stingward 0.1.6

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Lots of people now ship apps, Claude Code skills and MCP servers written with AI. Few of them know what a CVE is, why NEXT_PUBLIC_OPENAI_API_KEY is a problem, or that a skill can hide instructions only the model can read. Stingward finds those holes and tells you, in plain words, how to close each one.

_{A skill that looks like a PDF helper. Stingward decodes the invisible instruction and finds the key theft before you install it.}

Quick start

stingward                         # audit the project in the current folder
stingward skill ./some-skill      # audit a skill, plugin or MCP server before installing it
stingward skill owner/repo        # ...straight from GitHub (also accepts .../tree/branch/subfolder URLs)
stingward agents                  # audit the Claude Code, Claude Desktop, Cursor, Codex and Gemini setup on this machine

No account, no telemetry, no dependencies: pure Python 3.9+ on Linux, macOS and Windows.

What it finds

All 50 rules, with severity, examples and fixes: docs/rules.md.

_{A typical AI-built Next.js + Supabase app. Try both demos from a clone of this repo: python scripts/demo/fixtures.py demo, then stingward skill demo/pdf-helper and stingward demo/my-saas.}

How it works

1. Lists your files, skipping dependency and build folders such as node_modules and dist. When auditing a third-party skill it does read build output, since that is what actually runs.
2. Reads lockfiles and asks OSV.dev about every package and version in one batch. CVEs in CISA KEV become critical; EPSS and NVD fill in likelihood and missing scores.
3. Runs pattern checks for secrets, app mistakes, agent settings and skill content, aware of context: test folders, docs and examples count less, and in third-party skills reassuring wording can lower a finding to medium but never hide it.
4. Writes the report in English or Portuguese: a verdict, what to fix first, findings worst first with where, why and how to fix (the same problem in several files is listed once, with every place), a grade from A to F and a badge. --html FILE also writes a single-file report to hand over or print to PDF: summary, remediation plan with rough effort, findings with the code excerpt (secrets masked), and scope and method.

Only package names and versions leave your machine (to api.osv.dev, www.cisa.gov, api.first.org and services.nvd.nist.gov). Your code never does. Responses are cached for a few hours in ~/.cache/stingward.

Stingward reads hostile content by design, so it is careful with it: git can never discover a repository inside audited files, the launchers never import code from the audited folder, and secrets, terminal escapes and markdown are neutralized in every report format. See SECURITY.md.

GitHub Action

# .github/workflows/stingward.yml
name: stingward
on:
  pull_request:
  push: { branches: [main] }
  schedule: [{ cron: "0 9 * * *" }]   # new CVEs show up for code that did not change
permissions: { contents: read, pull-requests: write, security-events: write }
jobs:
  stingward:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: HiveControlAgents/StingWard@v0
        with: { lang: en, fail-on: high }

It comments the report on the PR (and updates the same comment on every run), uploads SARIF to the repository's Security tab, writes the job summary and exposes the grade as steps.<id>.outputs.grade.

Command line reference

Environment: STINGWARD_LANG sets the default language, NVD_API_KEY raises the NVD rate limit and STINGWARD_CACHE moves the cache folder.

Grade: A means only low findings or none · B medium, no high · C one or two high · D three or more high · F any critical. Every report ends with a badge for your README:

[![stingward: A](https://img.shields.io/badge/stingward-A-brightgreen)](https://github.com/HiveControlAgents/StingWard)

Human review: for an audit you hand over to someone, write the review file with stingward . --review-template review.json. Each entry points at a finding by its fingerprint (also in the JSON and SARIF output), which does not change when other parts of the file are edited. It is built from the flagged line with the secret already masked, so nothing derived from a secret goes into the review file. That is on purpose, and it means a key replaced by another key on the same line keeps its status: when a key is rotated, check that line again before you reuse the review. Set status to confirmed, false_positive, accepted_risk or fixed, add a comment, then run stingward . --review review.json --client "Client name" --notes notes.md --html report.html. False positives move to an appendix; they and the findings fixed during the audit leave the grade, the fix-first list and --fail-on. Accepted risks stay in the grade but leave the fix-first list. The text and Markdown reports follow the review too. Comments and notes are escaped and go through the same secret masking as code excerpts.

False positives: add a line to .stingwardignore in the project root (RULE path/glob, RULE or path/glob), or put stingward:ignore in a comment on the line. If a rule is wrong, tell us with a minimal sample.

Standards: findings carry CWE, OWASP Top 10 for LLM Applications 2026 (LLM01:2026...), OWASP Agentic Skills Top 10 (AST01..., v1 draft) and OWASP Top 10 for Agentic Applications 2026 (ASI01...) IDs, in the text report, JSON and SARIF tags.

Quality

• 400+ automated tests, plus online tests that query the real OSV, KEV, EPSS and NVD services across six ecosystems. Line coverage is above 90%.
• Every push runs 24 CI jobs: tests on Linux, macOS and Windows with Python 3.9 to 3.14, the PyPI and npm packages built and installed from scratch on all three systems, the Claude Code plugin validated, and the Action run against sample projects.
• Stingward audits itself in CI and must stay free of high and critical findings; its own grade is the A badge above.
• False positives are measured against popular public skill collections and MCP servers: no critical or high findings that are not real, and only a handful of medium ones per repository. Each fixed false positive becomes a regression test.
• Security review of Stingward itself, for example making sure a skill folder crafted as a git repository cannot run commands, and that a secret can never reach a report. Found issues are fixed with a test that fails on the old code.

python -m unittest discover -s tests -t .                       # offline, a few seconds
STINGWARD_ONLINE=1 python -m unittest discover -s tests -t .    # also hits the real databases

FAQ

Does a clean report mean my project is safe? No. It means the common mistakes are not there. Stingward is a fast first line, not a penetration test.

Does my code leave my machine? No. Only package names and versions are sent, to look up vulnerabilities.

How is it different from npm audit, Dependabot or a secret scanner? Those each cover one piece. Stingward puts dependencies, secrets and the mistakes specific to AI-built apps, agents, skills and MCP servers in one report, written for the person who built the project, and can audit a skill before you install it.

Why does npx stingward need Python? The engine is a single pure-Python package with no dependencies; the npm package is a thin launcher so JavaScript users can run it without pip. On Windows, npx does not find the .bat shims of pyenv-win: install Python from python.org, or use pip install stingward.

Can it check a private repository or a skill on my disk? Yes. Point it at a folder, or at owner/repo with your usual git credentials.

Scope

Stingward reports to whoever runs it, about code they chose to check. It does not crawl other people's repositories or open issues on them.

Contributing

Bug reports, false positives and new rules are welcome. Read CONTRIBUTING.md to get set up, and report security problems in Stingward itself privately as described in SECURITY.md.

Vulnerability data: OSV.dev (aggregating GitHub Advisories, NVD, PyPA, RustSec, Go and OpenSSF malicious packages), CISA KEV, FIRST EPSS and NVD.

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