Use when attacking or auditing a CI/CD pipeline or software supply chain — pwn requests, poisoned pipeline execution, compromised/mutable-tag actions, dependency confusion, registry worms, runner backdoors, OIDC trust abuse, SLSA/provenance
npx skills add https://github.com/hypnguyen1209/offensive-claude --skill cicd-supply-chain
pull_request_target / workflow_run "pwn requests" and Poisoned Pipeline Execution (PPE)| Technique | ATT&CK | CWE | Reference | Script |
|-----------|--------|-----|-----------|--------|
| Pwn request (pull_request_target checkout of fork head) | T1195.001 | CWE-269 | references/pipeline-poisoning.md | scripts/workflow_auditor.py |
| Script injection (${{ github.event.* }} into run:) | T1059 | CWE-94 | references/pipeline-poisoning.md | scripts/workflow_auditor.py |
| Direct / Indirect PPE (workflow or build-file modification) | T1195.001 | CWE-913 | references/pipeline-poisoning.md | scripts/workflow_auditor.py |
| GitLab .gitlab-ci.yml poisoning / pipeline-as-user (CVE-2024-6678) | T1648 | CWE-863 | references/pipeline-poisoning.md | scripts/workflow_auditor.py |
| Compromised Action via mutable tag (CVE-2025-30066 tj-actions) | T1195.001 | CWE-494 | references/action-dependency-compromise.md | scripts/malicious_action_scanner.py |
| Transitive Action compromise (CVE-2025-30154 reviewdog) | T1195.001 | CWE-1357 | references/action-dependency-compromise.md | scripts/malicious_action_scanner.py |
| Actions cache poisoning (cross-workflow escalation) | T1525 | CWE-349 | references/action-dependency-compromise.md | scripts/malicious_action_scanner.py |
| Dependency confusion (internal name on public registry) | T1195.002 | CWE-427 | references/package-registry-attacks.md | scripts/dependency_confusion.py |
| Typo / slopsquatting + malicious install hook | T1195.002 | CWE-829 | references/package-registry-attacks.md | scripts/dependency_confusion.py |
| Self-replicating registry worm (Shai-Hulud npm) | T1195.002 | CWE-829 | references/package-registry-attacks.md | scripts/malicious_action_scanner.py |
| Self-hosted / non-ephemeral runner abuse & backdoor | T1199 | CWE-668 | references/runner-attacks.md | scripts/runner_recon.sh |
| Jenkins Script Console RCE (/script, CVE-2024-23897) | T1648 | CWE-306 | references/runner-attacks.md | scripts/runner_recon.sh |
| CI secret exfiltration (toJSON(secrets), GhostAction) | T1552.004 | CWE-522 | references/secrets-oidc-abuse.md | scripts/oidc_trust_auditor.py |
| OIDC trust-policy abuse (missing/* sub, wrong org wildcard) | T1078.004 | CWE-1390 | references/secrets-oidc-abuse.md | scripts/oidc_trust_auditor.py |
| Build provenance / signing gate validation (defense) | T1195 | CWE-347 | references/build-integrity-defense.md | scripts/provenance_verify.sh |
# 0. Recon: enumerate workflows, triggers, used actions across an org (read-only token)
gh repo list ORG --limit 1000 --json nameWithOwner -q '.[].nameWithOwner' > repos.txt
python3 scripts/workflow_auditor.py --repos repos.txt --token "$GH_TOKEN" --out findings.json
# 1. Static audit any cloned repo for pwn-requests + injection sinks (offline, no token)
git clone https://github.com/ORG/REPO && python3 scripts/workflow_auditor.py --path REPO
# 2. Flag risky/mutable third-party Action refs (unpinned tags = supply-chain exposure)
python3 scripts/malicious_action_scanner.py --path REPO --check-pins --check-known-bad
# 3. Dependency confusion: find internal names not registered on public registries
python3 scripts/dependency_confusion.py --manifest REPO/package.json --registry npm
python3 scripts/dependency_confusion.py --manifest REPO/requirements.txt --registry pypi
# 4. OIDC abuse: audit AWS IAM trust policies tied to GitHub's OIDC provider
python3 scripts/oidc_trust_auditor.py --provider github --cloud aws --profile target
# 5. Runner recon (run ON a compromised self-hosted runner during an engagement)
bash scripts/runner_recon.sh
# 6. Defensive validation: verify SLSA provenance + cosign signature before promote
bash scripts/provenance_verify.sh --image ghcr.io/org/app:tag --repo org/app
Recommended OSS tooling: gato-x (offensive GHA enumeration/PPE), zizmor/octoscan/poutine/raven
(static workflow analysis), step-security/harden-runner (egress control), cosign+slsa-verifier (gates).
| Technique | Telemetry / IOC | Detection (Sigma/EDR) | OPSEC note |
|-----------|-----------------|------------------------|------------|
| Pwn request / PPE | Fork PR triggering privileged run; new .github/workflows/* in PR; outbound to non-allowlisted host | GH audit log workflows; Sigma on runner egress to new domains; zizmor in CI | Payload runs in build log; public-repo logs are world-readable — assume detection |
| Compromised Action / mutable tag | Action ref resolves to new SHA; tag force-push event; base64 in action source | Diff resolved SHA vs lockfile; alert on tag re-point in audit log | Tag re-point is logged org-side; SHA-pin victims are immune |
| Cache poisoning | Cache key written by read-only/low-priv job, restored by release job | Monitor actions/cache save/restore key ownership; provenance mismatch | Survives across workflows; harder to attribute than direct edit |
| Dependency confusion | Install-time outbound from build host; package version anomaly (very high ver) | EDR proc-tree npm/pip -> curl/node -e; registry telemetry | Higher public version wins resolver; noisy if scoped registries enforced |
| Registry worm (Shai-Hulud) | bundle.js postinstall; trufflehog filesystem /; public Shai-Hulud repo; shai-hulud-workflow.yml | EDR: npm child spawns trufflehog/git push; Sigma on workflow file creation | Self-propagating = loud and fast; burns the maintainer token |
| Runner backdoor | Rogue runner registration; persistent proc on host; outbound only to github.com | Runner-host EDR; GH audit self_hosted_runner events; one-job-per-runner | Traffic blends with github.com; non-ephemeral = persistence |
| Secret exfil / toJSON(secrets) | ${{ toJSON(secrets) }} in run step; POST of base64 to webhook | Static scan for toJSON(secrets); egress allowlist | GitHub masks *** in logs — encoding evades the mask |
| OIDC trust abuse | AssumeRoleWithWebIdentity; CloudTrail userName = repo:org/repo:ref | CloudTrail filter on federated principals; IAM Access Analyzer external findings | Short-lived creds, attributed to repo in logs; AWS blocks new bad policies (Jun 2025) |
toJSON(secrets), and AWS/GCP/Azure OIDC trust-policy misconfigurations.Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take hypnguyen1209/cicd-supply-chain from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.