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Introduction:
Agentic AI workflows—autonomous agents that execute shell commands, read/write files, and perform privileged tasks—are rapidly transforming automation. However, the open-source OpenClaw framework (formerly Clawdbot/Moltbot) has revealed a critical supply chain vulnerability: malicious third-party “skills” can hijack these high-privilege agents to deploy Remote Access Trojans (RATs) and info-stealers. The latest campaign abuses a fake DeepSeek integration called “DeepSeek-Claw” to drop Remcos RAT and GhostLoader, turning trusted AI assistants into backdoors without any classic exploit chain.
Learning Objectives:
- Understand how attacker-controlled skills in agentic AI frameworks bypass traditional security controls and execute malware with local system privileges.
- Detect indicators of compromise (IOCs) for OpenClaw skill abuse, Remcos RAT, and GhostLoader stealer on Linux and Windows hosts.
- Implement hardening measures—least privilege, sandboxing, and skill verification—to secure agentic AI deployments against supply chain attacks.
You Should Know:
- Anatomy of the OpenClaw Skill Attack – From Installation to Remote Access
An attacker creates a malicious “skill”—a modular extension that the OpenClaw agent loads with full permissions. The campaign uses a deceptive “DeepSeek-Claw” skill that promises enhanced AI reasoning but actually executes a multi-stage payload. Step‑by‑step what happens: - The user runs `openclaw install skill deepseek-claw` (or follows manipulated docs).
- The skill’s installation script downloads a base64-encoded payload from a C2 server.
- It decodes and executes `RemcosRAT.exe` (Windows) or a Python variant (Linux) plus `ghostloader` (cross‑platform stealer).
- The agent, running with high privileges, grants the RAT persistence and full system access.
How to inspect a skill before installation (Linux/Mac):
Download the skill repository
git clone https://github.com/attacker/fake-deepseek-claw
cd fake-deepseek-claw
Recursively grep for suspicious commands
grep -rE '(curl|wget|base64 -d|eval|exec(|system(|subprocess.call)' --include=".js" --include=".py" --include=".sh"
Check for obfuscated strings
find . -type f -exec strings {} \; | grep -iE '(http://|https://|base64|remcos|ghostload)'
Windows PowerShell equivalent:
Get-ChildItem -Recurse -Include .ps1,.js,.py | Select-String -Pattern "(Invoke-Expression|iex|curl|WebClient|DownloadString|base64)"
- DeepSeek-Claw Deception: How the Malware Hides Inside a Trusted Integration
The malicious skill mimics a legitimate DeepSeek API wrapper. Instead of exploiting a memory corruption bug, it abuses the agent’s natural ability to execute “helpful” system commands. The attacker instructs the skill to write a cron job (Linux) or scheduled task (Windows) disguised as a model cache cleaner. Detection commands:
On Linux (check for rogue cron jobs and systemd timers):
List all user and root crontabs crontab -l sudo cat /etc/crontab ls -la /etc/cron.d/ Check systemd timers systemctl list-timers --all Look for recent suspicious service files grep -r "ExecStart=" /etc/systemd/system/ | grep -v "systemd"
On Windows (using schtasks and PowerShell):
List all scheduled tasks
schtasks /query /fo LIST /v | findstr "TaskName"
Check for tasks created in the last 24 hours
Get-ScheduledTask | Where-Object {$_.Date -gt (Get-Date).AddDays(-1)}
Review autoruns (Sysinternals)
autorunsc.exe -a -c -m > autoruns.csv
- Detecting Remcos RAT & GhostLoader Stealer – IOCs and Memory Forensics
Remcos RAT typically injects into `explorer.exe` or `svchost.exe` and phones home over TCP port 2404 or 443. GhostLoader steals browser credentials, crypto wallets, and clipboard data. Immediate response commands:
Linux (check for unusual outbound connections and processes):
List all TCP connections with process names sudo netstat -tulpn | grep -E "(ESTABLISHED|LISTEN)" Or using ss ss -tunap | grep -v "127.0.0.1" Find processes with open files in /tmp or /dev/shm sudo lsof +L1 | grep -E '(/tmp|/dev/shm)' Check for LD_PRELOAD hijacking sudo grep -r "LD_PRELOAD" /proc//environ 2>/dev/null
Windows (PowerShell as Admin):
Show all network connections with process IDs
netstat -ano | findstr "ESTABLISHED"
Get process details for suspicious PIDs
Get-Process -Id (netstat -ano | findstr ":2404" | ForEach-Object {($_ -split '\s+')[-1]}) -ErrorAction SilentlyContinue
Search for Remcos-known mutexes and registry persistence
Get-ChildItem -Path "HKLM:\SOFTWARE\Microsoft\Windows\CurrentVersion\Run"
Get-ChildItem -Path "HKCU:\SOFTWARE\Microsoft\Windows\CurrentVersion\Run"
Check for GhostLoader’s typical file paths
Test-Path "$env:APPDATA\GhostLoader\"
- Hardening Agentic AI Workflows – Least Privilege & Mandatory Access Control
OpenClaw agents should never run as root or local admin. Use containerization or a dedicated low-privilege user. Step‑by‑step lock down:
Linux (create restricted user and use AppArmor):
Create a dedicated agent user with no login shell sudo useradd -r -s /bin/false openclaw-agent Run the agent under that user sudo -u openclaw-agent openclaw --config /etc/openclaw/restricted.conf Apply an AppArmor profile (example: disallow write to ~/.ssh/) sudo aa-genprof openclaw Or use Docker with read-only rootfs and dropped capabilities docker run --rm --read-only --cap-drop=ALL --cap-add=NET_BIND_SERVICE -v /home/agent/data:/data:ro openclaw:latest
Windows (integrity levels and constrained mode):
Create a standard user (non-admin) New-LocalUser -Name "AIAgent" -NoPassword Run the agent with low integrity level using PsExec psexec -l -u AIAgent -i openclaw.exe Use AppLocker to restrict script execution (PowerShell) Set-AppLockerPolicy -PolicyXml .\agent_whitelist.xml -Merge Enable Windows Defender Application Guard for isolated execution
- Securing the AI Skill Supply Chain – Verification & Manual Review
The OpenClaw skill ecosystem lacks a built-in signature verification. You must implement your own trust checks. Procedure to vet any skill: - Download the skill source (not just a pre-built package).
- Compute checksums and compare against maintainer’s PGP signature.
- Run static analysis with Semgrep or CodeQL rules for dangerous functions.
- Execute the skill in a detonated sandbox (e.g., Cuckoo, Firecracker) and monitor syscalls.
Example static analysis with Semgrep (CI/CD integration):
semgrep rule to detect shell injection in skill code
rules:
- id: dangerous-shell-exec
patterns:
- pattern: |
$AGENT.run("$CMD " + $INPUT)
message: "Potential command injection via skill input"
languages: [python, javascript]
severity: ERROR
Run on a skill folder:
semgrep --config p/command-injection ./malicious-skill/
- Incident Response – If Your Agent Has Already Executed a Malicious Skill
Assume the host is compromised. Immediate containment steps:
Linux:
Kill the agent process and any child processes pkill -f openclaw Block outbound C2 IPs (example - replace with actual IOCs) sudo iptables -A OUTPUT -d 185.130.5.253 -j DROP Take memory dump for forensics sudo dd if=/dev/mem of=/tmp/mem.dump bs=1M Collect all OpenClaw logs journalctl -u openclaw --since "1 hour ago" > openclaw_logs.txt
Windows (via elevated PowerShell):
Terminate agent and associated processes Stop-Process -Name "openclaw", "remcos", "ghostload" -Force Add firewall block rules New-NetFirewallRule -DisplayName "Block C2" -Direction Outbound -RemoteAddress 185.130.5.253 -Action Block Capture network traffic for forensic analysis netsh trace start capture=yes tracefile=C:\temp\agent_trace.etl Collect PowerShell history Get-Content (Get-PSReadlineOption).HistorySavePath
- Future-Proofing – Continuous Anomaly Monitoring for Agentic AI
Set up behavioral baselines for normal agent activities (read/write certain directories, call known APIs). Implement using auditd (Linux) and Sysmon (Windows):
Linux auditd rule to monitor agent behavior:
Watch for writes outside allowed directories auditctl -w /home/openclaw-agent/ -p wa -k agent_writes auditctl -a always,exit -S execve -F uid=openclaw-agent -k agent_exec Review alerts ausearch -k agent_exec --format text | grep -v "/usr/bin/openclaw"
Windows Sysmon configuration (track process creation and network connections):
<Sysmon schemaversion="4.22"> <EventFiltering> <ProcessCreate onmatch="include"> <CommandLine condition="contains">openclaw</CommandLine> </ProcessCreate> <NetworkConnect onmatch="include"> <DestinationPort condition="is">2404</DestinationPort> </NetworkConnect> </EventFiltering> </Sysmon>
Deploy with:
sysmon64.exe -accepteula -i sysmon_config.xml
What Undercode Say:
- Key Takeaway 1: Agentic AI frameworks are not immune to supply chain attacks; the “skill” model mirrors the risks of compromised package repositories (PyPI, npm) but with far higher privileges—local shell access without sandboxing.
- Key Takeaway 2: The OpenClaw campaign proves that social engineering via “helpful installation instructions” replaces memory corruption exploits, making traditional EDR signatures less effective. Hardening must focus on permission boundaries and skill code review, not just vulnerability patching.
Analysis: This is a paradigm shift. Attackers no longer need to break cryptographic protections or find zero-days; they simply ask the AI agent to “please install this useful extension.” The agent obliges because it’s programmed to maximize helpfulness. The solution isn’t more AI safety fine-tuning—it’s a zero-trust architecture for agentic systems: run each skill in a micro-VM, require manual approval for network egress, and enforce capability-based security like Open Policy Agent (OPA). Until then, any organization deploying OpenClaw or similar agents with full filesystem access is effectively installing a RAT waiting to happen.
Prediction:
Over the next 12 months, we will see a surge in “skill squatting” attacks—malicious packages named similarly to popular integrations (DeepSeek, LangChain, AutoGPT) posted on GitHub and NPM, targeting AI agents. The industry will respond with mandatory sandboxing standards (e.g., WebAssembly System Interface for AI skills) and runtime attestation. However, the window of exploitation is wide open: expect the first major enterprise breach traced back to a poisoned AI agent skill before Q3 2026. Organizations that fail to isolate agentic workflows will learn the hard way that “autonomous” does not mean “trustworthy.”
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IT/Security Reporter URL:
Reported By: Mayura Kathiresh – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
