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"4.16.5":"2026-10-01T04:51:50.052Z","4.16.6":"2026-10-03T00:18:53.671Z"},"author":{"url":"https://cognium.net","name":"Cognium Labs"},"license":"MIT","keywords":["security","sast","static-analysis","vulnerability","cli","ai","llm","taint-analysis"],"description":"AI-powered static analysis CLI with LLM-enhanced vulnerability detection","maintainers":[{"name":"openmason","email":"openmason@gmail.com"},{"name":"elarasu","email":"elarasu@gmail.com"}],"readme":"# cognium-ai\n\nAI-powered static analysis CLI with LLM-enhanced vulnerability detection.\n\n## Installation\n\n```bash\nnpm install -g cognium-ai\n```\n\n## Commands\n\n```bash\ncognium-ai scan <path>          # Scan for security vulnerabilities (LLM-enhanced)\ncognium-ai trust <path>         # Trust score across 36 passes (supply chain, security, AI safety, compliance)\ncognium-ai quality <path>       # Quality score (complexity, tests, docs, maintainability, performance)\ncognium-ai secrets <path>       # Scan working tree + Git history for hardcoded secrets (LLM-verified)\ncognium-ai spec-diff <path>     # Spec-gap analysis (code vs Specifica spec alignment)\ncognium-ai generate-spec <path> # Generate Specifica spec from code\n\ncognium-ai init                 # Create cognium.config.json (auto-detects languages)\ncognium-ai doctor               # Check environment, LLM config, project setup\ncognium-ai version              # Show component versions\n```\n\nRun `cognium-ai <command> --help` for command-specific options. Several\nadditional commands (`dead-code`, `health`, `metrics`, `understand`,\n`cluster`, `compare`, `analyze-skill`, `benchmark`) are available but\ncurrently undergoing CLI polish — they remain callable but are hidden\nfrom the headline `--help` until they soak.\n\n## Scan Options\n\n```bash\ncognium-ai scan src/                              # LLM-enhanced scan (default)\ncognium-ai scan src/ --no-llm                     # Static-only (no LLM)\ncognium-ai scan src/ -f json -o results.json      # JSON output to file\ncognium-ai scan src/ -f sarif -o results.sarif    # SARIF 2.1.0 (GitHub code-scanning)\ncognium-ai scan src/ --severity high              # High+ severity only\ncognium-ai scan src/ --exclude-tests              # Skip test files\ncognium-ai scan src/ --threads 20                 # Custom parallelism\ncognium-ai scan src/ -x '**/vendor/**'            # Exclude paths\ncognium-ai scan src/ --llm-timeout 180            # Raise per-call LLM timeout (slow local models)\ncognium-ai scan src/ --exit-code                  # Exit 1 on findings (CI)\n```\n\n`cognium-ai trust src/ -f sarif -o trust.sarif` produces SARIF 2.1.0\nacross all 36 trust passes (richer than `scan -f sarif`, which is\nscoped to OWASP Top 10 findings only).\n\n## Secret Scanning\n\n```bash\ncognium-ai secrets src/                            # working tree + last 100 commits (LLM verify on)\ncognium-ai secrets src/ --no-history               # working tree only\ncognium-ai secrets src/ --max-commits 500          # deeper Git history\ncognium-ai secrets src/ --include-category aws --include-category github\ncognium-ai secrets src/ --exclude-category high-entropy\ncognium-ai secrets src/ --min-confidence 0.9       # keep only highly-confident findings\ncognium-ai secrets src/ --min-confidence 0         # legacy: keep all (including raw high-entropy)\ncognium-ai secrets src/ --llm-verify-budget 0      # disable LLM verify (static patterns only)\ncognium-ai secrets src/ --severity high            # high+ severity only\ncognium-ai secrets src/ -f json -o secrets.json    # JSON output\ncognium-ai secrets src/ --exit-code                # Exit 1 on findings (CI)\n```\n\n`--severity` and `--min-confidence` are orthogonal: severity filters by\nthe detector's declared severity (`critical|high|medium|low`); confidence\nfilters by the per-finding score (raw bucket prior + optional LLM verify).\nRun `cognium-ai secrets --help` for the full flag list and pattern\ncategories.\n\n## LLM Configuration\n\nConfigure via CLI flags or environment variables (flags take precedence):\n\n```bash\n# CLI flags (override env vars)\ncognium-ai scan src/ \\\n  --llm-base-url https://api.openai.com/v1 \\\n  --llm-api-key sk-... \\\n  --llm-model gpt-4o\n\n# Environment variables (used as defaults)\nexport LLM_API_KEY=your-api-key\nexport LLM_BASE_URL=http://localhost:4000/v1\nexport LLM_ENRICHMENT_MODEL=cognium/gpt-oss-120b\n```\n\n| Flag | Description | Default |\n|------|-------------|---------|\n| `--llm-base-url <url>` | LLM API base URL (OpenAI-compatible) | `http://localhost:4000/v1` |\n| `--llm-api-key <key>` | LLM API key | `LLM_API_KEY` env var |\n| `--llm-model <model>` | LLM model name (universal — applies to all phases) | `cognium/gpt-oss-120b` |\n| `--llm-timeout <seconds>` | Per-call LLM timeout. Raise for slow local models (e.g. `--llm-timeout 180`); lower for fail-fast iteration. | `60` |\n| `--no-llm` | Disable LLM, use static analysis only | LLM enabled by default |\n\n### Provider Examples\n\n| Provider | `--llm-base-url` | `--llm-model` |\n|----------|-------------------|---------------|\n| Cognium (free) | `http://localhost:4000/v1` | `cognium/gpt-oss-120b` |\n| OpenAI | `https://api.openai.com/v1` | `gpt-4o` |\n| GitHub Models (free) | `https://models.github.ai/inference` | `openai/gpt-5` |\n| Azure OpenAI | `https://YOUR.openai.azure.com/...` | `gpt-4o` |\n| Ollama (local) | `http://localhost:11434/v1` | `llama3` |\n| Together AI | `https://api.together.xyz/v1` | `meta-llama/Llama-3-70b` |\n\n## Performance: local Ollama vs cloud LLM\n\nLLM-enriched scans dispatch ~3 LLM calls per source file (role classification, source discovery, sink discovery). Throughput is dominated by the LLM endpoint's per-call latency, not the SAST analysis. Practical guidance:\n\n| Setup | Per-call latency | Practical throughput | Recommended for |\n|---|---|---|---|\n| **Cognium proxy** (`http://localhost:4000/v1`) | ~1–3s | ~10–30 files/min | Daily scans, CI |\n| **Cloud (OpenAI gpt-4o-mini, GitHub Models)** | ~1–4s | ~10–30 files/min | Daily scans, CI |\n| **Ollama 7B+** (`llama3:8b`, `qwen2.5-coder:7b`) | ~5–15s | ~3–10 files/min | Small repos, local development |\n| **Ollama 1.5B–3B** (`llama3.2:3b`, `qwen2.5-coder:1.5b`) | ~3–10s | ~5–15 files/min | Development only — JSON quality is unreliable (#25, #37) |\n| **Ollama reasoning** (`deepseek-r1`, `o1`) | 30–120s+ | <1 file/min | Not recommended (#25 — `<think>` blocks break JSON parser) |\n| **Static-only** (`--no-llm`) | n/a | 100s files/sec | CI gates, large repos, air-gapped |\n\nFor a medium JS repo (~1000 source files), expect:\n- Cognium / cloud: 30 sec – 5 min\n- Ollama 7B: 2–6 hours\n- Ollama 3B: probably similar but with degraded finding quality\n- Static-only: <1 min\n\n### Large repos: skip project-profile auto-detection\n\nOn a repo with tens of thousands of files, **project-profile auto-detection is one of the largest\nrecoverable slices of a static scan** — about 5.6 s (~8 %) of a 70 s `--no-llm` scan of\n`apache/camel` (25.5k files), spent walking Maven/Gradle build context (cognium-ai#232).\n\nIt runs once per scan, and **only when you do not supply a profile**. Passing `--profile` skips it\nentirely:\n\n```bash\n# auto-detect (default): pays the detection walk\ncognium-ai scan ./repo --no-llm\n\n# explicit profile: detection is short-circuited\ncognium-ai scan ./repo --no-llm --profile library/backend\n```\n\nUse this on large repos in CI where the shape is already known. The detection cost is unrelated to\nLLM settings — it is paid on `--no-llm` scans too.\n\n### Tuning knobs\n\nIf you must run LLM-enriched scans against a slow endpoint:\n\n```bash\n# Raise per-call timeout for slow models (default 60s)\ncognium-ai scan ./src --llm-timeout 180\n\n# Concurrency control (env vars, default LLM_MAX_CONCURRENT=5, LLM_RATE_LIMIT=10)\nLLM_MAX_CONCURRENT=2 LLM_RATE_LIMIT=4 cognium-ai scan ./src\n\n# Bound the file count\ncognium-ai scan ./src --max-files 100\n\n# Or skip LLM entirely on large/CI runs\ncognium-ai scan ./src --no-llm\n```\n\n### When to choose which\n\n- **Daily / CI:** cognium proxy or a small cloud model (`gpt-4o-mini`, `openai/gpt-4o-mini` via GitHub Models — generous free tier)\n- **Local development:** static-only by default; use a 7B+ Ollama model for occasional LLM-augmented runs\n- **Air-gapped / sensitive repos:** static-only; the SAST core covers OWASP Top 10 / Juliet at >97% accuracy without LLM\n- **Reasoning models (`deepseek-r1`, `o1`):** route through the cognium proxy — direct calls hit the JSON parser issue documented in #25\n\n## CI/CD with GitHub Actions\n\nRun LLM-enhanced SAST in CI using [GitHub Models](https://github.com/marketplace?type=models) free tier -- no API keys to configure:\n\n```yaml\nname: Security Scan\non: [pull_request]\n\npermissions:\n  contents: read\n  models: read\n\njobs:\n  scan:\n    runs-on: ubuntu-latest\n    steps:\n      - uses: actions/checkout@v4\n      - uses: actions/setup-node@v4\n        with:\n          node-version: \"22\"\n\n      - run: npm install -g cognium-ai\n\n      - name: LLM-enhanced SAST scan\n        env:\n          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}\n        run: |\n          cognium-ai scan ./src \\\n            --llm-base-url https://models.github.ai/inference \\\n            --llm-api-key \"$GITHUB_TOKEN\" \\\n            --llm-model openai/gpt-5 \\\n            -f json -o scan.json\n\n      - name: Trust score with SARIF for code-scanning upload\n        env:\n          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}\n        run: |\n          cognium-ai trust ./src \\\n            --llm-base-url https://models.github.ai/inference \\\n            --llm-api-key \"$GITHUB_TOKEN\" \\\n            --llm-model openai/gpt-5 \\\n            -f sarif -o trust.sarif\n```\n\n**Free tier limits**: `openai/gpt-5` = 50 req/day, `openai/gpt-4o-mini` = 150 req/day. Uses the built-in `GITHUB_TOKEN` with `models: read` permission.\n\n## Supported Languages\n\n| Language | Extensions | Frameworks |\n|----------|------------|------------|\n| Java | `.java` | Spring, JAX-RS, Servlet API |\n| JavaScript | `.js`, `.mjs` | Express, Fastify, Node.js |\n| TypeScript | `.ts`, `.tsx` | Express, Fastify, Node.js |\n| Python | `.py` | Flask, Django, FastAPI |\n| Rust | `.rs` | Actix-web, Rocket, Axum |\n| Go | `.go` | net/http, Gin, Echo, Fiber, Chi, Gorilla |\n| Bash | `.sh`, `.bash` | Shell scripts |\n\n## Benchmark Results\n\n| Benchmark | Score |\n|-----------|-------|\n| OWASP Benchmark (Java) | 100% (1415/1415) |\n| Juliet Test Suite (14 CWEs) | 100% (243/243) |\n| SecuriBench Micro | 97.7% TPR, 6.7% FPR |\n| CWE-Bench-Java (120 CVEs) | mode `static` 50.8% (61/120) · mode `discovery` **86.7%** (104/120, Claude Opus; LLM told the CWE — research recall) · mode `product-verify` 20% (24/120, 4.0.1 / gpt-4o-mini; `cognium-ai scan --llm --llm-verify`) — IRIS-paper strict methodology |\n| OWASP NodeGoat / Juice Shop / DVJA | 100% |\n| NodeJS Synthetic (25 tests) | 92.9% Score (96.2% TPR, 11.1% FPR) |\n| CWE-Bench-Rust (30 tests) | 94.4% TPR, 0% FPR |\n| Bash Synthetic (31 tests) | 100% TPR, 0% FPR |\n| Go Synthetic (29 tests) | 84.2% TPR, 30% FPR |\n| Vulnerability-goapp (13 tests) | cmdi/xss/pathtraver 100%; sqli pending [circle-ir#2](https://github.com/cogniumhq/circle-ir/issues/2) |\n\nCWE-Bench-Java reference: **CodeQL 22.5%**, IRIS+GPT-4 45.8% — cognium-ai in discovery mode with Claude Opus is **2.85× CodeQL**. CWE-Bench-Java numbers carry a **mode label** (cognium-ai#303): `static` = no LLM; `discovery` = the built-in runner's `--llm-discovery`, where the LLM is *told the CWE* and asked where the fix is (a research recall metric); `product-verify` = what customers run, `cognium-ai scan --llm --llm-verify`, scored IRIS-strict by `npm run benchmark:cwe:product`. The three are not interchangeable — product-verify measured **24/120** on 4.0.1 / gpt-4o-mini (Aug 2026) against discovery's ~104/120.\n\n## License\n\nMIT\n","readmeFilename":"README.md"}