Shannon
Shannon is an open-source AI-driven penetration testing tool designed for web applications and APIs. It automates the identification and exploitation of security vulnerabilities by analyzing application source code, detecting potential attack vectors, and executing real-world exploits to confirm their existence. By integrating into the development lifecycle, Shannon helps teams uncover critical flaws before they reach production. Unlike traditional static analysis or simple scanners, Shannon uses artificial intelligence to understand the logic and flow of an application, enabling it to craft targeted attacks that mimic those of a human pentester. It goes beyond just flagging potential issues, actually performing safe but realistic attacks to validate the presence and severity of vulnerabilities such as SQL injection, cross-site scripting, and authentication bypasses. Built for developers and security engineers, Shannon can be run from the command line or integrated into CI/CD pipelines, making it suitable for both manual security assessments and automated regression testing. Its AI model learns from each engagement, potentially improving its accuracy and coverage over time.
- Category
- Vector DB
- Pricing
- Open Source
Problem solved
Manually penetration testing web applications and APIs is time-consuming and requires specialized expertise. Shannon automates this process by using AI to analyze source code, identify attack vectors, and execute exploits, enabling teams to find and verify vulnerabilities earlier and more efficiently.
Use cases
- Automated penetration testing in CI/CD pipelines
- Source code vulnerability scanning for web apps
- API security testing and exploit verification
- Pre-production security assessment to catch flaws early
- Assisting security researchers in identifying attack surfaces
- Educational tool for learning about web application security
Audience
- Developers
- Researchers
- Enterprise
- Startups
Pros
- Automates pentesting tasks reducing manual effort
- AI-driven analysis increases coverage and accuracy
- Open source and free to use
- Integrates into modern development workflows
- Provides proof-of-concept exploits for verified vulnerabilities
Cons
- May produce false positives or negatives depending on AI training
- Requires careful ethical use to avoid unintended exploitation
- Limited to web applications and APIs, not network-level testing
- Output requires human validation for critical decisions
- Potential learning curve for configuration and interpretation
Official website: https://github.com/KeygraphHQ/shannon
Canonical LambdaBase page: https://www.lambdabase.com/tools/shannon