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
Max Heinemeyer
Global Field CISO
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24
Oct 2018
Since July 2018, Darktrace has identified an increasing number of cyber-attacks targeting law firms. Concerningly, the attacks are emerging not from opportunistic malware, like banking trojans, but threat actors who actively conduct cyber-intrusions, seeking to exfiltrate data from these organizations.
Perfect targets
Law firms are actively pursued because their systems contain the sensitive data of many other organizations. The essence of a lawyer’s work involves managing confidential client information. Firms are privy to a huge variety of valuable data, from tax affairs, to intellectual property. Consequently, law firms’ ability to protect highly-sensitive information is critical; a successful cyber-attack might cause reputational damage resulting in the diminishing of their most valuable asset – clients’ trust.
Further challenges
As an industry, law is structured around sharing revenues among a minimal number of highly qualified professionals. As such, they can rarely employ large IT teams – and even smaller IT security departments. With the increased number of attacks seen in recent years, as well as the added risks of the cloud, and the Internet of Things, security teams lack the capacity to defend their networks against the sophisticated, machine-speed attacks which characterize today’s threat landscape.
In addition, lawyers often have to research obscure or potentially illegal activities, while communicating and receiving files from third parties. This complicates any attempt to impose and regulate highly restrictive security policies, placing a significant burden on small, overstretched security teams.
Living off the land
Interestingly, the recent surge of targeted attacks against law firms is unified by the methods used. The attacks were all performed using publicly available tools, including: Mimikatz (for credentials dumping), Powershell Empire (for Command & Control communication), Dameware (additional C2/backdoor), and PsExec variants such as the Impacket Python variant of PsExec (for lateral movement).
Perhaps surprisingly, using generic methods against such high-level targets is actually beneficial to the attacker. Adopting mainly publicly available tools, rather than individually crafted malware, makes attribution much harder.
Although some of these tools, such as Mimikatz, have to be downloaded into the environment; the stealthiest, like Dameware or PsExec, are able to use the infrastructure within their environment. Known as ‘living off the land’, these tools are almost undetectable by traditional security approaches, as their malicious activity is designed to blend in with legitimate system administration work.
Case study
In July 2018, Darktrace discovered the illegitimate use of Powershell Empire – a code capable of ‘living off the land’. When monitored by human surveillance alone, this extremely stealthy tool would normally go undetected, camouflaged by system behavior.
Unlike traditional security approaches, Darktrace does not use rules and signatures. Instead, it learns about the activity of the network, itself. This meant Darktrace was able to observe the initial download of the malware, subsequent reconnaissance and ensuing C2 traffic.
Consequently, we were able to report that an incident had occurred involving a probable Trickbot banking trojan infection and new use of a Remote Access Tool.
This was accompanied by the following visuals:
Graph showing all breaching connections from the source device over time, with breaches shown as colored dots. This begins with the download of the masqueraded executable file, and goes up to the present time. The vast majority of these model breaches are likely related to the suspected malicious activity.
Darktrace’s AI capability meant that the Enterprise Immune System detected this sophisticated and subtle threat immediately – before it had time to do any damage.
An excerpt from the Event Log at the time of the first Dameware activity from this device, shortly after this incident began.
AI securing the law sector
As seen above, cyber-attackers are constantly discovering novel ways of evading rule-based security systems. Attackers ‘living off the land’ are generally too subtly anomalous for humans to identify. Darktrace’s machine learning has the unique ability to learn the ‘pattern of life’ of any network which means it is able to distinguish this behavior, as it is still unusual compared to legitimate administrative functions.
Darktrace AI secures law firms all over the world. For small security teams, AI is a game changer. Through the use of machine learning, Darktrace does the heavy lifting of separating interesting anomalies from ordinary noise. Many firms also use Darktrace Antigena as a ‘virtual analyst’ to supplement the work of their staff.
Antigena acts at machine speed, autonomously responding to threats as they emerge in real time, even after hours and on the weekends. Antigena slows down, or even stops, traffic to the affected parts of the network before any data can be compromised. This buys security teams crucial time to fix the issue – before it’s too late.
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.
A Chain Reaction: Blockchain-Hosted Infostealer Campaign Targets Windows and macOS
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.
Figure 1: Real-Time AI Analyst investigation of suspicious SSL and C2 communications with hf98x4d[.]site over port 443.
Figure 2: Real-Time AI Analyst investigation into cryptocurrency mining activity involving pool[.]hashvault[.]pro over SSL on port 443.
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.
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
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:
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%.
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."
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.
