Keygraph Shannon: The Autonomous AI Pentester That Writes Its Own Exploits
Shannon is a fully autonomous AI penetration testing framework that performs white-box security assessments. It analyzes application source code, crafts attack strategies, then validates every vulnerability with a live proof-of-concept exploit in the browser — no false positives, no manual intervention.
What Shannon Does Differently
Most vulnerability scanners generate a wall of theoretical findings. Shannon takes the opposite approach: it reads your source code to understand application logic, then attacks your running application through a real browser to prove the vulnerability is exploitable. Every finding comes with a reproducible proof-of-concept.
Architecture
Shannon uses Temporal workflow orchestration to run parallel AI agents, each targeting different attack vectors simultaneously. The system operates in several phases:
• Reconnaissance — integrates Nmap (port scanning), Subfinder (subdomain enumeration), WhatWeb (technology fingerprinting), and Schemathesis (API schema fuzzing)
• Code analysis — the AI reads the target application source code from a local repo to identify vulnerable patterns, authentication logic, and data flows
• Attack execution — exploits are crafted and executed live in a dedicated browser environment
• Validation — every finding is verified with a working proof-of-concept before being reported
Vulnerability Coverage
• Injection attacks — SQL injection, command injection, template injection
• Cross-Site Scripting (XSS) — reflected, stored, and DOM-based variants
• Server-Side Request Forgery (SSRF) — internal network pivoting and cloud metadata access
• Broken authentication — session management flaws, auth bypass, privilege escalation
• Additional vulnerability classes are in active development
Benchmark Results
Shannon Lite achieved a 96.15% success rate on the hint-free, source-aware XBOW Benchmark — the leading AI security evaluation. Against OWASP Juice Shop (a deliberately vulnerable web app), it identified over 20 critical vulnerabilities including:
• Complete authentication bypass
• Database exfiltration via SQL injection
• Multiple stored and reflected XSS chains
• SSRF leading to internal service access
Setup & Requirements
• Docker (mandatory — Shannon runs in containers)
• Anthropic API key (Claude recommended for optimal performance)
• Target application source code in ./repos/ directory
• Optional YAML config for authenticated testing (supports TOTP 2FA and OAuth flows)
Run with: docker compose up followed by shannon run --target <app>
Editions
• Shannon Lite (AGPL-3.0) — open-source version for security teams and researchers testing their own applications
• Shannon Pro (commercial) — adds LLM-powered data flow analysis, advanced detection, CI/CD integration, and dedicated support as part of the Keygraph Security & Compliance Platform (SOC 2, HIPAA)
Limitations
• White-box only — requires source code access (no black-box scanning yet)
• Docker-dependent — no native installation
• API rate limits — Anthropic subscription tiers may throttle long assessments
• Router Mode (OpenAI/Gemini support via OpenRouter) is experimental and may produce inconsistent results
“Shannon doesn’t report theoretical risk. It shows you the exact exploit, the exact payload, and the exact response from your application.” — Keygraph HQ
Created by Austrian developer Peter Steinberger’s team at Keygraph HQ. The broader Keygraph platform aims to automate security compliance end-to-end. Shannon is the offensive security component that replaces annual pentesting engagements with continuous, on-demand AI-driven assessments.