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AI Agent 分析
SH

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.

Star
47k
语言
TypeScript
GitHub
来源与合规提示最近同步: Aug 14, 2026

Git-Stars 是独立产品,不隶属于 GitHub 或该项目。 分析可能由 AI 辅助生成,依据公开仓库元数据和 README 的短摘要。 我们不镜像完整 README、文档、Issue 或社媒评论。

原始 GitHub 来源方法论编辑政策

概览

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.

安装方式

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).

解决的问题

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.

可以构建什么

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.

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结论

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.

由 Git-Stars 分析 - 8/14/2026