Shannon AI Penetration Tester Delivers 96% Exploit Success Rate

Problem

Traditional penetration testing is typically performed once a year, leaving applications exposed for the remaining 364 days. Shannon is positioned as an on‑demand white‑box AI penetration tester that produces only reproducible proof‑of‑concepts, eliminating false‑positives.

Core capabilities

OWASP Juice Shop: discovered 20+ critical issues, including full authentication bypass, database leakage, full‑privilege escalation, and SSRF.

c{api}tal API: uncovered nearly 15 vulnerabilities such as root‑level injection, authentication bypass, and privilege escalation, all reported with zero false‑positives.

OWASP crAPI: identified 15+ issues, including multiple JWT attacks, database compromise, and SSRF, again with zero false‑positives.

The tool achieved a 96.15% vulnerability‑exploitation success rate and defeated opponents in the XBOW benchmark without prompts.

Supported vulnerability types

Injection (SQL injection, command injection)

Cross‑site scripting (XSS)

Server‑Side Request Forgery (SSRF)

Broken authentication/authorization

Insecure Direct Object Reference (IDOR)

Other OWASP‑listed categories

Key features

Fully autonomous : a single command handles 2FA/TOTP login, browser navigation, and report generation with minimal user interaction.

Pentester‑grade reports : include only verified, exploitable findings with copy‑pasteable PoCs.

Code awareness : analyses source code to guide attack strategies and performs real‑time exploitation on running applications.

Tool integration : bundles Nmap, Subfinder, WhatWeb, and Schemathesis.

Parallel processing : analyses and exploits all vulnerability classes concurrently for maximum speed.

Technical architecture

Shannon follows a four‑stage pipeline:

侦察 → 漏洞分析 → 漏洞利用 → 报告

Stage 1 – Reconnaissance

Analyzes source code, integrates Nmap and Subfinder to map the technology stack, and uses browser automation to discover entry points, API endpoints, and authentication mechanisms.

Stage 2 – Vulnerability Analysis

Runs parallel agents for each OWASP category. For injection and SSRF, performs structured data‑flow analysis to trace user input to dangerous sinks.

Stage 3 – Exploitation

Dedicated exploitation agents receive hypothesized attack paths and attempt real attacks via browser automation or command‑line tools. The principle “no exploit, no report” discards unsuccessful attempts.

Stage 4 – Reporting

Combines reconnaissance data with successful exploit evidence to generate a professional report containing only verified vulnerabilities and reproducible PoCs.

Installation and usage

git clone https://github.com/KeygraphHQ/shannon.git
cd shannon

Basic scan command:

# Basic penetration test
./shannon start URL=https://example.com REPO=/path/to/repo

# With configuration file
./shannon start URL=https://example.com REPO=/path/to/repo CONFIG=./configs/my-config.yaml

# Custom output directory
./shannon start URL=https://example.com REPO=/path/to/repo OUTPUT=./my-reports

Note: Shannon Lite is designed for white‑box testing and requires access to the application’s source code and repository layout.

Conclusion

Shannon demonstrates that AI can move penetration testing from an annual ritual to an on‑demand, automated practice, achieving a 96.15% exploitation success rate and providing concrete, reproducible PoCs for each verified vulnerability.

Project repository: https://github.com/KeygraphHQ/shannon