Anthropic-Cybersecurity-Skills/skills/detecting-supply-chain-attacks-in-ci-cd/SKILL.md

name, description, domain, subdomain, tags, version, author, license

name	description	domain	subdomain	tags	version	author	license
detecting-supply-chain-attacks-in-ci-cd	Scans GitHub Actions workflows and CI/CD pipeline configurations for supply chain attack vectors including unpinned actions, script injection via expressions, dependency confusion, and secrets exposure. Uses PyGithub and YAML parsing for automated audit. Use when hardening CI/CD pipelines or investigating compromised build systems.	cybersecurity	security-operations	detecting supply chain attacks	1.0	mahipal	MIT

Detecting Supply Chain Attacks in CI/CD

Instructions

Scan CI/CD workflow files for supply chain risks by parsing GitHub Actions YAML, checking for unpinned dependencies, script injection vectors, and secrets exposure.

import yaml
from pathlib import Path

for wf in Path(".github/workflows").glob("*.yml"):
    with open(wf) as f:
        workflow = yaml.safe_load(f)
    for job_name, job in workflow.get("jobs", {}).items():
        for step in job.get("steps", []):
            uses = step.get("uses", "")
            if uses and "@" in uses and not uses.split("@")[1].startswith("sha"):
                print(f"Unpinned action: {uses} in {wf.name}")

Key supply chain risks:

1. Unpinned GitHub Actions (using @main instead of SHA)
2. Script injection via ${{ github.event }} expressions
3. Overly permissive GITHUB_TOKEN permissions
4. Third-party actions with write access to repo
5. Dependency confusion via public/private package name collision

Examples

# Check for script injection in run steps
for step in job.get("steps", []):
    run_cmd = step.get("run", "")
    if "${{" in run_cmd and "github.event" in run_cmd:
        print(f"Script injection risk: {run_cmd[:80]}")