Ai Infra Guard

🚀 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
• 🖼️ Showcase
• 🚀 Quick Start
• 📖 User Guide
• 🔧 API Documentation
• 📝 Contribution Guide
• 🙏 Acknowledgements
• 💬 Join the Community
• 📖 Citation
• 📚 Related Papers
• 📄 License

✨ Features

🖼️ Showcase

A.I.G Main Interface

AI Infra Scan

MCP Server Scan

Jailbreak Evaluation

Plugin Management

🚀 Quick Start

Deployment with Docker

System Requirements

1. One-Click Install Script （Recommended）

# 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)

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

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://0.0.0.0:8088

Note: The AI-Infra-Guard project is positioned as an AI security risk platform for internal use by enterprises or individuals. It currently lacks an authentication mechanism and should not be deployed on public networks.

📖 User Guide

Visit our online documentation: https://tencent.github.io/AI-Infra-Guard/

For more detailed FAQs and troubleshooting guides, visit our documentation.

🔧 API Documentation

A.I.G provides a comprehensive set of task creation APIs that support AI infra scan, MCP Server Scan, and Jailbreak Evaluation capabilities.

After the project is running, visit http://localhost:8088/docs/index.html to view the complete API documentation.

For detailed API usage instructions, parameter descriptions, and complete example code, please refer to the Complete API Documentation.

📝 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
• 💡 Suggest a New Feature
• ⭐ Improve Documentation

🙏 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.

🤝 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!

Thank you to users from Google, Microsoft, Amazon, ByteDance, Alibaba, Huawei, Meituan, Douban, Peking University, Tsinghua University, HFUT, cuit, and many more amazing stargazers!

⭐ 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
• Issues & Bug Reports: Report issues or suggest features

📱 Discussion Community

WeChat Group

Discord

📧 Contact Us

For collaboration inquiries or feedback, please contact us at: zhuque@tencent.com

📖 Citation

If you use A.I.G in your research or product, please cite:

@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)

[5] Weibo Zhao, Jiahao Liu, Bonan Ruan et al. "When MCP Servers Attack: Taxonomy, Feasibility, and Mitigation." arXiv preprint arXiv:2509.24272v1 (2025). [[pdf]](http://arxiv.org/abs/2509.24272v1)

[6] Bin Wang, Zexin Liu, Hao Yu et al. "MCPGuard : Automatically Detecting Vulnerabilities in MCP Servers." arXiv preprint arXiv:22510.23673v1 (2025). [[pdf]](http://arxiv.org/abs/2510.23673v1)

📧 If you have used A.I.G in your research, we would love to hear from you! Contact us here.

📄 License

This project is licensed under the MIT License. See the License.txt file for details.