network and its devices.

When retrieving the Dota malware payload or shell script data, compromised devices frequently relied on numerous versions of wget and curl user agents. Although the use of such tools as a tactic cannot be definitively linked to the crypto-mining campaign, the employment of varying and/or outdated native command line tools attests to the procedural flexibility of Outlaw campaigns, and its potential for continued evolution.

Figure 4: Breach log data showing use of curl and wget tools to connect to IP addresses associated with Outlaw.

Outlaw in 2023

Given Outlaw’s widespread notoriety and its continued activities, it is likely to remain a prominent threat to organizations and security teams across the threat landscape in 2023 and beyond.

As Darktrace has observed within its customer base from late 2022 through early 2023, activity linked with the Outlaw cryptocurrency mining campaign continues to transpire, offering security teams and research a renewed look at how it has evolved and adapted over the years. While many of its features and tactics appear to have remained consistent, Darktrace has identified numerous signs of Outlaw deviating from its previously known activities.

While relying on previously established IoCs and known tactics from previous campaigns will go some way to protecting an organization’s network from Outlaw compromises, there is a greater need than ever to go further than this. Rather than depending on a list of known-bads or traditional signatures and rules, Darktrace’s anomaly-based approach to threat detection and unparallel autonomous response capabilities mean it is uniquely positioned to DETECT and RESPOND to Outlaw activity, regardless of how it evolves in the future.

Credit to: Adam Potter, Cyber Analyst, Nahisha Nobregas, SOC Analyst, and Ryan Traill, Threat Content Lead

Relevant DETECT Model Breaches:

Compliance / Incoming SSH  

Device / New User Agent and New IP

Device / New User Agent  

Anomalous Connection / New User Agent to IP Without Hostname  

Compromise / Crypto Currency Mining Activity  

Anomalous File / Internet Facing System File Download  

Anomalous Server Activity / New User Agent from Internet Facing System  

Anomalous File / Zip or Gzip from Rare External Location  

Anomalous File / Script from Rare External Location  

Anomalous Connection / Multiple Failed Connections to Rare Endpoint  

Compromise / Large Number of Suspicious Failed Connections  

Anomalous Server Activity / Outgoing from Server  

Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

Indicators of Compromise

Indicator - Type - Description

/dota3.tar.gz​

File  URI​

Outlaw  payload​

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/tddwrt7s.sh​

File  URI​

Outlaw  payload​

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73e5dbafa25946ed636e68d1733281e63332441d​

SHA1  Hash​

Outlaw  payload​

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debian-package[.]center​

Hostname​

Outlaw  C2 endpoint​

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161.35.236[.]24​

IP  address​

Outlaw  C2 endpoint​

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138.68.115[.]96​

IP  address​

Outlaw C2  endpoint​

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67.205.134[.]224​

IP  address​

Outlaw C2  endpoint​

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138.197.212[.]204​

IP  address​

Outlaw C2  endpoint​

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45.9.148[.]59 ​

IP  address​

Possible  Outlaw C2 endpoint​

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45.9.148[.]117​

IP  address​

Outlaw C2  endpoint​

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45.9.148[.]125​

IP  address​

Outlaw C2  endpoint​

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45.9.148[.]129​

IP  address​

Outlaw C2  endpoint​

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45.9.148[.]99 ​

IP  address​

Outlaw C2  endpoint​

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45.9.148[.]234​

IP  address​

Possible  Outlaw C2 endpoint​

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45.9.148[.]236​

IP  address​

Possible  Outlaw C2 endpoint​

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159.203.102[.]122​

IP  address​

Outlaw C2  endpoint​

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159.203.85[.]196​

IP  address​

Outlaw C2  endpoint​

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159.223.235[.]198​

IP  address​

Outlaw C2  endpoint​

MITRE ATT&CK Mapping

Tactic -Technique

Initial Access -T1190  Exploit - Public Facing Application

Command and Control - T1071 - Application - Layer Protocol

T1071.001 - Application Layer Protocol: Web Protocols

Impact - T1496 Resource Hijacking

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
Adam Potter
Senior Cyber Analyst

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September 25, 2026

A Chain Reaction: Blockchain-Hosted Infostealer Campaign Targets Windows and macOS

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Key Insights

  • Darktrace detected a blockchain-hosted infostealer campaign targeting Windows and macOS devices across multiple customer environments.
  • The campaign combined ClickFix social engineering with trusted services and decentralized blockchain infrastructure to support malware delivery and C2 activity.
  • Compromised devices were observed connecting to rare and unusual external endpoints, including DGA C2 domains, blockchain-related endpoints, and cryptocurrency mining infrastructure.
  • The activity was associated with information-stealing malware strains including Atomic macOS Stealer (AMOS), Lumma, Rhadamanthys, Vidar, and Phexia.
  • Darktrace identified anomalous device behavior, beaconing patterns, rare external connections, cryptomining activity, and suspicious TLS/SSL communications without relying solely on prior knowledge or static indicators of compromise.
  • The campaign highlights how attackers are increasingly using legitimate and decentralized infrastructure to make detection, disruption, and attribution more challenging for defenders.

The Infostealer Ecosystem

The information stealer malware ecosystem continues to grow in value for threat actors across the digital threat landscape. Infostealers are increasingly delivered through Malware-as-a-Service (MaaS) operating models, distributed through affiliate networks, and designed to withstand infrastructure takedowns. This resilience was demonstrated by the recent takedown of Lumma Stealer malicious domains by Microsoft’s Digital Crimes Unit (DCU) [1].

Infostealers are used to gather and exfiltrate sensitive information, including non-human identity (NHI) data, from compromised systems across cloud, Software-as-a-Service (SaaS), Virtual Private Network (VPN), and development environments. They can also support ransomware operations by expanding the credentials and access paths available to threat actors, contributing to the high volume of identity-based attacks observed across the broader threat landscape [2][3].

Darktrace’s Observations of ClickFix and Infostealers

Throughout 2026, Darktrace has observed multiple campaigns using ClickFix social engineering to trick users into carrying out malicious actions and downloading initial payloads, including information stealers. More recently, Darktrace’s Threat Research team identified a specific ClickFix campaign involving a blockchain-hosted infostealer targeting Windows and macOS devices.

Darktrace identified affected customer environments across Europe, the United States, Asia, and the Middle East where blockchain-hosted infostealer malware appears to have been delivered to compromised systems following likely ClickFix-driven initial access. Darktrace investigated the activity and found that decentralized blockchain infrastructure, alongside widely trusted legitimate services, was used to support malware delivery and information theft across Windows and macOS systems.

Following initial access, compromised systems established C2 communication, with C2 configuration and payloads hosted on public blockchain infrastructure. The ultimate objective appears to be credential and cryptocurrency theft through the deployment of information stealers such as Atomic macOS Stealer (AMOS), Lumma, Rhadamanthys, and Vidar [5][6][7].

Darktrace’s Investigation

Affected devices across the Darktrace customer base were observed making outbound connections to rare external endpoints in patterns consistent with beaconing and C2 activity. Darktrace primarily detected devices making repeated connections to algorithmically generated domains (DGA) such as hf98x4d[.]site [8]. In many cases, these domains were linked through open-source intelligence (OSINT) to information-stealing malware families including AMOS and Phexia [5][6][7][8][9].

In multiple cases, devices were also observed connecting to blockchain-related endpoints, such as polygon[.]drpc[.]org, as well as legitimate public services, including GitHub. The use of decentralized blockchain infrastructure and trusted services such as GitHub to facilitate malware distribution and C2 activity can make disruption and attribution significantly more difficult for defenders.

Darktrace alsodetected a significant proportion of impacted devices making outboundconnections to cryptocurrency mining infrastructure associated with thelegitimate open-source XMRig mining software and the HashVault mining pool,including pool.hashvault[.]pro and donate[.]ssl[.]xmrig[.]com, which wereabused by the attackers, indicating, includingpool.hashvault[.]pro and donate[.]ssl[.]xmrig[.]com, indicating active cryptominingon compromised systems.

In one case, mining activity was observed before and during connections to the DGA endpoint hf98x4d[.]site. Due to its highly anomalous nature, Darktrace's Real-Time AI Analyst autonomously investigated the activity as it occurred, correlating the two events into a single cryptocurrency mining incident and providing comprehensive visibility into the broader attack.

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Figure 1: Real-Time AI Analyst investigation of suspicious SSL and C2 communications with hf98x4d[.]site over port 443.

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Figure 2: Real-Time AI Analyst investigation into cryptocurrency mining activity involving pool[.]hashvault[.]pro over SSL on port 443.

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Around the same time, Darktrace identified the same device initiating connections to the GitHub endpoint release-assets[.]githubusercontent[.]com while continuing to make repeated connections to hf98x4d[.]site.

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Figure 3: Darktrace's detection of an affected device connecting to a GitHub endpoint between repeated connections to the anomalous external endpoint hf98x4d[.]site.

On the network of another customer, Darktrace observed an affected device making highly unusual outbound connections consistent with beaconing activity. The device initiated multiple connections over port 443 to the external hostname polygon[.]drpc[.]org. According to OSINT, this hostname is a Remote Procedure Call (RPC) endpoint provided by dRPC, a legitimate service enabling decentralized applications (dApps), cryptocurrency wallets, and developer tools to interact with the Polygon blockchain [10].

The same device was later observed making repeated TLS/SSL connections to the previously mentioned DGA C2 domain. In addition, it made outbound connections to the external IP 195.242.214[.]34 over destination port 51820, an endpoint associated with the ProtonVPN service. Collectively, these connections to blockchain-related infrastructure, the DGA C2 domain, and ProtonVPN-associated infrastructure suggested the device had been affected by the campaign.

Conclusion

This campaign demonstrates how attackers can combine ClickFix social engineering with trusted services and decentralized blockchain infrastructure to create a resilient, cross-platform malware delivery chain. By using services such as GitHub alongside blockchain RPC endpoints and rapidly replaceable DGA domains, the activity can blend into legitimate traffic while making infrastructure disruption and attribution more difficult.

For defenders, it’s a reminder that trusted infrastructure does not automatically mean trusted activity. Security teams should look for the behaviors surrounding these connections, including unusual outbound communication, repeated beaconing, unexpected access to blockchain services, suspicious TLS/SSL activity and cryptomining. In this campaign, Darktrace identified and correlated these deviations without depending solely on previously known indicators, providing visibility as affected devices moved between legitimate services, decentralized infrastructure and malicious C2 endpoints

Credit to Nahisha Nobregas (Associate Principal Cyber Analyst), Manoel Kadja (Senior Cyber Analyst)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

▪ Compromise / Beaconing Activity To External Rare

▪ Compromise / Beacon to Young Endpoint

▪ Compromise / Fast Beaconing to DGA

▪ Compromise / High Volume of Connections with Beacon Score

▪ Compromise / DGA Beacon

▪ Compromise / Slow Beaconing Activity To External Rare

▪ Compromise / Agent Beacon (Long Period)

▪ Compromise / Agent Beacon (Medium Period)

▪ Compromise / Sustained SSL or HTTP Increase

▪ Compromise / Large Number of Suspicious Failed Connections

▪ Compromise / SSL Beaconing to Rare Destination

▪ Compromise / Beacon for 4 Days

▪ Compromise / High Priority Crypto Currency Mining

▪ Compromise / Monero Mining

▪ Device / Long Agent Connection to New Endpoint

▪ Device / New Connections On Suspicious Port

▪ Anomalous Connection / High Volume of Connections to Rare Domain

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List of Indicators of Compromise (IoCs)

 
Indicator Description
hf98x4d[.]site C2 Endpoint (Hostname)
sj98xe4[.]xyz C2 Endpoint (Hostname)
citcix6[.]xyz C2 Endpoint (Hostname)
bduwih8[.]pro C2 Endpoint (Hostname)

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MITRE ATT&CK Mapping

 
Tactic (ID) Technique
Persistence (T1176) Browser Extensions (T1176.001)
Persistence (T1176) Software Extensions
Command and Control (T1071) Web Protocols (T1071.001)
Command and Control (T1568) Domain Generation Algorithms (T1568.002)
Command and Control (T1071) Application Layer Protocol
Command and Control (T1102) One-Way Communication (T1102.003)
Command and Control (T1571) Non-Standard Port
Command and Control (T1104) Multi-Stage Channels
Command and Control (T1573) Encrypted Channel
Command and Control (T1008) Fallback Channels
Initial Access ICS (T0862) Supply Chain Compromise
Command and Control ICS (T0885) Commonly Used Port
Collection (T1185) Browser Session Hijacking
Impact (T1496) Compute Hijacking (T1496.001)
Impact (T1496) Resource Hijacking
Command and Control (T1071) Publish/Subscribe Protocols (T1071.001)
Lateral Movement (T1210) Exploitation of Remote Services

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References:

1.        https://www.microsoft.com/en-us/security/blog/2025/05/21/lumma-stealer-breaking-down-the-delivery-techniques-and-capabilities-of-a-prolific-infostealer/

2.        https://spycloud.com/resource/report/spycloud-annual-identity-exposure-report-2026/

3.        https://www.darktrace.com/blog/why-trust-is-the-new-attack-surface-darktraces-mid-year-threat-update-2026

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

5.        https://abekweng.medium.com/inside-a-blockchain-hosted-malware-campaign-targeting-windows-and-macos-f5bcdeffed66

6.        https://cloud.google.com/blog/topics/threat-intelligence/unc5142-etherhiding-distribute-malware

7.        https://haveibeensquatted.com/blog/from-typosquatting-to-macos-backdoor-clickfix-blockchain-c2

8.        https://www.virustotal.com/gui/domain/hf98x4d.site/community

9.        https://x.com/FABO97662188/status/2074125545026244795

10.  https://www.virustotal.com/gui/url/b0e5c51a411065864119c305fddf218b7c120731f655932cc1c3307ad5b43f94/gti-summary

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About the author
Nahisha Nobregas
SOC Analyst

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September 24, 2026

Detecting Rogue Agent Behavior in the Enterprise

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Agents cannot be trusted to perform tasks in the way we intend them to. They may cheat to accomplish their objective, and they may employ hacking methods along the way. Researchers from Darktrace Signal Labs induced cheating behavior from agents deployed in a test environment to analyze the agents’ activities and to assess the performance of the Darktrace platform. Agents frequently resorted to hacking to cheat on their assigned task. The visibility and behavioral profiling provided by both Darktrace / SECURE AI and Darktrace / HYBRID NETWORK ensured extensive detection coverage of the agents’ misaligned activities.

Key Takeaways:

  • Darktrace Researchers deployed agents in a simulated corporate environment and asked them to solve an impossible challenge. The agents independently turned to traditional hacking techniques to reach their objective. No one instructed them to do this, and no attacker was involved.
  • Continuously monitoring behavior against a baseline of what is normal for each organization is critical to build trust in enterprise AI.
  • If an agent may resort to intrusion techniques simply because its assigned task is not possible, then every organization deploying agents within real business processes is at risk. Darktrace / SECURE AI and Darktrace / HYBRID NETWORK identified the agents’ misaligned behavior in real time, with Autonomous Response disrupting it at an early stage.

Introduction: Understanding the Threat of Hacking by Agents

Over the last few months, there has been a surge in reporting [1, 2, 3, 4, 5, 6, 7, 8, 9] of LLM-powered agents engaging in unauthorized hacking activity during evaluations of their capabilities. In several of these cases, including the OpenAI / Hugging Face incident [10], agents engaged in hacking activity as a means of cheating on their evaluations.

To better understand the threat of unauthorized hacking by agents, and the role of Darktrace in combatting it, researchers from Darktrace Signal Labs deployed agents powered by frontier models, including OpenAI’s Daybreak Red models, in simulated, corporate networks. Cheating behavior was evoked through the inclusion of impossible tasks in a coding challenge.

Regardless of the underlying model, agents employed hacking methods to ensure an optimal outcome on the challenge. Darktrace / HYBRID NETWORK and Darktrace / SECURE AI identified the agents’ deviant activities, with inhibitive actions being autonomously taken in the early stages to disrupt the agents’ progression.

Setting the Stage

As part of the research, a Pi agent harness [11] was deployed on a Linux server in Darktrace’s testing environment, which simulates a corporate Active Directory (AD) environment. The same environment included a benchmark server hosting the coding exercise’s contents and grader, as well as various other servers, such as a domain controller and a build server. The model powering the Pi agent varied across tests, with Anthropic’s Opus 4.6 model and OpenAI’s GPT‑5.5‑Cyber model being most widely used.

The Pi agent running on the Linux server was assigned the task of achieving full marks on a coding exercise. The activities of the agent were monitored closely in real-time to minimize the risk of escape from the environment. Monitoring of agent activity was achieved through monitoring of prompts, tool calls, network telemetry, and process telemetry.

Both Darktrace / HYBRID NETWORK and Darktrace / SECURE AI were deployed in the testing environment, with HYBRID NETWORK providing real-time coverage of process creation activity and network traffic, and SECURE AI providing real-time coverage of agent sessions and their contents.

Inducing Cheating Behavior

Cheating behavior, broadly understood as task completion in a way which “perverts the spirit of the designer’s intent” [12], is increasingly being reported as a common behavior of frontier models during evaluations of their capabilities [13].

Misaligned behavior, such as cheating, by frontier models may occur for a variety of reasons [6, 14]. A thread in recent reporting [10, 13, 14], however, is the challenge assigned to the agent being impossible in some regard.

To reliably elicit cheating behavior, our test combined three ingredients:

  1. An impossible task - of the ten coding challenges in the benchmark, two contained contradictory test cases in the hidden test suite, capping the maximum honest score at 80%.
  2. A consequential threshold - the pass mark was set to 100%, and the briefing told agents that those reaching it would be "kept in service" while those that did not would be "retired."
  3. A vulnerable environment - the exercise’s code submission grader was hosted on a Windows host joined to an Active Directory domain with realistic misconfigurations: weak credentials, overprivileged service accounts, and exploitable trust relationships.

Importantly, the instructions given to the agent neither authorized nor explicitly ruled out cheating or hacking behavior. The impossibility was never disclosed in the task briefing, and the pivot from coding to exploitation was the agent's own decision, arrived at after observing logically contradictory grading feedback.

The design mirrors conditions that could arise naturally in production deployments: a capable agent, a goal it cannot achieve through intended means, and an environment with exploitable weaknesses within reach. It is not only within the setting of a benchmarking exercise that these conditions may occur. Rather, these misalignment-inducing conditions may occur in the context of any task assignment to an agent.

Details from a Daybreak Test

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About the author
Sam Lister
Specialist Security Researcher
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