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.
Table of Contents
🚀 Quick Start
🐳 Deploy A.I.G with Docker
| 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 -d
Once the service is running, you can access the A.I.G web interface at:
http://localhost:8088
Other Installation Methods
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 | bash
Method 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 -d
Note: 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
🧰 Standalone Use
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.
AI Infrastructure Scan
Scan a domain, IP, or URL and match fingerprints and vulnerabilities.
pip install aig-infra-scan
aig-infra-scan -t https://example.com
View README
AI Safety Compliance Check
Open 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-compliance
View README
LLM Security Assessment / Jailbreak
Evaluate 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 Raw
View README
MCP Scan
Scan 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.json
View README
Skill Scan
Audit 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.json
View README
Agent Scan
Send 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
View README
🖼️ Showcase
A.I.G Main Interface
📖 User Guide
Visit our online documentation: https://tencent.github.io/AI-Infra-Guard/
For more detailed FAQs and troubleshooting guides, visit our documentation.
📝 Contribution Guide
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.
Plugin Contribution Rules
- 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/eval directory.
Please refer to the existing rule formats, create new files, and submit them via a Pull Request.
Other Ways to Contribute
🛡️ 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.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
