MCP Sentinel

Build-time security scanning for MCP servers.

AI agents invoke tools exposed by MCP servers, and a single unsafe tool can leak credentials, run arbitrary commands, or hijack the agent. MCP Sentinel catches those holes before you ship — it scans an MCP server and reports the security findings, each mapped to a known threat class.

It works in three layers: deterministic static rules find candidate issues, GPT-5.6 reviews each one in context to cut false positives and order and parameterize the probe plan, and a Docker-isolated sandbox runs the four probes to confirm exploitable or unsafe runtime behavior. Every finding maps to the OWASP Agentic Top 10 (the industry threat list for AI agents) and renders as console, JSON, or SARIF — the standard format GitHub reads for its security tab.

Want the overview before running anything? Jump to What it checks and the Architecture diagram.

Try Sentinel in three minutes

No source checkout or OpenAI API key is required. Install from PyPI, then run the bundled GPT replay with real Docker probes.

1. Install Sentinel

Use Python 3.10, 3.11, 3.12, or 3.13 and install the exact release with pipx:

pipx install portunusmcp-sentinel==0.2.0

Or use uv:

uv tool install portunusmcp-sentinel==0.2.0

2. Check Docker

Start Docker Engine on Linux or Docker Desktop on macOS/Windows. Docker Desktop on Windows must use Linux containers.

docker info
docker buildx version

The first run may download Docker images and fixture dependencies through Sentinel's restricted build network. The scanned server has no runtime network access.

3. Run the demo

macOS or Linux:

sentinel --version
sentinel demo --replay-review --verbose

Windows PowerShell:

sentinel --version
sentinel demo --replay-review --verbose

The demo should exit 0 with Status: COMPLETE, evaluate all seven static and four dynamic rules, and report SENT-001 through SENT-011. It writes validated reports to:

sentinel-demo-results/report.json
sentinel-demo-results/report.sarif

Validate the SARIF independently on macOS or Linux:

python -m sentinel.report.validate_sarif sentinel-demo-results/report.sarif

On Windows PowerShell:

python -m sentinel.report.validate_sarif sentinel-demo-results\report.sarif

The validator produces no output when the report is valid and exits 0. Replay is prominently disclosed and makes no model call; checked responses captured from GPT-5.6 still pass through the production parser, evidence and probe-plan validators, all four real Docker probes, merge logic, and report validation.

See the accepted live SENT-010 GitHub code-scanning alert and the complete Action evidence.

Architecture

flowchart LR
    A[Untrusted MCP repository] --> B[AST + Semgrep rules]
    B --> C[Canonical candidates]
    C --> D[GPT-5.6 semantic review]
    D --> E[Constrained four-probe plan]
    E --> F[Docker sandbox]
    F --> G[Reviewed dynamic evidence]
    D --> H[Deduplication + provenance merge]
    G --> H
    H --> I[Console]
    H --> J[JSON 1.2.0]
    H --> K[SARIF 2.1.0]
    K --> L[GitHub code scanning]

Static analysis never imports or executes target code. Dynamic analysis runs only local Python MCP targets in fresh containers with read-only source, restricted build egress, no runtime network, resource limits, and forced cleanup. GPT can order and bind four permanent inert templates; it cannot emit executable probe code or create rule-less findings.

What it checks

Each rule maps to a category in the OWASP Agentic Top 10; the ASI0x:2026 codes are that list's threat identifiers (e.g. ASI03 is Identity & Privilege Abuse).

Published SENT-xxx rule IDs are compatibility contracts. Existing IDs are never renumbered or reused for a different detection; changed meanings receive new IDs.

See the rule catalog for boundaries, false-positive risks, evidence, and remediation.

Human, Codex, and GPT contribution

The human owner defined product scope, architecture, trust boundaries, threat model, phase gates, and release decisions.

How Codex was used

Codex was the implementation partner for the entire build. The working pattern was design-first: before any implementation, a long Codex session worked through scope and architecture — MVP versus deferred features, how findings map to OWASP categories, the allowed state transitions for a finding, and whether semantic review should be optional (it should not; a flag would have made it decorative). That session is the architectural backbone the rest of the project was built against.

From there Codex built the static rule engine and Semgrep adapter, the Docker sandbox and probe harness, the reporting pipeline, the SARIF validator, the cross-platform test matrix, artifact automation, and the documentation. It also did the debugging on the harder cross-platform problems — Semgrep output parsing and runtime-file isolation on Windows, and the two rounds of SARIF fixes needed before GitHub code scanning would render the reports correctly.

The repository ships an AGENTS.md that constrains how Codex works in this codebase: ask rather than assume, no speculative complexity, no unrelated edits, explicit success criteria. Design decisions stayed with the human owner; Codex accelerated everything downstream of them.

How GPT-5.6 was used

GPT-5.6 is inside the shipped product, not just the build. It is load-bearing at scan time: it reads the server code, decides which static candidates are real findings, and orders and parameterizes the four probes the sandbox runs — turn it off and you get different results. It does not replace the deterministic detectors or the Docker boundary.

The constraints are the design: strict Structured Outputs against a versioned schema, store: false, redacted and capped context, and host-validated source ranges, so the model cannot cite a line that does not exist, invent a finding outside the rule set, or emit executable probe code. See GPT-5.6 behavior and disclosure for the full runtime contract, and artifacts/gpt-ablation.json for a measured comparison of rules-only, GPT-reviewed, and dynamically confirmed outcomes.

Codex session record

Primary Codex /feedback thread for core implementation: 019f70e6-a5fb-7f13-8eae-bca041fc37ad.

Supporting implementation threads:

• 019f7469-e3ed-75a0-9906-7059299b1484
• 019f741f-cf91-7000-b12c-e9aa2a50ff03
• 019f77a1-f2f0-7ab2-9a5d-e72fa1ebc40e

The Phase 5 /feedback record was submitted from the primary thread above.

Requirements and installation

Sentinel supports Python 3.10–3.13 on Linux, macOS, and Windows. Dynamically scanned Python targets remain limited to Python 3.10–3.12. Full scans and demos require Docker Engine or Docker Desktop with Buildx. The GitHub Action runs on Ubuntu.

Development checkout:

uv sync --extra dev
uv run sentinel --version

The pip-compatible development path is:

pip install -e ".[dev]"

Install the exact release from PyPI with pipx:

pipx install portunusmcp-sentinel==0.2.0

Or use uv:

uv tool install portunusmcp-sentinel==0.2.0

Historical v0.1.0 artifact

The earlier v0.1.0 GitHub Release contains mcp_sentinel-0.1.0-py3-none-any.whl, produced by the successful release workflow. Its SHA-256 digest is 4672e63413e87bf750113c06a21133162d00f1e71ca6259a8394028c22b677aa.

To test a locally built 0.2.0 artifact without an index:

uv build
pipx install dist/portunusmcp_sentinel-0.2.0-py3-none-any.whl
# or
uv tool install dist/portunusmcp_sentinel-0.2.0-py3-none-any.whl

CLI

# Complete static + GPT + Docker analysis
sentinel scan ./path/to/server

# Validated SARIF
sentinel scan ./path/to/server --format sarif --output results.sarif

# Static analysis plus required semantic review
sentinel scan ./path/to/server --static-only

# Explicitly allow unreviewed candidates when GPT is unavailable
sentinel scan ./path/to/server --static-only --allow-degraded

# Compact output is default; bounded evidence is opt-in
sentinel scan ./path/to/server --verbose

# Set the failure threshold; default is high
sentinel scan ./path/to/server --fail-on critical

--fail-on accepts critical, high, medium, low, or informational, and determines which findings produce exit code 1.

--color/--no-color overrides display detection. Otherwise NO_COLOR disables style and interactive TTYs receive color. Presentation flags are rejected for JSON/SARIF rather than silently ignored.

A normal scan requires sentinel.target.yaml and sentinel.permissions.yaml. --static-only omits Docker and launch configuration but still requires semantic review. OPENAI_API_KEY is read only by Sentinel; it is never printed, persisted, forwarded to the target, or stored by the Responses API.

Exit codes are stable:

Operational messages use target error:, configuration error:, and infrastructure error: prefixes. --debug adds internal tracebacks.

Judge demo

The wheel contains the vulnerable and clean fixtures, schemas, and GPT cassettes. No source checkout is required.

# Offline GPT replay plus real Docker probes
sentinel demo --replay-review --verbose

# Live GPT review plus real Docker probes
export OPENAI_API_KEY=your-key
sentinel demo --verbose

Both commands atomically refresh validated reports under ./sentinel-demo-results/; use --output-dir to change the location. Expected vulnerabilities make the demo successful, so a complete demo exits 0. Recorded review is prominently labeled and is never represented as a live call. See the judge runbook and narration.

GPT-5.6 behavior and disclosure

The production reviewer uses the OpenAI Responses API with:

• requested model gpt-5.6-sol and recorded returned model ID;
• store: false;
• medium reasoning effort by default;
• strict Structured Outputs using the versioned review schema;
• deterministic context selection, redaction, batching, and candidate caps;
• validated source-range claims and constrained probe plans;
• current/origin latency, tokens, cache, failure, and micro-USD telemetry.

Live mode calls the model. Replay mode feeds checked live responses through the same parser, validators, merge logic, dynamic probes, and reports. Degraded mode is explicit, leaves candidates in needs_review, and remains fail-on eligible. Suppressed candidates stay visible in every report with their reasoning.

GitHub Action

name: MCP Sentinel

on:
  pull_request:
  push:
    branches: [main]

permissions:
  contents: read
  security-events: write

jobs:
  scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - id: sentinel
        uses: BashaarJavaid/MCP-Sentinel@ee91e07d0fa78106dbb6d85b60bd8288173abd23
        with:
          target-path: .
          fail-on: high
          openai-api-key: ${{ secrets.OPENAI_API_KEY }}

The Action validates SARIF before upload and exposes sarif-path, findings-count, and highest-severity. Fork pull requests receive no secret; they run visibly degraded analysis and skip code-scanning upload. Non-fork runs remain fail-closed. The preserved live proof is documented in artifacts/phase4-action-evidence.md.

Reports and reproducibility

python -m sentinel.schema check
python -m sentinel.report.validate_sarif results.sarif
make artifacts-check
make notices-check

artifacts/example.sarif is the checked live report. artifacts/gpt-ablation.json compares rules-only, GPT-reviewed, and dynamically confirmed outcomes over the versioned truth set. Routine generation uses replay and Docker; the final live refresh is hard-capped:

make artifacts
MAX_USD=0.50 make artifacts-live

License

MCP Sentinel is MIT licensed. Dependency licenses and packaged notice files are recorded in THIRD_PARTY_NOTICES.md.