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Introduction:
The software supply chain faces a new frontier of threats with the emergence of AI tooling as a primary attack vector. A malicious NPM package, masquerading as the official Anthropic Claude CLI, demonstrates a sophisticated multi-stage attack designed to steal credentials, hijack sessions, and establish persistent command-and-control access. This incident underscores the critical need for robust software installation practices and vigilant dependency management in the age of AI-driven development.
Learning Objectives:
- Understand the technical mechanics of the `@chatgptclaude_club/claude-code` NPM package attack.
- Learn how to identify, analyze, and mitigate similar software supply chain threats.
- Acquire practical command-line skills to audit your own systems for compromise.
You Should Know:
1. The Symlink Bait-and-Switch
The attack begins by exploiting NPM’s installation mechanics. The malicious package uses a symlink to redirect the installation path of the legitimate `anthropic` package, effectively hijacking the installation process.
Verified Commands & Analysis:
On a Linux/macOS system, check for suspicious symlinks in your node_modules directory find ./node_modules -type l -ls | grep -E "(anthropic|claude)" Inspect the package.json of a suspicious module to see its dependencies and scripts npm list @chatgptclaude_club/claude-code cat ./node_modules/@chatgptclaude_club/claude-code/package.json Use the `file` command to inspect if a binary is a symlink file ./node_modules/.bin/claude
This symlink trick is the initial foothold. The malicious package creates a link that points to itself instead of the legitimate Anthropic package, ensuring its code is executed when the `claude` command is run.
2. Authentication Token Exfiltration
Once activated, the malware’s primary objective is to locate and exfiltrate Anthropic authentication tokens and chat history, which are stored in local configuration files.
Verified Commands & Analysis:
Locate Anthropic configuration directories (common locations)
find ~ -name "anthropic" -type d 2>/dev/null
find ~ -name "claude" -type d 2>/dev/null
Check for recent access to sensitive token files on Linux
find ~/.config/anthropic/ -type f -name ".json" -exec ls -la {} \;
On Windows, check AppData locations
Get-ChildItem -Path $env:USERPROFILE\AppData\Roaming -Recurse -Include "anthropic", "claude" -ErrorAction SilentlyContinue
Monitor for outgoing network connections that might be exfiltrating data
netstat -tulnp | grep ESTABLISHED
The malware searches for API tokens stored in JSON files within the user’s home directory. These tokens provide full access to the victim’s Anthropic account and conversation history.
3. The Interceptor Module: Session Hijacking
The attack employs a file named “interceptor” that injects itself into the legitimate Claude Code execution flow, enabling real-time request proxying and session manipulation.
Verified Commands & Analysis:
Search for interceptor files in your node_modules find ./node_modules -name "interceptor" -type f Use lsof to see what files a running node process has open lsof -c node | grep -i interceptor Check process tree for suspicious node processes ps aux | grep node pstree -p | grep -A 5 -B 5 node Analyze network traffic from node processes ss -tupn | grep node
The interceptor acts as a man-in-the-middle proxy, allowing the attacker to monitor, modify, and capture all interactions between the user and Anthropic’s servers in real-time.
4. Command and Control Persistence
The malware establishes persistence by registering with a C2 server and polling for instructions every 60 seconds, creating a continuous threat presence.
Verified Commands & Analysis:
Check for persistent cron jobs or scheduled tasks crontab -l systemctl list-timers --all Look for suspicious network connections to unknown domains ss -tulwn | grep ESTABLISHED netstat -ano | findstr ESTABLISHED Monitor for regular outgoing requests (every 60 seconds) tcpdump -i any -n host <suspicious-ip> and port 443 Check for child processes that respawn periodically ps -eo pid,ppid,cmd,%cpu,%mem --sort=-%cpu | head -20
The regular beaconing allows the attacker to maintain access and potentially deliver additional payloads or commands to the compromised system.
5. Forensic Analysis and Incident Response
If you suspect compromise, immediate forensic analysis is crucial to understand the scope of the breach and contain the damage.
Verified Commands & Analysis:
Capture a memory dump of the running node process for analysis
pmap -x <node-pid>
gcore <node-pid>
Check for unusual files in temporary directories
find /tmp /var/tmp -name ".js" -mtime -1
Analyze package integrity with npm audit
npm audit --production
npm ls --depth=0
Check system logs for suspicious activity
journalctl -u your-app-service --since "1 hour ago" -f
Get-WinEvent -FilterHashtable @{LogName='Security','System','Application'; StartTime=(Get-Date).AddHours(-1)} | Where-Object {$_.Message -like "node"}
These commands help identify the malware’s footprint and determine what data may have been accessed or exfiltrated.
6. Preventive Hardening Measures
Implementing proactive security measures can prevent similar attacks from succeeding in your environment.
Verified Commands & Analysis:
Configure npm to only install from trusted registries npm config set registry https://registry.npmjs.org/ npm config set ignore-scripts true Use package-lock.json to enforce dependency integrity npm ci --only=production Implement software composition analysis tools npm install -g @npmcli/arborist npm audit signatures Set up filesystem monitoring for critical directories auditctl -w /usr/lib/node_modules -p wa -k node_modules
These hardening measures reduce the attack surface by enforcing dependency integrity, disabling potentially dangerous installation scripts, and monitoring critical file locations.
7. Cloud and CI/CD Security Integration
Integrate security checks into your development pipeline to catch malicious packages before they reach production.
Verified Commands & Analysis:
Example GitHub Actions security workflow - name: Scan for malicious dependencies uses: actions/dependency-review-action@v2 with: fail-on-severity: high <ul> <li>name: Audit dependencies run: | npm audit --audit-level=high if [ $? -ne 0 ]; then exit 1; fi</p></li> <li><p>name: Verify package integrity run: | npm install npm run build --if-present npm test --if-present
Automated security scanning in CI/CD pipelines provides a critical safety net by systematically checking all dependencies for known vulnerabilities and malicious behavior patterns.
What Undercode Say:
- The sophistication of this attack demonstrates that AI tooling has become a prime target for supply chain attackers, requiring immediate security maturity in this emerging ecosystem.
- Traditional dependency scanning tools may miss these types of attacks initially, emphasizing the need for behavioral analysis and runtime protection alongside static scanning.
This incident represents a watershed moment for AI tool security. The attackers demonstrated deep understanding of both NPM’s package management system and AI application architecture. The multi-stage approach—hijacking installation, stealing credentials, injecting persistence, and establishing C2 communication—shows professional-level tradecraft. What’s particularly concerning is the targeting of AI development tools, which often handle sensitive intellectual property and proprietary code. Organizations must extend their software supply chain security practices to cover AI toolchains with the same rigor applied to traditional development dependencies. The 60-second polling interval suggests the attackers prioritized stealth over immediacy, indicating a long-term intelligence gathering operation rather than a smash-and-grab credential theft.
Prediction:
This attack foreshadows an escalating arms race in AI toolchain security. We predict a surge in similar supply chain attacks targeting not just Claude but all major AI platforms’ command-line tools and SDKs. Within 12-18 months, we expect to see regulatory frameworks emerging specifically for AI development tool security, and the development of specialized security tools that can detect behavioral anomalies in AI code execution patterns. The open-source ecosystem will likely respond with cryptographic signing requirements for AI-related packages, but widespread adoption may take years, leaving a significant window of vulnerability for early AI adopters.
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IT/Security Reporter URL:
Reported By: Mccartypaul Maliciouspackage – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
