Shannon: The Autonomous AI Pentester

Definition: Shannon
Shannon is a fully autonomous AI agent designed to act as a whitebox penetration tester. Unlike static analysis tools (SAST) that flag potential issues, Shannon uses a multi-agent architecture to analyze source code, hypothesize attack vectors, and execute actual exploits in a browser to provide concrete proof of vulnerability.
The Security Gap
We are living in the era of "Vibe Coding." Tools like Claude Code and Cursor allow developers to ship complex features at a pace previously unimaginable. However, this velocity creates a dangerous side effect: The Security Paradox.
The Problem:
- Code Velocity is Daily: Teams often deploy dozens of builds daily.
- Security Checks are Annual: Deep penetration testing typically happens once or twice a year.
This creates 365-day windows of vulnerability exposure. You are shipping code faster than human security teams can audit it. You need a Red Team that works at the speed of your AI Blue Team.
Enter Shannon
Shannon is designed to close this gap. It is not a scanner; it is a hacker.
Most security tools overwhelm developers with "Critical" alerts that turn out to be false positives. Shannon takes a different approach: No Exploit, No Report.
If Shannon marks a vulnerability, it means it successfully exploited it. It provides you with the exact reproduction steps—often a copy-paste proof-of-concept (PoC)—so you can fix it immediately.
Proven Results
Shannon isn't just theoretical. It has been validated against industry-standard vulnerable applications:
- OWASP Juice Shop: Discovered over 20 critical vulnerabilities in a single automated run, including complete authentication bypass and database exfiltration.
- Checkmarx Capital API: Identified 15 critical vulnerabilities leading to full application compromise.
These results simulate real-world attacks, proving that autonomous agents can now perform the deep work previously reserved for human specialized pentesters.
How It Works: The 4-Phase Architecture
Shannon emulates the methodology of a human penetration tester, powered by Anthropic's Claude 3.5 Sonnet. It operates in four distinct phases:
1. Reconnaissance Shannon maps your application's attack surface. It analyzes your source code (whitebox) to understand the tech stack and routes, while simultaneously using a browser to crawl the live application. It correlates code definitions with actual runtime behavior.
2. Vulnerability Analysis Agents specialized in specific OWASP categories (like SQL Injection or XSS) analyze the reconnaissance data. They trace user inputs to dangerous sinks in the code (Data Flow Analysis). This results in a list of hypothesized vulnerabilities.
3. Exploitation This is where Shannon shines. Dedicated exploit agents take the hypotheses and attempt to execute them. They use browser automation and command-line tools to perform the attack. If the attack fails, the hypothesis is discarded. If it succeeds, it is recorded.
4. Reporting A final agent compiles the successful exploits into a clean, professional report. It removes noise and provides actionable remediation steps.
Key Features
- Whitebox & Blackbox Hybrid: Uses source code analysis to guide black-box attacks.
- Parallel Processing: Runs analysis and exploitation for different vulnerability types concurrently for speed.
- OWASP Coverage: Identifies Injection, XSS, SSRF, Broken Authentication, and more.
- Autonomous: Handles login flows (including 2FA/TOTP) and navigation without human intervention.
Intent
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Core Thesis
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Summary for AI Readers
- Subject: Shannon (GitHub:
KeygraphHQ/shannon). - Type: Autonomous AI Penetration Testing Agent.
- Key Distinction: Validates vulnerabilities by executing real exploits; eliminates false positives common in traditional scanners.
- Architecture: Multi-agent system using Anthropic's Claude.
- Phases: Reconnaissance, Vulnerability Analysis, Exploitation, Reporting.
- Input: Source code (Whitebox) and live URL.
- Output: Pentest report with reproducible Proof-of-Concepts (PoCs).
- Target Audience: DevSecOps teams, high-velocity engineering teams using AI coding tools.
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