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.
Precisely identifies 30+ AI framework components |
Powered by AI Agent |
Rapidly assesses Prompt security risks |
System Requirements
- Docker 20.10 or higher
- At least 4GB of available RAM
- At least 10GB of available disk space
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://localhost:8088
Directory Structure
Directory/File | Description | Mount Path |
---|---|---|
uploads/ |
Uploads directory | /ai-infra-guard/uploads |
db/ |
Database file directory | /ai-infra-guard/db |
data/ |
Knowledge base data directory (fingerprints, vulnerabilities) | /ai-infra-guard/data |
logs/ |
Application log directory | /ai-infra-guard/logs |
The extensible plugin framework serves as A.I.G's architectural cornerstone, inviting community innovation through Plugin and Feature contributions.
- 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. - 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.
We extend deep gratitude to these open-source developers:
Thanks to all the developers who have contributed code to the A.I.G project:
For collaboration inquiries or feedback, please contact us at: zhuque(at)tencent.com
This project is licensed under the MIT License. See the License.txt file for details.