📖 Documentation | 🌐 🇬🇧 English · 🇨🇳 中文
A.I.G (AI-Infra-Guard) brings together OpenClaw security checks, Agent security scanning, AI tool and skill scanning, AI infrastructure vulnerability scanning, and LLM security assessment (jailbreak evaluation), aiming to provide the most comprehensive, intelligent, and easy-to-use way to check AI security risks.
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!
- 🚀 Quick Start
- 🧰 Standalone Use
- 🖼️ Showcase
- 📖 User Guide
- 🏗️ Architecture Evolution
- 📝 Contribution Guide
- 🛡️ About the Team
- 🙏 Acknowledgements
- 💬 Join the Community
- 📖 Citation
- 📄 License
| Docker | RAM | Disk Space |
|---|---|---|
| 20.10 or higher | 4GB+ | 10GB+ |
# 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 -dOnce the service is running, you can access the A.I.G web interface at:
http://localhost:8088
Method 2: 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 | bashMethod 3: Build and run from source
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
# (For Docker Compose V2+, replace 'docker-compose' with 'docker compose')
docker-compose up -dNote: The AI-Infra-Guard project is positioned as an AI red teaming platform for internal use by enterprises or individuals. It currently lacks an authentication mechanism and should not be deployed on public networks.
For more information, see: https://tencent.github.io/AI-Infra-Guard/?menu=getting-started
The tools below can be used from the command line without starting the web service. Only the shortest usage is shown. See each README for parameters and examples.
Scan a domain, IP, or URL and match fingerprints and vulnerabilities.
pip install aig-infra-scan
aig-infra-scan -t https://example.comOpen a local page to check whether a model refuses unsafe prompts, and optionally scan infrastructure. Evaluation sets live in data/eval. Run from the repository root, or set AIG_EVAL_DIR.
pip install aig-compliance
aig-complianceEvaluate a model that exposes an OpenAI-compatible API.
pip install aig-model-scan
aig-model-scan \
--model "gpt-4o-mini" \
--base_url "https://api.openai.com/v1" \
--api_key "your-api-key" \
--max_concurrent 10 \
--scenarios Bias \
--techniques RawScan an MCP server source directory, or connect to a service that is already running.
pip install aig-mcp-scan
export LLM_API_KEY="your-api-key"
aig-mcp-scan --repo /path/to/mcp-server -o result.sarif.jsonAudit a local Agent Skill directory.
pip install aig-skill-scan
export LLM_API_KEY="your-api-key"
aig-skill-scan --repo /path/to/your/skill -o result.sarif.jsonSend test prompts to a running Agent. target.yaml describes the Agent under test. The API key is for the model used by the scanner.
pip install aig-agent-scan
export LLM_API_KEY="your-api-key"
aig-agent-scan --agent_provider target.yaml -o result.json
Visit our online documentation: https://tencent.github.io/AI-Infra-Guard/
For more detailed FAQs and troubleshooting guides, visit our documentation.
One of A.I.G's core strengths is its rich plugin system, which can be configured quickly. We welcome high-quality plugins and features from the community.
- Fingerprint Rules: Add new YAML fingerprint files to the
data/fingerprints/directory. - Vulnerability Rules: Add new vulnerability scan rules to the
data/vuln/directory. - MCP Plugins: Add new MCP security scan rules to the
data/mcp/directory. - Model Evaluation Datasets: Add new model evaluation datasets to the
data/evaldirectory.
Please refer to the existing rule formats, create new files, and submit them via a Pull Request.
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".
| Role | Member | Contribution |
|---|---|---|
| Head of Tencent Security Platform Department | Yong Yang | Initiated A.I.G and proposed automated assessment of AI agent loss-of-control risks, guiding the platform's expansion from AI infrastructure vulnerability scanning to agent execution risk, tool misuse, and permission-boundary evaluation. |
| Head of Tencent Zhuque Lab | Xing Zheng | Proposed the automated vulnerability-update and benchmark-alignment mechanism, helping AI Infra fingerprints, CVE/GHSA rules, and benchmarks iterate continuously. |
| Project Lead | Nicky | Frontier security research, product planning, technical-route decisions, internal and external collaboration, and communications. |
| Technical Lead | Python | Overall architecture design, core module development, and version iteration. |
| Core Contributor | Zona | Frontend interaction, product experience, community operations, and user-feedback loop. |
| Core Contributor | Fyoung | AI Infra vulnerability component fingerprint updates and Benchmark system construction. |
| Core Contributor | Xiangfan | Security capability development for Skill risks and agent loss-of-control scenarios. |
| Core Contributor | Elwood | Enhancing Agent security scanning capabilities and updating technical reports. |
| Core Contributor | Robert | LLM safety assessment and jailbreak-evaluation strategy operations. |
| Core Contributor | Zoe | LLM safety assessment, jailbreak evaluation, and model-integration module development. |
| Contributor | Ronin | Participated in AI agent security scanning development. |
| Contributor | Rsin | Participated in community operations and campaign communications. |
We thank our academic partners for their research contributions and technical support.
Prof. hui Li |
Bin Wang |
Zexin Liu |
Hao Yu |
Ao Yang |
Zhengxi Lin |
Prof. Zhemin Yang |
Kangwei Zhong |
Jiapeng Lin |
Cheng Sheng |
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- GitHub Discussions: Join our community discussions
- Issues & Bug Reports: Report issues or suggest features