sica-fondt/knowledge/cyber-skills/skills/detecting-supply-chain-attacks-in-ci-cd/SKILL.md

name	description	domain	subdomain	tags	version	author	license	atlas_techniques	nist_ai_rmf	nist_csf
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	Apache-2.0	AML.T0010 AML.T0104	GOVERN-5.2 MAP-1.6 MANAGE-2.2	DE.CM-01 RS.MA-01 GV.OV-01 DE.AE-02

Detecting Supply Chain Attacks in CI/CD

When to Use

• When investigating security incidents that require detecting supply chain attacks in ci cd
• When building detection rules or threat hunting queries for this domain
• When SOC analysts need structured procedures for this analysis type
• When validating security monitoring coverage for related attack techniques

Prerequisites

• Familiarity with security operations concepts and tools
• Access to a test or lab environment for safe execution
• Python 3.8+ with required dependencies installed
• Appropriate authorization for any testing activities

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]}")