CI CD Ssdf Security

Context

CI/CD pipelines are high-value targets — they hold secrets, produce deployable artifacts, and run with elevated permissions. A misconfigured GitHub Actions workflow can leak credentials, allow unauthorized deployments, or execute attacker-controlled code with write access to the repository. This skill covers GitHub Actions permission hardening, pinned action SHAs, secret hygiene, OIDC federation, pull_request_target risks, release security, and alignment with NIST SSDF practices — including AI-specific considerations from SP 800-218A.

Best used for

GitHub Actions, Azure DevOps, release workflows, and compliance-sensitive repos. Use this skill when reviewing workflow files, pipeline configurations, deployment processes, and any CI/CD changes that affect build integrity or secret handling.

Primary references

• NIST SP 800-218 Secure Software Development Framework (SSDF): https://csrc.nist.gov/pubs/sp/800/218/final
• NIST SP 800-218A Generative AI and SSDF Practices: https://csrc.nist.gov/pubs/sp/800/218/a/final
• GitHub Actions Security Hardening: https://docs.github.com/en/actions/security-for-github-actions/security-guides/security-hardening-for-github-actions
• Microsoft Learn — Custom CodeQL Queries: https://learn.microsoft.com/en-us/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/customizing-your-advanced-setup-for-code-scanning

Patterns

GITHUB_TOKEN minimum permissions

Without a permissions: block, the token defaults to write-all. Every workflow must declare minimum permissions at the workflow level, granting additional scopes only at the job level where needed.

Least-privilege baseline (workflow level):

permissions: {}  # deny all at workflow level; grant per job only

Scope only what each job needs:

jobs:
  build:
    permissions:
      contents: read
  release:
    permissions:
      contents: write
      attestations: write
      id-token: write

Pinned action SHAs

Tags like @v4 are mutable — a compromised upstream can retag to malicious code. Pin to full commit SHAs with version comments. Use npx pin-github-action .github/workflows/*.yml to automate.

Secrets in workflows

• Always use ${{ secrets.NAME }} — never pass secrets through environment variable indirection.
• Never echo a secret. GitHub masks known secrets, but novel derivations may leak.
• Use ::add-mask:: for dynamically generated sensitive values. Note: masking only applies to log output after the line where it is set — any value logged before the mask command remains visible.
• Never pass secrets to untrusted third-party actions — audit source first.

pull_request_target risk

pull_request_target runs in the base repository context with write permissions and access to secrets — even for fork PRs. If the workflow checks out fork code, an attacker controls what runs with elevated privileges.

If pull_request_target is genuinely required (e.g., labeling), never check out or execute code from the PR head — only read metadata.

OIDC federation vs long-lived credentials

Long-lived cloud credentials stored as secrets do not expire, cannot be scoped to a single run, and are hard to rotate. Prefer OIDC federation (id-token: write) for Azure, AWS, and GCP — tokens are short-lived, audience-scoped, and tied to the specific workflow run.

Build reproducibility

Reproducible builds require deterministic dependency resolution:

• Commit packages.lock.json and restore with --locked-mode.
• Pin all tool versions (SDK, runtime, Node.js) in global.json or workflow setup-* actions.
• Avoid floating refs in action inputs (e.g., dotnet-version: 8.x — use 8.0.401).

Static analysis in CI

Enable CodeQL or GitHub Advanced Security. Requires security-events: write to upload SARIF results to the Security tab. Configure for all project languages; run on every PR and push to the default branch.

Release security

• Environment protection rules: require manual approval for production.
• Required reviewers: at least one reviewer before deployment.
• Artifact attestation: actions/attest-build-provenance for signed SLSA provenance (requires attestations: write and id-token: write permissions).
• Branch protection: require CI pass and PR reviews before merge to main; disallow direct pushes.

SSDF alignment

• PO.1 (Define security requirements): CI/CD pipelines should enforce security requirements automatically — static analysis, dependency review, secret scanning.
• PW.4 (Reuse existing, well-secured software): pin action versions, verify third-party actions before adoption, prefer official actions from actions/* and verified publishers.
• RV.1 (Identify vulnerabilities): integrate CodeQL, dependency review, and secret scanning into every PR.
• RV.2 (Assess, prioritize, and remediate): track and triage findings from CI security tools within defined SLAs.

AI-generated workflow considerations (SP 800-218A)

AI tools can generate or modify GitHub Actions workflows. Risks include:

• Suggesting pull_request_target without understanding the security implications.
• Omitting permissions: blocks (defaulting to overly broad access).
• Using mutable action tags instead of pinned SHAs.
• Introducing workflow steps that echo or expose secrets.
• Generating composite actions with unnecessary GITHUB_TOKEN usage.

Review action: treat all AI-generated workflow changes as untrusted input. Review line-by-line against this skill's patterns before merging.

Anti-Patterns

• No permissions: block in workflow (defaults to write-all).
• pull_request_target with actions/checkout referencing github.event.pull_request.head.sha.
• Secrets echoed to logs: echo ${{ secrets.TOKEN }} or env | grep.
• Mutable action tags (@v4, @main) instead of pinned SHAs.
• Long-lived cloud credentials as secrets instead of OIDC federation.
• No branch protection rules on main — direct pushes allowed.
• CodeQL or security scanning not enabled on repositories with deployed services.
• Missing environment: gates for production deployments.
• AI-generated workflow files merged without manual security review.

Examples

BAD — no permissions, mutable tags, secret echo, untrusted action:

on: push
jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: echo "Deploying with token ${{ secrets.DEPLOY_TOKEN }}"
      - uses: some-org/deploy-action@main
        with:
          token: ${{ secrets.DEPLOY_TOKEN }}

GOOD — minimal permissions, pinned SHAs, OIDC, environment gate:

permissions:
  contents: read

on:
  push:
    branches: [main]
jobs:
  deploy:
    runs-on: ubuntu-latest
    environment: production
    permissions:
      id-token: write
      contents: read
    steps:
      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
      - uses: azure/login@a457da9ea143d694b1b9c7c869ebb04ebe844ef5 # v2.3.0
        with:
          client-id: ${{ secrets.AZURE_CLIENT_ID }}
          tenant-id: ${{ secrets.AZURE_TENANT_ID }}
          subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
      - run: dotnet publish -c Release

BAD — pull_request_target checking out fork code:

on: pull_request_target
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683
        with:
          ref: ${{ github.event.pull_request.head.sha }}
      - run: dotnet test  # attacker-controlled code runs with write token

GOOD — pull_request for untrusted code:

on: pull_request
jobs:
  test:
    runs-on: ubuntu-latest
    permissions:
      contents: read
    steps:
      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
      - run: dotnet test

Review cues

• Every workflow file has an explicit permissions: block at the workflow or job level.
• Permissions follow least privilege — only the scopes the job actually needs.
• All uses: references are pinned to full commit SHAs with version comments.
• No pull_request_target workflows check out or execute PR head code.
• Secrets are accessed only via ${{ secrets.NAME }} — never echoed, printed, or passed to untrusted actions.
• Cloud authentication uses OIDC federation, not long-lived credential secrets.
• Production deployments use environment: with protection rules and required reviewers.
• CodeQL or equivalent static analysis runs on every PR and pushes to default branch.
• security-events: write is granted only to jobs that upload SARIF results.
• Branch protection requires CI pass and PR review before merge to main.
• AI-generated workflow changes have been manually reviewed against these patterns.
• Artifact attestation is configured for release artifacts.

Good looks like

• Every workflow declares permissions: contents: read at minimum, with additional scopes only at job level.
• All action references are pinned to immutable SHAs with descriptive version comments.
• Cloud auth uses OIDC federation — no long-lived credential secrets in the repository.
• pull_request_target is not used, or is used only for metadata operations (labeling) without checking out PR code.
• Secrets are never logged; ::add-mask:: is used for dynamically generated sensitive values.
• CodeQL runs on every PR; SARIF results are uploaded to GitHub Security tab.
• Production deployments require environment approval gates and reviewer sign-off.
• Build artifacts have signed provenance attestations.
• Branch protection is enforced: CI must pass, PRs required, no direct pushes to main.
• AI-generated workflow modifications are flagged for manual review in the PR process.

Common findings / likely remediations

Source: betaio/LLMTestProject — distributed by TomeVault.