AI Agent Analysis

KeygraphHQ/shannon

Shannon is an AI pentester for web applications and APIs. It analyzes your source code, identifies attack vectors, and executes real exploits to prove vulnerabilities before they reach production.

stars

47k

Language

TypeScript

GitHub

Source and compliance noteLast synced: Aug 14, 2026

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Overview

Shannon is an autonomous AI pentester for web applications and APIs. It combines source-code analysis with live exploitation to identify attack vectors and execute real exploits, proving vulnerabilities with working proof-of-concepts before they reach production. The open-source version runs locally via CLI, while the same agent powers Keygraph's commercial platform.

Installation

Run `npx @keygraph/shannon setup` to configure credentials, then `npx @keygraph/shannon start -u https://your-app.com -r /path/to/your-repo` to start a pentest. Requires Docker, Node.js 18+, and AI provider credentials (Anthropic, OpenAI, xAI, or AWS Bedrock).

Problem solved

Traditional penetration testing is infrequent (e.g., once a year), leaving a security gap between releases. Shannon provides on-demand, automated pentesting that can run against every build or release, closing the gap and catching vulnerabilities before they reach production. It also reduces reliance on manual security expertise by automating the entire process.

What you can build

Developers can build automated security testing pipelines that scan web apps and APIs for vulnerabilities like SQL injection, authentication bypass, IDOR, SSRF, and command injection. The agent can be integrated into CI/CD to test every build, and it produces detailed reports with proof-of-concept exploits. The ceiling includes running comprehensive pentests on complex applications, as demonstrated by reports on OWASP Juice Shop and crAPI, and it can be extended to various AI providers and custom environments.

Community sentiment

Positive

No community feedback yet.

Concerns

No concerns documented yet.

Bottom line

Shannon is ideal for development teams and security-conscious organizations that want continuous, automated pentesting without manual effort. It is not suitable for those without proper authorization to test targets or who lack the infrastructure (Docker, AI credentials). The key trade-off is automation and speed versus the need for human oversight to ensure ethical use and handle edge cases.

Analyzed by Git-Stars - 8/14/2026