Information
Documentation | 中文 | English
AI Red Teaming Platform by Tencent Zhuque Lab
**A.I.G (AI-Infra-Guard)** integrates capabilities such as AI infra vulnerability scan, MCP Server risk scan, and Jailbreak Evaluation, aiming to provide users with the most comprehensive, intelligent, and user-friendly solution for AI security risk self-examination.We are committed to making A.I.G(AI-Infra-Guard) the industry-leading AI red teaming platform. More stars help this project reach a wider audience, attracting more developers to contribute, which accelerates iteration and improvement. Your star is crucial to us!
## Table of Contents - [ Features](#-features) - [️ Showcase](#-showcase) - [ Quick Start](#-quick-start) - [ User Guide](#-user-guide) - [ Contribution Guide](#-contribution-guide) - [ Acknowledgements](#-acknowledgements) - [ Join the Community](#-join-the-community) - [ Citation](#-citation) - [ Related Papers](#-related-papers) - [ License](#-license) ## Features | Feature | More Info | |:--------|:------------| | **AI Infra Scan** | Precisely identifies over 30 AI framework components and covers nearly 400 known CVE vulnerabilities, including Ollama, ComfyUI, vLLM, etc. | | **MCP Server Scan** | Powered by AI Agent, Detects 9 major categories of MCP security risks, Supports source code/remote URL scanning. | | **Jailbreak Evaluation** | Rapidly assesses Prompt security risks, Includes multiple curated jailbreak evaluation datasets, Cross-model security performance comparison. | | **Easy-to-use Web Interface** | Modern, user-friendly web UI for seamless operation, One-click scanning with real-time progress tracking, Comprehensive Analysis Reports. | | **Multi-Language Support** | Chinese and English interface, Localized documentation and help. | | **Cross-Platform Compatibility** | Linux, macOS, and Windows support, Docker-based deployment. | | **Free & Open Source** | Offered completely free of charge under the MIT license. |## ️ Showcase ### A.I.G Main Interface  ### AI Infra Scan  ### MCP Server Scan  ### Jailbreak Evaluation  ### Plugin Management 
## Quick Start ### Deployment with Docker **System Requirements** | Docker | RAM | Disk Space | |:-------|:----|:----------| | 20.10 or higher | 4GB+ | 10GB+ | **1. One-Click Install Script (Recommended)** \`\`\`bash # This method will automatically install Docker and launch A.I.G with one command curl https://raw.githubusercontent.com/Tencent/AI-Infra-Guard/refs/heads/main/docker.sh | bash \`\`\` **2. Run with pre-built images (Recommended)** \`\`\`bash git clone https://github.com/Tencent/AI-Infra-Guard.git cd AI-Infra-Guard # This method pulls pre-built images from Docker Hub for a faster start docker-compose -f docker-compose.images.yml up -d \`\`\` **3. Build from source and run** \`\`\`bash git clone https://github.com/Tencent/AI-Infra-Guard.git cd AI-Infra-Guard # This method builds a Docker image from local source code and starts the service docker-compose up -d \`\`\` Once the service is running, you can access the A.I.G web interface at: \`http://localhost:8088\`
## User Guide Visit our online documentation: [https://tencent.github.io/AI-Infra-Guard/](https://tencent.github.io/AI-Infra-Guard/) For more detailed FAQs and troubleshooting guides, visit our [documentation](https://tencent.github.io/AI-Infra-Guard/?menu=faq).
## Contribution Guide The extensible plugin framework serves as A.I.G's architectural cornerstone, inviting community innovation through Plugin and Feature contributions. ### Plugin Contribution Rules 1. **Fingerprint Rules**: Add new YAML fingerprint files to the \`data/fingerprints/\` directory. 2. **Vulnerability Rules**: Add new vulnerability scan rules to the \`data/vuln/\` directory. 3. **MCP Plugins**: Add new MCP security scan rules to the \`data/mcp/\` directory. 4. **Jailbreak Evaluation Datasets**: Add new Jailbreak evaluation datasets to the \`data/eval\` directory. Please refer to the existing rule formats, create new files, and submit them via a Pull Request. ### Other Ways to Contribute - [Report a Bug](https://github.com/Tencent/AI-Infra-Guard/issues) - [Suggest a New Feature](https://github.com/Tencent/AI-Infra-Guard/issues) - ⭐ [Improve Documentation](https://github.com/Tencent/AI-Infra-Guard/pulls)
## Acknowledgements ### Gratitude to Contributing Developers Thanks to all the developers who have contributed to the A.I.G project, Your contributions have been instrumental in making A.I.G a more robust and reliable AI Red Team platform.
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### Appreciation for Our Users We are deeply grateful to the following teams and organizations for their trust, and valuable feedback in using A.I.G.
### Thanks to Our Stargazers! We are deeply grateful to all the developers who have starred our repository!
⭐ Every star encourages us to keep improving and innovating! ⭐
Help us reach more developers by starring this repository.
## Join the Community ### Online Discussions - **GitHub Discussions**: [Join our community discussions](https://github.com/Tencent/AI-Infra-Guard/discussions) - **Issues & Bug Reports**: [Report issues or suggest features](https://github.com/Tencent/AI-Infra-Guard/issues) ### Discussion Community
| WeChat Group | Discord [link] |
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## Citation If you use A.I.G in your research or product, please cite: \`\`\`bibtex @misc\{Tencent_AI-Infra-Guard_2025, author=\{\{Tencent Zhuque Lab\}\}, title=\{\{AI-Infra-Guard: A Comprehensive, Intelligent, and Easy-to-Use AI Red Teaming Platform\}\}, year=\{2025\}, howpublished=\{GitHub repository\}, url=\{https://github.com/Tencent/AI-Infra-Guard\} \} \`\`\`
## Related Papers We are deeply grateful to the research teams who have used A.I.G in their academic work and contributed to advancing AI security research: [1] Yongjian Guo, Puzhuo Liu, et al. **"Systematic Analysis of MCP Security."** arXiv preprint arXiv:2508.12538 (2025). [[pdf]](https://arxiv.org/abs/2508.12538) [2] Zexin Wang, Jingjing Li, et al. **"A Survey on AgentOps: Categorization, Challenges, and Future Directions."** arXiv preprint arXiv:2508.02121 (2025). [[pdf]](https://arxiv.org/abs/2508.02121) [3] Yixuan Yang, Daoyuan Wu, Yufan Chen. **"MCPSecBench: A Systematic Security Benchmark and Playground for Testing Model Context Protocols."** arXiv preprint arXiv:2508.13220 (2025). [[pdf]](https://arxiv.org/abs/2508.13220) [4] Ping He, Changjiang Li, et al. **"Automatic Red Teaming LLM-based Agents with Model Context Protocol Tools."** arXiv preprint arXiv:2509.21011 (2025). [[pdf]](https://arxiv.org/abs/2509.21011) If you have used A.I.G in your research, we would love to hear from you! [Contact us here](#-join-the-community).
## License This project is licensed under the **MIT License**. See the [License.txt](./License.txt) file for details.
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