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secure-review

v0.5.13

Published

Multi-model security review for AI-generated code. Runs OpenAI, Anthropic, and Google reviewers in parallel and posts findings as PR comments.

Readme

secure-review

npm version npm downloads License: MIT

Multi-model security review for AI-generated code. CLI and GitHub Action that runs several LLM reviewers (Anthropic, OpenAI, Google) and SAST tools (Semgrep, ESLint, npm audit) against your codebase. Findings are aggregated across reviewers — overlap becomes a confidence signal. Four modes: scan (SAST only), review (multi-model report), fix (cross-model rotating loop applies fixes), pr (GitHub Action entrypoint posting line-anchored comments).

npm install --save-dev secure-review        # https://www.npmjs.com/package/secure-review
npx secure-review init                      # interactive scaffold
npx secure-review review ./src              # report — no file changes
npx secure-review fix ./src                 # report + apply fixes via cross-model loop

How it actually works under the hood — see WORKFLOW.md for the full per-mode pseudo-code (read this if you're evaluating the methodology, not just running the tool).

The design is grounded in recent LLM-security research showing that (1) SAST alone is nearly blind to AI-generated code, and (2) same-model self-review loops often regress. The tool operationalizes the cross-model-review pattern the industry uses informally.

Why this, not GitHub Copilot PR review?

| | GitHub Copilot code review | secure-review | |---|---|---| | Models | 1 (OpenAI via Copilot) | N, any provider | | Security-specialized | No (general quality) | Yes (skill-configurable) | | Agreement signal across models | No | Yes | | SAST integrated with AI | No | Yes (Semgrep + ESLint + npm audit) | | Provider-agnostic | No (Copilot only) | Yes | | Empirical justification | Marketing | Grounded in LLM-security research (see below) |

Quick start — CLI

npm install --save-dev secure-review
npx secure-review init        # interactive scaffold: .secure-review.yml + .env or .env.example
# if init created .env.example: cp .env.example .env
# edit .env — paste your API keys
npx secure-review review ./src

.env in the current directory is auto-loaded — no source .env needed.

init asks a few yes/no questions (which providers, enable SAST, enter keys now or later) and drops a working config + env file. Use --yes to skip the prompts and accept all defaults; that non-interactive path writes .env.example, so copy it to .env and fill in the keys before running an AI-backed mode.

Other CLI subcommands

| Command | Purpose | |---|---| | secure-review init | Scaffold .secure-review.yml + .env or .env.example + optional GitHub Actions workflow | | secure-review scan <path> | SAST only — no AI calls, no API keys needed | | secure-review review <path> | Multi-model review, no file changes | | secure-review fix <path> | Iterative review → write → re-review loop | | secure-review setup-secrets | Push API keys from local .env to GitHub Action secrets via gh CLI | | secure-review pr | GitHub Action entry point (called by the workflow) |

One key is enough. You don't need keys for all three providers — secure-review runs with as few as one reader, as long as the writer also uses an enabled provider. Disable any provider during init (or remove its entry from .secure-review.yml) and the tool simply doesn't instantiate that provider. This is useful if you only have an OpenAI key, or want to keep cost down to a single provider.

Quick start — GitHub Action

# .github/workflows/secure-review.yml
name: Secure Review
on: pull_request
permissions:
  contents: read
  pull-requests: write
  checks: write
jobs:
  review:
    runs-on: ubuntu-latest
    if: github.event.pull_request.head.repo.fork == false
    steps:
      - uses: actions/checkout@v4
        with: { fetch-depth: 0 }
      - uses: actions/setup-node@v4
        with: { node-version: 20 }
      - run: npm ci
      - uses: fonCki/secure-review@v1
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
          OPENAI_API_KEY:    ${{ secrets.OPENAI_API_KEY }}
          GOOGLE_API_KEY:    ${{ secrets.GOOGLE_API_KEY }}
          GITHUB_TOKEN:      ${{ secrets.GITHUB_TOKEN }}

Open a PR — a single review is posted, with inline comments for findings that land on GitHub-commentable diff lines and summary text for changed-file findings outside those lines.

Setting GitHub Action secrets

You need to set the API keys as GitHub repo secrets so the action can authenticate with the providers. Two ways:

A) Automated (requires gh CLI installed and gh auth login done):

npx secure-review setup-secrets
# Reads keys from .env, sets one secret per enabled provider via `gh secret set`.
# Use --repo owner/name if not running inside a clone.

B) Manual (always works):

gh secret set ANTHROPIC_API_KEY    # paste when prompted
gh secret set OPENAI_API_KEY
gh secret set GOOGLE_API_KEY

Or via the web UI: https://github.com/<owner>/<repo>/settings/secrets/actions — click New repository secret for each key.

Only set secrets for providers you actually enabled. If you only use OpenAI, just OPENAI_API_KEY. GITHUB_TOKEN is auto-provided by Actions — don't set it.

Config (.secure-review.yml)

writer:
  provider: anthropic
  model: claude-sonnet-4-6
  skill: skills/secure-node-writer.md

reviewers:
  - name: codex-web-sec
    provider: openai
    model: gpt-5-codex
    skill: skills/web-sec-reviewer.md
  - name: sonnet-owasp
    provider: anthropic
    model: claude-sonnet-4-6
    skill: skills/owasp-reviewer.md
  - name: gemini-dependencies
    provider: google
    model: gemini-2.5-pro
    skill: skills/dependency-reviewer.md

sast:
  enabled: true
  tools: [semgrep, eslint, npm_audit]
  inject_into_reviewer_context: true   # reviewers see SAST findings

review:
  parallel: true

fix:
  mode: sequential_rotation             # verifier = reviewers[i % len] each iteration
  max_iterations: 3
  final_verification: all_reviewers

gates:
  block_on_new_critical: true
  max_cost_usd: 20
  max_wall_time_minutes: 15

Every reviewer is a {provider, model, skill} triple. Skills are Markdown files defining the reviewer's role (web-sec pen-tester, OWASP auditor, supply-chain specialist, etc.). Write your own by copying skills/*.md.

Environment

ANTHROPIC_API_KEY=...
OPENAI_API_KEY=...
GOOGLE_API_KEY=...

# Local dev: use the provider's CLI binary instead of API (Claude Max / Gemini CLI subscription).
# GitHub Actions runners: must be api (factory refuses cli mode in runners).
ANTHROPIC_MODE=api        # api | cli
OPENAI_MODE=api           # api only
GOOGLE_MODE=api           # api | cli

# For `secure-review pr`
GITHUB_TOKEN=...

Modes

Each mode below is the friendly summary. For the full per-step pseudo-code, see WORKFLOW.md.

scan — SAST only

secure-review scan ./src

Runs Semgrep, then ESLint, then npm audit, and normalizes their output to the same Finding schema the AI readers use. No LLM calls, no API keys required. Cheapest pre-commit triage.

review — multi-model parallel one-shot

secure-review review ./src

SAST runs first, then every reader (e.g. anthropic-haiku + openai-mini + gemini-flash) scans the same code with the SAST findings passed as prior context when enabled. Reviewers run in parallel by default; set review.parallel: false in .secure-review.yml to run them sequentially. Findings are deduped by {file, line-bucket, CWE-or-title-prefix} — overlapping findings merge, and reportedBy accumulates names. Confidence per finding is min(1, |reportedBy| / 3), so a finding flagged by 2 of 3 reporters is high-confidence.

No file mutations. Output: reports/review-<timestamp>.{md,json}.

fix — cross-model rotating loop (0.5.0+ semantics)

secure-review fix ./src --max-iterations 3 --max-cost-usd 20

The mode that actually fixes things. Three phases:

  1. Initial union scan — SAST runs first, then all readers run in parallel. The aggregated union becomes the writer's iter-1 to-do list (no reader's blind spots get a free pass).
  2. Iteration loop (rotating verifier per iter):
    • Step A: Writer applies fixes for the current findings list (iter 1: union; iter 2+: previous verifier's audit).
    • Step B: Next reader in rotation audits the writer's output with fresh eyes (different model = different blind spots).
    • Step C: That audit becomes the next iteration's input.
    • The loop only exits when N consecutive verifiers all see clean (full rotation), or a gate fires (block_on_new_critical, max_cost_usd, max_wall_time_minutes).
  3. Final verification — by default, all readers in parallel re-scan the final state. Catches anything the per-iteration verifiers missed individually.

The writer is always the same model; the verifier rotates. This prevents the writer from drifting toward "code that satisfies one specific model" — every iteration a different judge shows up.

Earlier versions (pre-0.5.0) used a different loop: each iteration's reviewer scanned alone, single-reviewer-zero exited the loop early, and the initial scan was a vanity baseline metric. See CHANGELOG.md for the migration notes.

Output: reports/fix-<timestamp>.{md,json} plus modified source files.

pr — GitHub Action entrypoint

Runs review mode on the full checkout, then filters the aggregated findings against the PR diff before posting a single review. Findings are split into three buckets:

  • inline — finding on a changed line in a changed file → posted as inline comment
  • summary — finding in a changed file but on an unchanged line → mentioned in the review summary
  • dropped — finding in an untouched file → not posted

Fork PRs are skipped by default (forks don't have secret access). Fails the check if any CRITICAL finding lands on a diff line.

Architecture

secure-review architecture: entrypoints flow through modes and core modules to reports and GitHub PR review

For the per-mode runtime flow (sequence diagrams, state diagrams, full pseudo-code), see WORKFLOW.md.

Evidence JSON

Every run emits a self-contained JSON with per-iteration counts and severity breakdowns — suitable for plotting, diffing across runs, or feeding into dashboards:

{
  "task_id": "my-app",
  "tool": "secure-review",
  "condition": "F-fix",
  "run": 1,
  "timestamp": "2026-04-28T12:00:00.000Z",
  "model_version": "claude-sonnet-4-6|gpt-5-codex+claude-sonnet-4-6+gemini-2.5-pro",
  "total_findings_initial": 12,
  "findings_by_severity_initial": { "CRITICAL": 1, "HIGH": 3, "MEDIUM": 5, "LOW": 2, "INFO": 1 },
  "total_findings_after_fix": 4,
  "findings_by_severity_after_fix": { "CRITICAL": 0, "HIGH": 1, "MEDIUM": 2, "LOW": 1, "INFO": 0 },
  "new_findings_introduced": 1,
  "findings_resolved": 9,
  "resolution_rate_pct": 75.0,
  "semgrep_after_fix": 0,
  "eslint_after_fix": 0,
  "lines_of_code_fixed": 0,
  "reviewers": ["codex-web-sec", "sonnet-owasp", "gemini-dependencies"],
  "iterations": 3,
  "per_iteration": [...]
}

The same schema is used by both review and fix modes. Review-only runs use condition: "F-review" and set the before/after finding counts to the same values because no fixes are applied.

Developing

npm install
npm run typecheck
npm test
npm run build         # library (dist/)
npm run build:action  # Action bundle (dist-action/index.js) — commit with PRs that touch src/

License

MIT © 2026 Alfonso Pedro Ridao, Shana Stampfli