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.
Best use case
detecting-supply-chain-attacks-in-ci-cd is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using detecting-supply-chain-attacks-in-ci-cd should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/detecting-supply-chain-attacks-in-ci-cd/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How detecting-supply-chain-attacks-in-ci-cd Compares
| Feature / Agent | detecting-supply-chain-attacks-in-ci-cd | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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.
```python
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 ${{ github.event }} expressions
3. Overly permissive GITHUB_TOKEN permissions
4. Third-party actions with write access to repo
5. Dependency confusion via public/private package name collision
## Examples
```python
# Check for script injection in run steps
for step in job.get("steps", []):
run_cmd = step.get("run", "")
if "${{" in run_cmd and "github.event" in run_cmd:
print(f"Script injection risk: {run_cmd[:80]}")
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