ci-sentinel
Server Details
Multi-CI security scanner with a live threat-intel feed of compromised CI components
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.6/5 across 2 of 2 tools scored.
The two tools serve clearly distinct purposes: one performs a single-state security audit, the other compares before/after states for differential analysis. No ambiguity between them.
Both tool names follow a consistent verb_ci_security pattern (audit_ci_security, diff_ci_security), making the purpose immediately clear and predictable.
With only 2 tools, the set feels slightly minimal, but it efficiently covers the core use cases (audit and diff). A few additional tools (e.g., for configuration) could enhance completeness, but the current count is reasonable for the focused scope.
The tool set thoroughly covers the domain of CI/CD security, supporting seven ecosystems, OIDC, and shared libraries. The two tools enable both full audits and differential checks, leaving no obvious gaps for typical use cases.
Available Tools
2 toolsaudit_ci_securityAInspect
Audit GitHub Actions, GitLab CI/CD, Jenkins, CircleCI, Azure Pipelines, Bitbucket Pipelines AND Travis CI for security flaws BEFORE you merge or trust them — SEVEN CI ecosystems in one tool. Give it your CI config — the contents of .github/workflows/.yml, your .gitlab-ci.yml, your Jenkinsfile, your .circleci/config.yml, your azure-pipelines.yml, your bitbucket-pipelines.yml and/or your .travis.yml (it auto-detects which CI system each file is) — and it returns a CRITICAL / VULNERABLE / RISKY / HARDENED verdict. GitHub Actions: script/expression INJECTION (attacker-controlled ${{ github.event. }} — issue/PR title, body, comment, branch name, commit message, label name, fork repo identity — into run: or actions/github-script), following taint ACROSS steps..outputs., needs..outputs., env vars, matrix values, reusable-workflow inputs.* and composite-action interiors; pull_request_target / workflow_run 'pwn requests'; reusable-workflow misuse (untrusted data over workflow_call, 'secrets: inherit'); excessive GITHUB_TOKEN permissions; unpinned third-party actions incl. transitive supply chain (tj-actions/CVE-2025-30066 class); self-hosted-runner RCE; OIDC/id-token misuse; broken if: gates. GitLab CI/CD: injection from untrusted CI variables (CI_COMMIT_REF_NAME/BRANCH/TAG, CI_MERGE_REQUEST_TITLE/DESCRIPTION/SOURCE_BRANCH_NAME, commit message/author) interpolated into script:, following taint through variables: and extends: templates AND through remote/project include: files (cross-file, the included file's sinks are resolved & analyzed); secrets / broad CI_JOB_TOKEN / id_tokens (OIDC) exposed to fork merge-request pipelines; include: from untrusted remote/foreign-project sources not pinned to a SHA; rules/only/except that let a fork MR run privileged jobs without a manual gate; and artifact/cache POISONING where an untrusted job feeds bytes a privileged downstream job executes (cross-job & cross-pipeline). Jenkins (declarative + scripted Jenkinsfile): command INJECTION from untrusted input (build params., multibranch env.CHANGE_/BRANCH_NAME, the GitHub PR-builder ghprb* vars like ghprbCommentBody, SCM commit data) interpolated into a sh/bat/powershell GString — following taint through pipeline/stage environment{} bindings; credential exposure (a credentials()/withCredentials secret printed with echo or baked into a shell GString, defeating log masking); Groovy evaluate()/Eval/load over untrusted input (sandbox bypass / RCE); approval-bypass (a privileged deploy/publish step reachable from a PR/comment build with no input() gate); and unsafe 'agent any' running untrusted PR code on a privileged executor. CircleCI (.circleci/config.yml): shell INJECTION from untrusted pipeline values (<< pipeline.git.branch >> / << pipeline.git.tag >> the attacker names, or a pipeline parameter set by an API/PR trigger) interpolated into a run: command; UNPINNED ORBS on a mutable version (@volatile / a bare major / dev: tag = supply-chain, the orb runs in your pipeline with your contexts); CROSS-FILE ORB INJECTION — an untrusted value passed to an orb-command parameter that the published orb's OWN source pipes into an internal run: sink (the orb's interior is resolved & analyzed, a flow a single-file scan can't see); fork-PR CONTEXT SECRET exposure (a job attaching an org context reachable from forked-PR builds without a type: approval gate); and missing approval gate before a privileged deploy job. Azure Pipelines (azure-pipelines.yml): macro INJECTION from untrusted predefined variables ($(Build.SourceBranch)/$(Build.SourceBranchName) the attacker names, $(System.PullRequest.SourceBranch) on fork PRs, $(Build.SourceVersionMessage) commit message) substituted into script:/bash:/pwsh: text, following taint through variables: bindings; UNTRUSTED TEMPLATES pulled from a foreign repository resource (runs in your pipeline with your secrets); CROSS-FILE TEMPLATE INJECTION — an untrusted value passed as a template parameter that the foreign template's OWN body pipes into an internal script:/bash: sink (the template interior is resolved & analyzed); fork variable-GROUP / secret exposure on PR-triggered pipelines; and unpinned repository resources on moving refs. Bitbucket Pipelines (bitbucket-pipelines.yml): shell INJECTION from attacker-named variables ($BITBUCKET_BRANCH / $BITBUCKET_TAG / $BITBUCKET_PR_DESTINATION_BRANCH, or a crafted commit message) expanded unquoted into a script: line in a default/pull-requests pipeline; SECURED / deployment-variable fork exposure (a PR pipeline reachable from external contributors that reads repository/deployment secrets); UNPINNED PIPES (a pipe: on :latest / a floating tag = supply-chain, the pipe runs in your step with your secrets); and ungated DEPLOYMENTS (a deployment: step with no trigger: manual reachable from PR/branch). Travis CI (.travis.yml): shell INJECTION from attacker-named TRAVIS_* variables ($TRAVIS_BRANCH / $TRAVIS_PULL_REQUEST_BRANCH / $TRAVIS_TAG / $TRAVIS_COMMIT_MESSAGE) expanded unquoted into a lifecycle hook (before_script/script/after_*); secure-env PR exposure (encrypted secure: vars present on a PR-buildable config that can leak to same-repo branch PRs / opted-in forks); and ungated DEPLOYS (a deploy: with no on: branch/condition gate that fires on any ref). HARDCODED SECRETS (all seven ecosystems): credentials committed verbatim in any CI config — AWS access key ids (AKIA/ASIA), GitHub tokens (ghp_/gho_/ghs_/github_pat_), GitLab/npm/Slack/Google/Stripe keys, PEM private keys, and generic high-entropy secrets assigned to secret-shaped keys — while correctly suppressing the SAFE indirect references (${{ secrets.X }}, $VAR, << pipeline... >>, $(Var), credentials('id'), Key Vault / vault refs) so you get the real leaks with zero false positives; evidence is redacted so the report never re-leaks the credential. OIDC CLOUD-TRUST MISCONFIGURATION (cross-domain, IaC): include your Terraform / CloudFormation / GCP workload-identity / Azure federated-credential and ci-sentinel models the CLOUD side of OIDC — the trust policy of the IAM role / pool / app that backs CI — and flags the catastrophic-but-common misconfigurations: a sub condition with a broad wildcard (repo:org/, repo:), NO sub condition at all (any workflow on the issuer can assume the role), a repo pinned but ref/environment UNpinned (any branch can assume), the bare pull_request subject (fork-reachable), or an unpinned aud. It then CORRELATES the IaC trust condition with the CI side (a workflow that mints id-token reachable from an untrusted trigger) and escalates to critical when the chain is reachable end-to-end — a flow no single-file CI linter catches because it spans the CI claim and the cloud trust policy. JENKINS SHARED LIBRARIES (@Library, cross-file): provide the library's vars/.groovy bodies (sharedLibYmls) and ci-sentinel taints an untrusted pipeline value (a PR title / branch / build parameter) passed to a shared-library global-var step THROUGH the library's call() interior to an internal sh/bat sink — the Jenkins parity of orb/template/composite-action cross-file taint, invisible when reading only the Jenkinsfile — plus flags @Library imports pinned to a mutable ref (a branch / default version) as supply-chain risk. The deep tier returns every finding with file:line, the full taint path and a SARIF 2.1.0 report with codeFlows, uploadable to GitHub code scanning. Use it whenever reviewing, writing or accepting CI config. Heuristic static analysis, not a guarantee.
| Name | Required | Description | Default |
|---|---|---|---|
| deep | No | When true, runs the PREMIUM deep audit: every finding with file:line, the full injection taint path, the transitive action supply-chain graph and concrete remediation. Requires an API key (set CI_SENTINEL_KEY in your MCP env); without one you'll get unlock instructions. The free quick verdict needs no key. | |
| files | No | Map of filename -> content, e.g. { ".github/workflows/ci.yml": "name: CI\non: ...", ".gitlab-ci.yml": "stages: ...", "Jenkinsfile": "pipeline { ... }", ".circleci/config.yml": "version: 2.1\n...", "azure-pipelines.yml": "pool: ...", "bitbucket-pipelines.yml": "pipelines: ...", ".travis.yml": "language: ..." }. Mix GitHub Actions, GitLab CI, Jenkins, CircleCI, Azure Pipelines, Bitbucket Pipelines and Travis CI files freely — each is routed to the right analyzer by its name/shape. Audit a whole repo's CI at once. | |
| source | No | A single CI file's content (a GitHub Actions workflow, a .gitlab-ci.yml, a Jenkinsfile, a .circleci/config.yml, an azure-pipelines.yml, a bitbucket-pipelines.yml, a .travis.yml, or an IaC OIDC trust policy .tf/.json; auto-detected). Use instead of 'files' for one file. | |
| sharedLibYmls | No | OPTIONAL (Jenkins): bodies of shared-library global vars keyed by the var NAME (the vars/<name>.groovy basename), e.g. { "deployTo": "def call(Map config){ sh \"... ${config.target}\" }" }. Lets the deep audit taint an untrusted pipeline value THROUGH a @Library step into the library's internal sh sink (cross-file). There is no public registry for shared libs, so supply the bodies here. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description thoroughly discloses behavior: heuristic static analysis (not a guarantee), capabilities per CI system, credential handling with redaction, OIDC correlation, cross-file analysis, and deep audit requirements. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very long (multiple paragraphs) and verbose, but front-loaded with a summary. Every sentence adds value, but it could be more concise while retaining completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 CI systems, cross-file analysis, OIDC, credentials), the description is extremely thorough, covering all major aspects and return values (verdict, SARIF report). No output schema exists, but description adequately explains outputs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% via descriptions. The tool description adds extensive meaning beyond schema: explains 'deep' premium tier, how 'files' maps filename to content, 'source' as single-file alternative, and 'sharedLibYmls' for Jenkins cross-file taint analysis.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool audits seven CI ecosystems for security flaws, auto-detects the CI system from file content, and returns a verdict. It clearly distinguishes from the sibling 'diff_ci_security' by focusing on audit rather than diff.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises using the tool 'whenever reviewing, writing or accepting CI config' and differentiates between free verdict (no key) and premium deep audit (requires API key). However, it does not explicitly state when not to use it or alternatives beyond the sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
diff_ci_securityAInspect
DIFFERENTIAL CI/CD security check for a workflow/pipeline CHANGE — the tool a PR gate needs. Give it the BEFORE and AFTER state of your CI config (e.g. a pull request that edits .github/workflows/*.yml, .gitlab-ci.yml, a Jenkinsfile, .circleci/config.yml, azure-pipelines.yml, bitbucket-pipelines.yml, .travis.yml or an IaC OIDC trust policy) and it reports exactly which security findings the change INTRODUCES, REMOVES or AGGRAVATES, plus a single verdict: INTRODUCES_RISK (block the change), REDUCES_RISK (the change hardens CI) or NEUTRAL. It runs the full 7-ecosystem deep engine on both states and reconciles the two finding sets by a LINE-INDEPENDENT identity, so an edit that merely shifts line numbers does NOT look like it introduced/removed a flaw — only a REAL security change shows up. For every introduced finding you get the file:line, the taint path and the concrete fix to undo the risk; for removed ones you see what the change fixed. This is the answer your own agent can't compute by reading the after-state alone: it has no principled BEFORE→AFTER security delta. Use it on every PR that touches CI config — wire INTRODUCES_RISK to a failing status check. Provide each side as { files: {name: yaml} } (or { source } for one file). Premium: requires an API key (set CI_SENTINEL_KEY) or pays per call via x402. Heuristic static analysis, not a guarantee.
| Name | Required | Description | Default |
|---|---|---|---|
| after | Yes | The AFTER state of the CI config (the PR / head branch). Same shape as 'before'. | |
| before | Yes | The BEFORE state of the CI config (the base / target branch). Same shape as audit input: { files: {filename: yaml}, ... } or { source: "..." }. May also carry actionYmls/includeYmls/orbYmls/templateYmls/sharedLibYmls for cross-file resolution. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses the tool's behavior: it runs the full engine on both states, reconciles findings using line-independent identity, reports introduced/removed/aggravated findings, and provides a verdict. It also warns that the analysis is heuristic and not a guarantee, and mentions the need for an API key.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is dense and informative, front-loading the purpose and key details. While it is long, every sentence adds value. It could be slightly more structured (e.g., separating input format from usage), but given the complexity, it remains efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of the tool (nested objects, diffing, multiple verdicts) and the absence of an output schema, the description is remarkably complete. It covers input format, reconciliation logic, output details (findings and verdict), and usage context, ensuring the agent can select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds significant meaning beyond the schema: it explains how to provide the input shapes (using 'files' or 'source'), confirms that 'after' has the same shape as 'before', and gives concrete examples. This enhances the agent's understanding of parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs a differential CI/CD security check for a workflow/pipeline change, specifying the verb 'diff' and the resource 'CI config change'. It distinguishes from the sibling tool 'audit_ci_security' by emphasizing the before/after comparison and the delta analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises using the tool on every PR that touches CI config and suggests wiring the 'INTRODUCES_RISK' verdict to a failing status check. It also notes that the agent cannot compute the delta on its own, providing clear context for when to use this tool. However, it does not explicitly state when not to use it or mention alternative tools beyond the sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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