TL;DR: Supply chain attacks targeting Python packages are increasing. This tutorial shows how to use Claude Code with the python-dependency-threat-scan skill to automatically detect malicious packages through static analysis, behavioral pattern recognition, and IOC matching. One prompt replaces manual code review and multiple security tools.

Claude code tell me my machine not affected while i eating my pizza

What is a Supply Chain Attack

Supply chain attacks compromise software dependencies to inject malicious code into applications that trust those packages. Once installed, these packages can exfiltrate credentials, establish backdoors, or pivot to production systems.

Recent Examples

Case: LiteLLM

Attackers hijacked the LiteLLM PyPI package and published malicious versions that silently executed on install, stealing credentials and installing backdoors.

Source: Future Search - LiteLLM PyPI Supply Chain Attack

Case: Telnyx

TeamPCP published malicious versions of the Telnyx Python SDK to PyPI that execute on import, hiding malware inside fake .wav audio files and stealing credentials across systems.

Source: The Hacker News - TeamPCP Pushes Malicious Telnyx

Why AI Helps Malicious Package Detection

Traditional dependency scanners like pip-audit and safety rely on CVE databases and signature-based detection. They miss zero-day threats, new obfuscation techniques, and behavioral patterns that don't match known signatures.

AI-assisted detection combines static analysis with reasoning:

  • Pattern recognition - Identifies suspicious behavioral patterns like network calls in setup.py, obfuscated strings, or credential harvesting code

  • Contextual analysis - Understands code intent beyond simple pattern matching

  • IOC correlation - Matches against known indicators of compromise from threat intelligence

  • Natural language queries - Ask "does this package make external network calls?" instead of writing custom regex

Result: 1 prompt replaces 20 manual steps

Claude Code flow

Setup

Step 1: Install the skill from plugin marketplace

Command: Send below command to Terminal

claude plugin marketplace add mfakbar127/agent-skills
claude plugin install python-dependency-threat-scan@mfakbar127-skills

Step 2: Verify installation

Command: Send below command to Terminal

claude skill

checking the skill list

Tools Reference

The python-dependency-threat-scan skill provides these capabilities:

Tool Category

Capability

Use Case

Package Discovery

List installed packages

Enumerate dependencies to scan

File Inspection

Read package source files

Analyze setup.py, init.py for malicious code

Static Analysis

Pattern detection

Find obfuscation, network calls, file system access

IOC Matching

Threat intelligence correlation

Match against known malicious indicators

Behavioral Analysis

Code intent reasoning

Understand what suspicious code does

Use Case: Detecting Malicious Telnyx Package

Goal

Scan system-wide Python installation and all projects under ~/Repo for the compromised telnyx package (versions 4.87.1 and 4.87.2).

Prompt

Prompt: Send below prompt to Claude Code

Use python-dependency-threat-scan skills

Context:
The telnyx package on PyPI was compromised, versions 4.87.1 and 4.87.2 contain credential-stealing malware.

Check both on system wide and all project under ~/Repo/

What Happens

  1. Claude Code invokes python-dependency-threat-scan skill

  2. Scans system-wide packages and discovers 100+ dependency files across ~/Repo (requirements.txt, pyproject.toml, uv.lock, etc.)

  3. Discovers 100+ dependency files in ~/Repo (requirements.txt, pyproject.toml, uv.lock, etc.)

  4. Generates comprehensive threat report with IOC matches and verification steps

Finding Report

🔒 PYTHON DEPENDENCY THREAT SCAN REPORT

### SUMMARY
Scan Mode: Targeted (IOC-based)
Target IOC Package: telnyx
Compromised Versions: 4.87.1, 4.87.2
Target Directory: ~/Repo + System-wide
Search Directories: 6 Python paths + 100+ dependency files
Risk Level: LOW ✅
Confidence: HIGH
Conclusion: NO COMPROMISE DETECTED - The malicious Telnyx package (versions 4.87.1/4.87.2)
was not found in your system or any projects.
.....

Finding reports generated by Claude Code

Follow-Up Prompts

Try these prompts to follow up the initial analysis:

Scan all dependencies:

Prompt: Send below prompt to Claude Code

Scan all installed Python packages and rank them by risk level based on suspicious patterns

Generate remediation script:

Prompt: Send below prompt to Claude Code

Generate a Python remediation script for the compromised telnyx package that:
- Uninstalls the malicious telnyx package (versions 4.87.1 or 4.87.2)
- Removes persistence mechanisms (cron jobs, backdoors)
- Installs the legitimate telnyx-sdk package
- Provides post-remediation checklist for credential rotation

Custom IOC Matching:

Prompt: Send below prompt to Claude Code

Check all packages against this list of IOCs:
- IP: 185.xxx.xx.xx
- Domain: evil-c2.com
- File path: /tmp/.backdoor

Security Considerations

Tool Poisoning Risk

The python-dependency-threat-scan skill executes in your local environment and reads package files. If a malicious package includes anti-analysis techniques, it could potentially interfere with the scan.

Mitigations:

  • Run scans in isolated virtual environments or containers

  • Use read-only snapshots when scanning suspicious packages

  • Don't execute code from flagged packages

False Positives

AI-assisted detection may flag legitimate packages that have unusual but benign behaviors. Examples include packages that download ML models on first run or system utilities that require elevated permissions.

Mitigations:

  • Review findings critically - AI provides reasoning, you make the decision

  • Cross-reference with official package documentation

  • Check package maintainer reputation and history on PyPI

What Could Be Better

Integrate with Other Tools

Combine python-dependency-threat-scan with pip-audit for CVE checks and safety for known vulnerabilities.

Integration with Vet

Vet by SafeDep provides additional protection against malicious open source packages.

Workflow Automation

Schedule security scanning across development lifecycle:

  • Scheduled weekly scans

  • Dependency update reviews - Auto-analyze pip list --outdated results before upgrading

Conclusion

Supply chain attacks targeting Python packages are a growing threat. Traditional tools catch known vulnerabilities but miss new attacks like typosquatting, credential harvesting, and persistence mechanisms.

AI-assisted detection with Claude Code makes it practical to scan dependencies continuously. The python-dependency-threat-scan skill combines static analysis, threat intelligence, and reasoning to catch malicious packages that signature-based tools miss.

The Telnyx case demonstrates how one prompt can detect obfuscated payloads, network exfiltration, and persistence mechanisms - replacing hours of manual reverse engineering.

Ready to apply AI to your Security Engineering?

Subscribe to Secengai Newsletter for weekly actionable content on AI for security engineers.

Further Reading

MCP Servers:

Skills & Agents:

Config Management:

How Trail of Bits Member Sets Up Claude Code for Security Research & Development

A Complete Claude Code Setup for Security Researchers

labs.secengai.com/p/how-trail-of-bits-member-sets-up-claude-code-for-security-research-development

Keep Reading

© 2026 SecEngAI.com - Applied AI For Security Engineering.
Report abusePrivacy policyTerms of use
beehiivPowered by beehiiv