How Darktrace Stopped Impersonation Attack on Board Member
Prevent impersonation attacks with Darktrace. Learn how AI identified & neutralized phishing attempts targeting high-profile individuals in a Gmail environment.
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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.
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06
Jul 2020
Email and other communication platforms rely on an assumption of trust that, given the increased reliance on communication platforms during the pandemic, has never been under greater scrutiny. Email’s very efficacy is dependent on the fact that employees trust the source of the requests and the information landing in their inbox every day. This is especially true when emails come from a known figure, such as a trusted supplier, adviser, partner, or executive, even one who isn’t a frequent correspondent.
It is for this reason that impersonation attacks are so dangerous, and why, given their success rates, they’re being used even more by cyber-criminals. Recently, Darktrace saw three related email attacks target high-profile individuals at a financial services organization, each sent from three separate email accounts impersonating the CEO, the CFO, and a board member.
It is not unusual for board members to email from addresses that are external to the company they serve on the board of. An investor might choose to use their firm’s account or their personal email. Many organizations have a bio of their board members and executives publicly available on their websites, including references to other activities they are involved with. This easily provides contextually relevant information to would-be attackers. For these reasons, impersonation attacks are both highly tailored and highly effective.
The phishing attacks that slipped the hook
This attack was detected in the Gmail environment of an Antigena Email customer. Over the course of a single week, three malicious emails bypassed the existing email security tools in place and attempted to solicit sensitive information from three senior staff members. Each was sent on a different day, at different times, from different addresses, but the ASN address revealed that all three seemed to originate from the same source. This suggests that the three emails were launched by the same attacker attempting to impersonate a few key users to gain access to company details.
Figure 1: An overview of the three anomalous emails identified within the same week
Looking into the most recent email, we see Antigena Email’s analysis of the threat. This email appears to be a targeted spoofing or solicitation attack imitating a board member and targeting a high-ranking individual in the finance department.
We can see from the 86% anomaly score that Antigena Email understood this email as being significantly unusual. The tags summarize the main findings: the email showed signs of solicitation, but interestingly did not appear to contain attachments or links. This is a common method that attackers use to bypass legacy tools that rely on access and deny lists, known attacks, rules, and signatures to spot and stop email attacks. With no malicious links for legacy tools to flag, these attacks easily slip past traditional security solutions.
Figure 2: The email tags and actions associated with one of the offending emails
Catching the subtle and stealthy with AI
This was clearly a well-thought-out attack. The varied senders, the inconsistency in the emails, and the gaps of time between the messages suggests that this was an attacker who was taking their time. Rather than relying on a ‘spray and pray’ approach – sending out thousands of emails in the hope that one or two users might engage – they spent time researching the organization, crafting highly-tailored, well-written messages in an attempt to gain a foothold.
The attacker presumably believed that this slow and stealthy strategy could have paid off with high rewards, targeting only high-profile individuals with the attacks. And without Darktrace’s self-learning email security technology actively analyzing every email in real time, this approach may have been successful.
Indeed, these well-researched, carefully-crafted emails would be nearly impossible for security teams to identify with legacy tools alone. Luckily, by deploying Cyber AI across email, which can learn the patterns of normal communication and behavior for every employee across a company, organizations can detect and prevent these kinds of stealthy attacks that are so often hidden in the noise of email communications.
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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.
CVE-2026-1731: How Darktrace Sees the BeyondTrust Exploitation Wave Unfolding
Note: Darktrace's Threat Research team is publishing now to help defenders. We will update continue updating this blog as our investigations unfold.
Background
On February 6, 2026, the Identity & Access Management solution BeyondTrust announced patches for a vulnerability, CVE-2026-1731, which enables unauthenticated remote code execution using specially crafted requests. This vulnerability affects BeyondTrust Remote Support (RS) and particular older versions of Privileged Remote Access (PRA) [1].
A Proof of Concept (PoC) exploit for this vulnerability was released publicly on February 10, and open-source intelligence (OSINT) reported exploitation attempts within 24 hours [2].
Previous intrusions against Beyond Trust technology have been cited as being affiliated with nation-state attacks, including a 2024 breach targeting the U.S. Treasury Department. This incident led to subsequent emergency directives from the Cybersecurity and Infrastructure Security Agency (CISA) and later showed attackers had chained previously unknown vulnerabilities to achieve their goals [3].
Additionally, there appears to be infrastructure overlap with React2Shell mass exploitation previously observed by Darktrace, with command-and-control (C2) domain avg.domaininfo[.]top seen in potential post-exploitation activity for BeyondTrust, as well as in a React2Shell exploitation case involving possible EtherRAT deployment.
Darktrace Detections
Darktrace’s Threat Research team has identified highly anomalous activity across several customers that may relate to exploitation of BeyondTrust since February 10, 2026. Observed activities include:
- Outbound connections and DNS requests for endpoints associated with Out-of-Band Application Security Testing; these services are commonly abused by threat actors for exploit validation. Associated Darktrace models include:
o Compromise / Possible Tunnelling to Bin Services
o Compromise / High Priority Crypto Currency Mining
And model alerts for:
o Compromise / Rare Domain Pointing to Internal IP
IT Defenders: As part of best practices, we highly recommend employing an automated containment solution in your environment. For Darktrace customers, please ensure that Autonomous Response is configured correctly. More guidance regarding this activity and suggested actions can be found in the Darktrace Customer Portal.
Appendices
Potential indicators of post-exploitation behavior:
AI/LLM-Generated Malware Used to Exploit React2Shell
Introduction
To observe adversary behavior in real time, Darktrace operates a global honeypot network known as “CloudyPots”, designed to capture malicious activity across a wide range of services, protocols, and cloud platforms. These honeypots provide valuable insights into the techniques, tools, and malware actively targeting internet‑facing infrastructure.
A recently observed intrusion against Darktrace’s Cloudypots environment revealed a fully AI‑generated malware sample exploiting CVE-2025-55182, also known as React2Shell. As AI‑assisted software development (“vibecoding”) becomes more widespread, attackers are increasingly leveraging large language models to rapidly produce functional tooling. This incident illustrates a broader shift: AI is now enabling even low-skill operators to generate effective exploitation frameworks at speed. This blog examines the attack chain, analyzes the AI-generated payload, and outlines what this evolution means for defenders.
Initial access
The intrusion was observed against the Darktrace Docker honeypot, which intentionally exposes the Docker daemon internet-facing with no authentication. This configuration allows any attacker to discover the daemon and create a container via the Docker API.
The attacker was observed spawning a container named “python-metrics-collector”, configured with a start up command that first installed prerequisite tools including curl, wget, and python 3.
Figure 1: Container spawned with the name ‘python-metrics-collector’.
Subsequently, it will download a list of required python packages from
hxxps://pastebin[.]com/raw/Cce6tjHM,
Finally it will download and run a python script from:
hxxps://smplu[.]link/dockerzero.
This link redirects to a GitHub Gist hosted by user “hackedyoulol”, who has since been banned from GitHub at time of writing.
Notably the script did not contain a docker spreader – unusual for Docker-focused malware – indicating that propagation was likely handled separately from a centralized spreader server.
Deployed components and execution chain
The downloaded Python payload was the central execution component for the intrusion. Obfuscation by design within the sample was reinforced between the exploitation script and any spreading mechanism. Understanding that docker malware samples typically include their own spreader logic, the omission suggests that the attacker maintained and executed a dedicated spreading tool remotely.
The script begins with a multi-line comment: """ Network Scanner with Exploitation Framework Educational/Research Purpose Only Docker-compatible: No external dependencies except requests """
This is very telling, as the overwhelming majority of samples analysed do not feature this level of commentary in files, as they are often designed to be intentionally difficult to understand to hinder analysis. Quick scripts written by human operators generally prioritize speed and functionality over clarity. LLMs on the other hand will document all code with comments very thoroughly by design, a pattern we see repeated throughout the sample. Further, AI will refuse to generate malware as part of its safeguards.
The presence of the phrase “Educational/Research Purpose Only” additionally suggests that the attacker likely jailbroke an AI model by framing the malicious request as educational.
When portions of the script were tested in AI‑detection software, the output further indicated that the code was likely generated by a large language model.
Figure 2: GPTZero AI-detection results indicating that the script was likely generated using an AI model.
The script is a well constructed React2Shell exploitation toolkit, which aims to gain remote code execution and deploy a XMRig (Monero) crypto miner. It uses an IP‑generation loop to identify potential targets and executes a crafted exploitation request containing:
A deliberately structured Next.js server component payload
A chunk designed to force an exception and reveal command output
A child process invocation to run arbitrary shell commands
def execute_rce_command(base_url, command, timeout=120): """ ACTUAL EXPLOIT METHOD - Next.js React Server Component RCE DO NOT MODIFY THIS FUNCTION Returns: (success, output) """ try: # Disable SSL warnings urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
res = requests.post(base_url, files=files, headers=headers, timeout=timeout, verify=False)
This function is initially invoked with ‘whoami’ to determine if the host is vulnerable, before using wget to download XMRig from its GitHub repository and invoking it with a configured mining pool and wallet address.
Many attackers do not realise that while Monero uses an opaque blockchain (so transactions cannot be traced and wallet balances cannot be viewed), mining pools such as supportxmr will publish statistics for each wallet address that are publicly available. This makes it trivial to track the success of the campaign and the earnings of the attacker.
Figure 3: The supportxmr mining pool overview for the attackers wallet address
Based on this information we can determine the attacker has made approx 0.015 XMR total since the beginning of this campaign, which as of writing is valued at £5. Per day, the attacker is generating 0.004 XMR, which is £1.33 as of writing. The worker count is 91, meaning that 91 hosts have been infected by this sample.
Conclusion
While the amount of money generated by the attacker in this case is relatively low, and cryptomining is far from a new technique, this campaign is proof that AI based LLMs have made cybercrime more accessible than ever. A single prompting session with a model was sufficient for this attacker to generate a functioning exploit framework and compromise more than ninety hosts, demonstrating that the operational value of AI for adversaries should not be underestimated.
CISOs and SOC leaders should treat this event as a preview of the near future. Threat actors can now generate custom malware on demand, modify exploits instantly, and automate every stage of compromise. Defenders must prioritize rapid patching, continuous attack surface monitoring, and behavioral detection approaches. AI‑generated malware is no longer theoretical — it is operational, scalable, and accessible to anyone.
Analyst commentary
It is worth noting that the downloaded script does not appear to include a Docker spreader, meaning the malware will not replicate to other victims from an infected host. This is uncommon for Docker malware, based on other samples analyzed by Darktrace researchers. This indicates that there is a separate script responsible for spreading, likely deployed by the attacker from a central spreader server. This theory is supported by the fact that the IP that initiated the connection, 49[.]36.33.11, is registered to a residential ISP in India. While it is possible the attacker is using a residential proxy server to cover their tracks, it is also plausible that they are running the spreading script from their home computer. However, this should not be taken as confirmed attribution.
Credit to Nathaniel Bill (Malware Research Engineer), Nathaniel Jones ( VP Threat Research | Field CISO AI Security)
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