tencent/ai-infra-guard

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🚀 AI Red Teaming Platform by Tencent Zhuque Lab

A.I.G (AI-Infra-Guard) integrates capabilities such as ClawScan(OpenClaw Security Scan), Agent Scan，AI infra vulnerability scan, MCP Server & Agent Skills 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!

📋 User Feedback Survey

Help us improve A.I.G! Please take 3-5 minutes to fill out our User Feedback Survey. Users who provide high-quality feedback and leave a valid email address will receive an exclusive Tencent souvenir gift.

🚀 What’s New

• 2026-06-25 · v4.1.15 — MCP Scan: 3 new threat detection rules (tool poisoning, credential exfiltration, command injection); 6 new llama.cpp CVE rules; model.token now optional with system default fallback.
• 2026-06-18 · v4.1.14 — Prompt Security: 9 new single-turn jailbreak operators; new aig-agent-redteam skill for comprehensive Agent red-team assessment.
• 2026-06-11 · v4.1.13 — New version check API endpoint; security scoring switched to absolute deduction model for clearer risk assessment.
• 2026-06-08 · v4.1.12 — Fingerprint library expanded: 39 new AI Web fingerprints added, 18 existing fingerprints enhanced.
• 2026-05-28 · v4.1.10 — Coverage expanded to 68 AI components (added junoclaw, lollms, sglang); 600+ new CVE rules; WebSocket agent provider support for Agent Scan.

👉 Earlier releases · 🔍 aig-skill-scan · 📊 SkillTrustBench · 🩺 EdgeOne ClawScan

Table of Contents

• 🚀 Quick Start
• ✨ Features
• 🖼️ Showcase
• 📖 User Guide
• 🔧 API Documentation
• 🏗️ Architecture Evolution
• 📝 Contribution Guide
• 🛡️ About the Team
• 🙏 Acknowledgements
• 💬 Join the Community
• 📖 Citation
• 📚 Papers
• ⚖️ License & Attribution

🚀 Quick Start

🐳 Deploy A.I.G with Docker

# This method pulls pre-built images from Docker Hub for a faster start
git clone https://github.com/Tencent/AI-Infra-Guard.git
cd AI-Infra-Guard
# For Docker Compose V2+, replace 'docker-compose' with 'docker compose'
docker-compose -f docker-compose.images.yml up -d

Once the service is running, you can access the A.I.G web interface at: http://localhost:8088

Use from OpenClaw

You can also call A.I.G directly from OpenClaw chat via the aig-scanner skill.

clawhub install aig-scanner

Then configure AIG_BASE_URL to point to your running A.I.G service.

For more details, see the aig-scanner README.

⚡ Install aig-skill-scan with a Single Command

Agent Skill security audit tool, easily integrated into enterprise CI/CD pipelines. Vulnerability classification aligns with SkillTrustBench T01–T09 taxonomy. Learn more →

pip install aig-skill-scan

# Set API key via environment variable
export LLM_API_KEY="your-api-key"

# Scan a local Skill project directory
aig-skill-scan --repo /path/to/your/skill \
           -m deepseek-v4-flash \
           --language en \
           -o result.json

🌟 Try the Online Pro Version

Experience the Pro version with advanced features and improved performance. The Pro version requires an invitation code and is prioritized for contributors who have submitted issues, pull requests, or discussions, or actively help grow the community. Visit: https://aigsec.ai/.

✨ Features

🔍 aig-skill-scan Performance & Coverage

Performance on SkillTrustBench with different LLMs:

Covers 9 categories of Skill security risks (SkillTrustBench T01–T09):

For full leaderboard and details, visit SkillTrustBench.

🔬 Security Scanning & Evaluation

🖼️ Showcase

A.I.G Main Interface

Plugin Management

🗺️ Quick Usage Guide

After deployment, open http://localhost:8088 in your browser.

AI Infrastructure Vulnerability Scan

What to enter as the target URL / IP?

The target is the network address of a running AI service you want to scan - not a GitHub URL or source code path. A.I.G connects to the live service and fingerprints it for known CVE vulnerabilities.

Step-by-step: Scan a local vLLM instance

1. Start vLLM normally (e.g. python -m vllm.entrypoints.api_server --model meta-llama/...)
2. In the A.I.G web UI, click “AI基础设施安全扫描 / AI Infra Scan”
3. Enter http://127.0.0.1:8000 (or the IP/port where vLLM is listening)
4. Click Start Scan - A.I.G will fingerprint the service and match it against 1900+ known CVEs
5. View the report: component version, matched vulnerabilities, severity, and remediation links

💡 Tip: To scan the nightly build of vLLM specifically, just run that nightly build and point A.I.G at its address. The scanner detects the version automatically.

MCP Server & Agent Skills Scan

Enter either a remote URL (e.g. https://github.com/user/mcp-server) or upload a local source archive - no running instance required.

Jailbreak Evaluation

Configure the target LLM’s API endpoint (base URL + API key) in Settings → Model Config, then select a dataset and start the evaluation.

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📖 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

🛡️ About the Team

This project is led and developed by Tencent Zhuque Lab, part of the Tencent Security Platform Department. Founded in 2019, Tencent Zhuque Lab is a top-tier security research lab focused on real-world offensive and defensive research and frontier technology in the AI security space, covering large model security, AI agent security, AI-empowered security, and AI-generated content detection.

The team has helped major vendors such as NVIDIA, Google, and Microsoft, as well as open-source communities like OpenClaw, Linux, and Hugging Face, fix a large number of high-risk vulnerabilities, and has been publicly acknowledged by them.

We have released open-source AI security products including the AI Red Team Security Testing Platform A.I.G (AI-Infra-Guard) and the Zhuque AI Detection Assistant. Our research has been widely published at top international security and AI conferences such as Black Hat, DEF CON, ICLR, CVPR, NeurIPS, and ACL, and we have authored the book “AI Security: Technology and Practice”.

👥 Core Members & Contributions

🙏 Acknowledgements

🎓 Academic Collaborations

We thank our academic partners for their research contributions and technical support.

👥 Gratitude to Contributing Developers

Thanks to all the developers who have contributed to the A.I.G project.

🤝 Appreciation for Our Users

Thanks to the users from the following organizations and teams for using A.I.G and their valuable feedback.

💬 Join the Community

🌐 Online Discussions

• GitHub Discussions: Join our community discussions
• Issues & Bug Reports: Report issues or suggest features

📱 Discussion Community

📧 Contact Us

For collaboration inquiries or feedback, please contact us at: [email protected]

🔗 Recommended Security Tools

If you are interested in code security, check out A.S.E (AICGSecEval), the industry’s first repository-level AI-generated code security evaluation framework open-sourced by the Tencent Wukong Code Security Team.

📖 Citation

If you use A.I.G in your research, 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}
}

📚 Papers

1. “Securing the AI Agent: A Unified Framework for Multi-Layer Agent Red Teaming” — A comprehensive framework for securing AI agent systems through multi-layer red teaming across infrastructure, supply chain, runtime interaction, and deployment surfaces. [arXiv] [pdf]

2. “AI-Infra-Guard Technical Report” — Technical report covering architecture design, scanning engine, and assessment methodology. [pdf]

3. “AI-Infra-Guard: An AI Red Teaming Platform” — Black Hat Europe 2025 Arsenal presentation showcasing A.I.G’s capabilities and real-world use cases. [pdf]

4. “MCP Unchained: Compromising The AI Agent Ecosystem Via Its Universal Connector” — Black Hat Europe 2025 talk revealing security risks in the MCP protocol within the AI agent ecosystem. [pdf]

Thanks to the research teams who have cited A.I.G in their academic work (19 papers):

📧 If you have used A.I.G in your research or product, or if we have inadvertently missed your publication, we would love to hear from you! Contact us here.

⚖️ License & Attribution

This project is open-sourced under the Apache License 2.0. We warmly welcome and encourage community contributions, integrations, and derivative works, subject to the following attribution requirements:

1. Retain notices: You must retain the LICENSE and NOTICE files from the original project in any distribution.
2. Product attribution: If you integrate AI-Infra-Guard’s core code, components, or scanning engine into your open-source project, commercial product, or internal platform, you must clearly state the following in your product documentation, usage guide, or UI “About” page:

   “This project integrates AI-Infra-Guard, open-sourced by Tencent Zhuque Lab.”

3. Academic & article citation: If you use this tool in vulnerability analysis reports, security research articles, or academic papers, please explicitly mention “Tencent Zhuque Lab AI-Infra-Guard” and include a link to the repository.

Repackaging this project as an original product without disclosing its origin is strictly prohibited.