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Shannon Lite Autonomous AI Pentester Achieves 96% Benchmark Success

TL;DR

Open-source AI pentester Shannon Lite autonomously discovers and exploits vulnerabilities in web applications with proof-of-concept exploits, achieving 96.15% success on XBOW benchmark.

Key Points

  • 96.15% success rate on hint-free, source-aware XBOW benchmark testing
  • Discovered 20+ critical vulnerabilities in OWASP Juice Shop including auth bypass and database exfiltration
  • Multi-agent architecture combines white-box source analysis with black-box dynamic exploitation across four phases
  • Targets Injection, XSS, SSRF, and Broken Authentication/Authorization with zero-intervention autonomous operation
  • AGPL v3 open source; ~$50 per full test run using Claude 4.5 Sonnet, 1-1.5 hours runtime

Why It Matters

Closes the critical security gap between continuous code deployment (enabled by Claude Code and Cursor) and annual penetration tests. Developers can now run on-demand whitebox pentesting with real exploit validation rather than false-positive alerts, enabling faster secure shipping without waiting for manual penetration testers.
View Shannon Lite on GitHub

Source: github.com