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Decision brief
AI-Infra-Guard is designed for organizations looking to secure their AI infrastructures comprehensively through various scanning and evaluation tools.
Good fit when
- If you need advanced LLM jailbreak evaluation capabilities specific to the vulnerabilities identified by Tencent's research, consider using AI-Infra-Guard.
- Suitable when your organization requires security assessments that go beyond just code but also cover skill assessments for AI-driven tasks or environments.
Avoid when
- Avoid if you are looking exclusively for a tool that focuses solely on the runtime behavior of LLMs without broader infrastructural scanning capabilities.
- Not recommended when your primary focus is on network-level security rather than comprehensive AI infrastructure security assessments and evaluations.
Observed Jul 17, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install AI-Infra-GuardSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Tencent/AI-Infra-Guard is a comprehensive security solution for AI ecosystems providing tools for agent scanning, skill assessment, and LLM jailbreak evaluation to ensure the integrity and safety of AI infrastructures.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Jul 28, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Jul 28, 2026
- Languages
- python
Source: github.language · Jul 28, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 28, 2026)
1. Start vLLM normally (e.g. `python -m vllm.entrypoints.api_server --model meta-llama/...`)Source link
Tags
README
🐳 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
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.
More installation options
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
⚡ 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
---
### 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.
| Scenario | Example target |
|:---------|:--------------|
| A locally running vLLM instance | `http://127.0.0.1:8000` |
| An Ollama server on your LAN | `http://192.168.1.100:11434` |
| A ComfyUI instance exposed internally | `http://10.0.0.5:8188` |
| Multiple hosts (one per line) | `192.168.1.0/24` (CIDR), `10.0.0.1-10.0.0.20` (range) |
**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.
---
## ⚖️ 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
For agents
This page has a .md twin and JSON over the API.