Shannon – AI Pentesting Tool that Autonomously Checks for Code Vulnerabilities and Executes Real Exploits
Shannon is a fully autonomous AI pentesting tool for web applications that identifies attack vectors via code analysis and validates them with live browser exploits.
Shannon is a fully autonomous AI pentesting tool for web applications that identifies attack vectors via code analysis and validates them with live browser exploits.
Unlike traditional static analysis tools that merely flag potential issues, Shannon operates as a fully autonomous penetration tester that identifies attack vectors and actively executes real-world exploits to validate them.
The tool outperforms human pentesters and proprietary systems on the XBOW benchmark, marking a shift toward continuous security testing.
Shannon emulates human red team tactics across reconnaissance, vulnerability analysis, exploitation, and reporting phases.
It ingests source code to map data flows, then deploys parallel agents for OWASP-critical flaws like injection, XSS, SSRF, and broken authentication, using tools such as Nmap and browser automation.
Only confirmed exploits with reproducible proofs-of-concept appear in pentester-grade reports, minimizing false positives.
The Lite edition (AGPL-3.0) suits researchers, while Pro adds LLM data flow analysis for enterprises.
Shannon demonstrated superior performance on vulnerable benchmarks, delivering actionable insights beyond static scans.
ApplicationVulnerabilities IdentifiedKey Exploits ConfirmedOWASP Juice Shop20+ criticalAuth bypass, DB exfiltration, IDOR, SSRFctal API15 critical/highInjection chaining, legacy API bypass, mass assignmentOWASP crAPI15+ critical/highJWT attacks, SQLi DB compromise, SSRFXBOW Benchmark96.15% success rateBeats human (85%, 40 hours) and XBOW prop system (85%) These results highlight Shannon’s ability to autonomously achieve full app compromise.
Powered by Anthropic’s Claude Agent SDK, Shannon runs white-box tests on monorepos or consolidated setups via Docker, supporting 2FA logins and CI/CD integration .
The Lite edition (AGPL-3.0) suits researchers, while Pro adds LLM data flow analysis for enterprises. Typical runs take 1-1.5 hours at ~$50 cost, producing deliverables like executive summaries and PoCs.
As dev teams accelerate with AI coders like Claude, annual pentests leave gaps; Shannon enables daily testing on non-production environments.
Creators emphasize ethical use with authorization required, warning against production runs due to mutative exploits. Available on GitHub, it invites community contributions toward broader coverage.
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Based on reporting by Cyber Security News.