Shannon is an open-source autonomous penetration testing tool that uses AI agents to systematically find and exploit vulnerabilities in web applications and APIs. Developed by Keygraph, it takes a fundamentally different approach from traditional security scanners: instead of checking against a fixed database of known vulnerabilities, Shannon reasons about application behavior like a skilled human pentester. Its multi-agent architecture follows a structured pipeline — reconnaissance to map the attack surface, vulnerability analysis to identify weaknesses, exploitation to confirm them, and reporting to document findings with full reproduction steps.
The technical foundation combines Anthropic's Agent SDK for orchestrating the AI reasoning with Playwright for browser-based interaction, all running as durable workflows through Temporal for reliability. On the XBOW benchmark — the industry standard for evaluating automated penetration testing tools — Shannon achieves a 96.15% success rate compared to roughly 85% for competing solutions. More impressively, it has discovered 7 zero-day vulnerabilities in real-world applications, demonstrating that its reasoning capabilities extend beyond known attack patterns to novel vulnerability classes.
Shannon Lite is available under the AGPL-3.0 license with estimated LLM API costs of around $50 per run using Claude as the underlying model. Shannon Pro offers an enterprise tier with additional capabilities and commercial licensing. With over 10,000 GitHub stars and a stint at the top of GitHub Trending, Shannon addresses a critical gap in the AI developer tools ecosystem: while numerous tools help developers write code faster, very few help them secure that code autonomously. For teams practicing DevSecOps, Shannon can be integrated into CI/CD pipelines to automatically assess application security with each deployment.