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Labs"},"license":"PolyForm-Noncommercial-1.0.0","homepage":"https://github.com/cogniumhq/circle-ir-ai#readme","keywords":["sast","security","static-analysis","llm","ai","vulnerability-detection","taint-analysis","code-analysis"],"repository":{"url":"git+https://github.com/cogniumhq/circle-ir-ai.git","type":"git"},"description":"LLM-enhanced SAST analysis built on circle-ir","maintainers":[{"name":"openmason","email":"openmason@gmail.com"},{"name":"elarasu","email":"elarasu@gmail.com"}],"readme":"# circle-ir-ai\n\nLLM-enhanced static analysis built on [circle-ir](https://github.com/cogniumhq/circle-ir). Provides CLI tools, AI-powered vulnerability verification, and comprehensive security analysis.\n\n## Features\n\n- **Multi-Language Support**: Java, JavaScript/TypeScript, Python, Rust, Go, Bash, HTML\n- **LLM Discovery**: Discover vulnerabilities beyond static patterns via LLM\n- **Cross-File Analysis**: Track taint across file boundaries\n- **AI Skills Analysis**: Security analysis for AI agent skills (MCP servers)\n- **Security Scanning**: OWASP Top 10 mapping with trend tracking\n- **Dead Code Detection**: Find unreachable code via call graph analysis\n- **Secret Scanning**: Detect secrets in code and Git history\n- **Health Scoring**: Calculate overall codebase health\n\n## Installation\n\n```bash\nnpm install circle-ir-ai circle-ir\n```\n\n## CLI\n\nFor CLI usage, install [cognium-ai](https://www.npmjs.com/package/cognium-ai):\n\n```bash\nnpm install -g cognium-ai\n\ncognium-ai scan ./src                    # Security scan (OWASP Top 10)\ncognium-ai scan ./src --language java    # Filter by language\ncognium-ai trust ./src                   # Trust score (27 passes)\ncognium-ai quality ./src                 # Quality score (5 passes)\ncognium-ai dead-code ./src               # Dead code detection\ncognium-ai secrets ./src                 # Secret scanning\ncognium-ai health ./src                  # Codebase health score\n```\n\nOptions: `--format json|summary|sarif`, `-o <file>`, `--llm`, `-q`\n\n### LLM CLI Flags\n\n```bash\ncognium-ai scan ./src --llm                        # Enable LLM enrichment\ncognium-ai scan ./src --llm --llm-model gpt-4o     # Override model\ncognium-ai scan ./src --llm --llm-base-url <url>   # Override API base URL\n```\n\n| Flag | Description | Default |\n|------|-------------|---------|\n| `--llm` | Enable LLM enrichment | off |\n| `--llm-base-url <url>` | LLM API base URL (OpenAI-compatible) | `LLM_BASE_URL` env var |\n| `--llm-api-key <key>` | LLM API key | `LLM_API_KEY` env var |\n| `--llm-model <model>` | LLM model name | `LLM_ENRICHMENT_MODEL` env var |\n| `--language <lang>` | Filter by language (java, typescript, python, etc.) | all |\n\nCLI flags override environment variables. Any OpenAI-compatible API works (OpenAI, Azure, Ollama, Together, GitHub Models, etc.).\n\n## Programmatic API\n\n```typescript\nimport { initAnalyzer } from 'circle-ir';\nimport { analyzeFile } from 'circle-ir-ai';\n\nawait initAnalyzer();\n\nconst result = await analyzeFile('MyClass.java', code, {\n  language: 'java',\n  enableEnrichment: true,\n  enableVerification: true,\n});\n\nconsole.log('Findings:', result.findings);\n```\n\n### Multi-file analysis\n\n```typescript\nimport { analyzeProjectTwoPhase } from 'circle-ir-ai';\n\nconst result = await analyzeProjectTwoPhase(files, 'java', {\n  enableEnrichment: true,\n  parallelPhase1: true,\n  maxConcurrency: 10,\n});\n\nconsole.log('Cross-file taint flows:', result.crossFileFlows);\n```\n\n### Security scanning, dead code, secrets, health\n\n```typescript\nimport { scanDirectory, detectDeadCode, scanForSecrets, calculateHealthScore } from 'circle-ir-ai';\n\nconst scan = await scanDirectory('/path/to/project');\nconst deadCode = await detectDeadCode({ target: '/path/to/project' });\nconst secrets = await scanForSecrets('/path/to/project', { scanHistory: true });\nconst health = await calculateHealthScore('/path/to/project');\n```\n\n### AI skill analysis\n\n```typescript\nimport { analyzeSkillBundle } from 'circle-ir-ai';\n\nconst result = await analyzeSkillBundle('./my-skill', {\n  enableCrossArtifact: true,\n  enableVerification: true,\n});\n\nconsole.log(`Trust Score: ${result.score}`);\nconsole.log(`Findings: ${result.findings.length}`);\n```\n\n## LLM Configuration\n\nConfigure via environment variables or CLI flags (flags take precedence):\n\n```bash\n# Environment variables\nexport LLM_API_KEY=your-api-key\nexport LLM_BASE_URL=http://localhost:4000/v1\nexport LLM_ENRICHMENT_MODEL=cognium/gpt-oss-120b   # default (FREE)\n\n# Or use CLI flags directly\ncognium-ai scan ./src --llm \\\n  --llm-base-url https://api.openai.com/v1 \\\n  --llm-api-key sk-... \\\n  --llm-model gpt-4o\n```\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## 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 needed:\n\n```yaml\nname: SAST 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      - name: Install cognium-ai\n        run: |\n          npm install cognium-ai@latest\n          ln -s node_modules/circle-ir/dist/wasm wasm\n\n      - name: LLM-enhanced SAST scan\n        env:\n          LLM_BASE_URL: https://models.github.ai/inference\n          LLM_API_KEY: ${{ github.token }}\n          LLM_ENRICHMENT_MODEL: openai/gpt-4o-mini\n        run: |\n          npx cognium-ai scan ./src --llm --format json -o sast-results.json\n```\n\n**GitHub Models free tier limits**: `openai/gpt-5` = 50 req/day, `openai/gpt-4o-mini` = 150 req/day. Auth uses the built-in `GITHUB_TOKEN` with `models: read` permission.\n\n## Benchmark Results\n\n| Benchmark | Score |\n|-----------|-------|\n| OWASP Benchmark (Java, 1415 tests) | **100%** |\n| Juliet Test Suite (243 tests) | **100%** |\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`) |\n| NodeJS Synthetic (25 tests) | 93.8% TPR |\n| CWE-Bench-Rust (50 tests) | 86.5% TPR, 7.7% FPR |\n| Go Synthetic (29 tests) | 84.2% TPR, 30% FPR |\n| Vulnerability-goapp (13 tests, real-app) | cmdi/xss/pathtraver 100%; sqli precision pending [circle-ir#2](https://github.com/cogniumhq/circle-ir/issues/2) |\n\nCWE-Bench-Java reference (IRIS-paper strict methodology — sink within fix method range): CodeQL 22.5%, IRIS+GPT-4 45.8%. 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### Top 100 Secure Repos (LLM Discovery on 21 production repos, 967K LOC)\n\n| Model | LLM Findings | Notes |\n|-------|--------------|-------|\n| claude-opus-latest | **29** | Zero JSON failures, most reliable |\n| grok-code-latest | 29 | Best Rust detection |\n| gpt-oss-120b (FREE) | ~27 | 90% detection at zero cost |\n\nTop findings: Jenkins (9), HuggingFace (4), Tauri (6-7), TiKV (3), Deno (3), Cargo (2-5).\n\n## Supported Languages\n\n| Language | Frameworks |\n|----------|------------|\n| Java | Spring, JAX-RS, Servlet API |\n| JavaScript/TypeScript | Express, Fastify, Node.js |\n| Python | Flask, Django, FastAPI |\n| Rust | Actix-web, Rocket, Axum |\n| Go | net/http, Gin, Echo, Fiber, Chi, Gorilla |\n| Bash | Shell script command-injection / code-injection detection |\n| HTML | XSS detection in templates |\n\n## MCP tools\n\n`circle-ir-ai/mcp` exports this library's ten MCP tools as a module for\n[`@cognium/mcp-server`](https://www.npmjs.com/package/@cognium/mcp-server). Install both and the\nserver finds the module at startup and lists the tools after its own:\n\n```bash\nnpm install @cognium/mcp-server circle-ir-ai\nnpx mcp-server-cognium-dev\n```\n\nThe module is refused, on one line of stderr, if it was built against a different `circle-ir`\nminor than the server has; the server's own tools keep serving.\n\nEach tool declares annotations in `tools/list`. All ten are read-only. Seven never leave the\nmachine; `find_stale_dependencies`, `find_typosquats` and `find_credential_exposure` can, and say\nso (`openWorldHint: true`).\n\nEvery response carries `provenance`: `deterministic`, or `llm: <model>` when a model judged\nsomething in that call. Nine of the ten tools are deterministic. The one model path is\n`llm_verify` on `find_credential_exposure`: off by default, and refused with one line unless\nthe install has a configured endpoint (`COGNIUM_ENDPOINT`) or a licence token.\n\n## Related Packages\n\n- **[circle-ir](https://github.com/cogniumhq/circle-ir)**: Core SAST library (open source, MIT)\n- **[@cognium/mcp-server](https://www.npmjs.com/package/@cognium/mcp-server)**: The MCP server that loads `circle-ir-ai/mcp` (MIT)\n\n## License\n\nPolyForm Noncommercial License 1.0.0 -- free for noncommercial use.\nCommercial use requires a separate license. See [LICENSE](./LICENSE) for details.\n","readmeFilename":"README.md"}