AI-Infra-Guard
经 AI Skill Hub 精选评估，AI-Infra-Guard 获评「推荐使用」。已获得 3.9k 颗 GitHub Star，这款MCP工具在功能完整性、社区活跃度和易用性方面表现出色，AI 评分 7.5 分，适合有一定技术背景的用户使用。
📚 深度解析
通过安装 AI-Infra-Guard，你的 AI 助手将获得额外的工具调用能力，可以用自然语言直接操控该工具的功能，无需学习复杂的命令行语法。MCP 工具的核心价值在于"一次配置，永久增强"——配置完成后，每次与 AI 对话时都可以无缝调用这些工具。
在技术实现上，MCP 工具通过标准的 JSON-RPC 协议与 AI 客户端通信，工具的功能以"工具列表"的形式暴露给 AI 模型，AI 可以按需调用。AI-Infra-Guard 提供了结构化的工具调用接口，使 AI 模型能够精确地理解和使用每个功能点，显著降低 AI 在工具使用上的错误率。
与传统的 API 集成相比，MCP 工具的优势在于无需编写代码——用户只需在配置文件中添加几行 JSON，即可让 AI 获得全新能力。AI Skill Hub 将 AI-Infra-Guard 评为 AI 评分 7.5 分，属于同类工具中的优质选择。
📋 工具概览
AI-Infra-Guard 是一款遵循 MCP（Model Context Protocol）标准协议的 AI 工具扩展。通过 MCP 协议，它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务，实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用，都可以通过自然语言在 AI 对话中直接触发，极大提升生产效率。
📖 中文文档
AI-Infra-Guard 是一款遵循 MCP（Model Context Protocol）标准协议的 AI 工具扩展。通过 MCP 协议，它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务，实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用，都可以通过自然语言在 AI 对话中直接触发，极大提升生产效率。
- 通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
- 提供结构化工具调用接口，显著降低 AI 集成复杂度
- 支持 Claude Desktop 和 Claude Code 无缝接入，开箱即用
- 可与其他 MCP 工具组合叠加，构建完整 AI 工作站
- 轻量无侵入设计，不影响现有系统架构
- 在 Claude Desktop 对话中直接调用本地工具，实现 AI 与系统的深度联动
- 通过自然语言驱动复杂的多步骤自动化任务，代替繁琐手动操作
- 将多个 MCP 工具组合使用，构建个人专属 AI 工作站
# 方式一：通过 Claude Code CLI 一键安装
claude skill install https://github.com/Tencent/AI-Infra-Guard
# 方式二：手动配置 claude_desktop_config.json
{
"mcpServers": {
"ai-infra-guard": {
"command": "npx",
"args": ["-y", "ai-infra-guard"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
- 确认已安装 Node.js（v18 或以上版本）
- 打开 Claude Desktop 或 Claude Code 的 MCP 配置文件
- 按「交给 Agent 安装 → Claude Desktop」标签中的 JSON 配置填入 mcpServers 字段
- 保存配置文件并重启 Claude 客户端
- 重启后，在对话中即可使用本工具
# 安装后在 Claude 对话中直接使用 # 示例： 用户: 请帮我用 AI-Infra-Guard 执行以下任务... Claude: [自动调用 AI-Infra-Guard MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入："列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"ai-infra-guard": {
"command": "npx",
"args": ["-y", "ai-infra-guard"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
简介
<p align="center"> <h1 align="center"><img vertical-align="middle" width="400px" src="img/logo-full-new.png" alt="A.I.G"/></h1> </p> <p align="center"> <a href="https://tencent.github.io/AI-Infra-Guard/">📖 Documentation</a> | 🌐 <a href="./readme/README_ZH.md">🇨🇳 中文</a> · <a href="./readme/README_JA.md">🇯🇵 日本語</a> · <a href="./readme/README_ES.md">🇪🇸 Español</a> · <a href="./readme/README_DE.md">🇩🇪 Deutsch</a> · <a href="./readme/README_FR.md">🇫🇷 Français</a> · <a href="./readme/README_KR.md">🇰🇷 한국어</a> · <a href="./readme/README_PT.md">🇧🇷 Português</a> · <a href="./readme/README_RU.md">🇷🇺 Русский</a> </p> <p align="center"> <a href="https://github.com/tencent/AI-Infra-Guard/stargazers"> <img src="https://img.shields.io/github/stars/tencent/AI-Infra-Guard?style=social" alt="GitHub stars"> </a> <a href="https://github.com/Tencent/AI-Infra-Guard"> <img alt="GitHub downloads" src="https://img.shields.io/github/downloads/Tencent/AI-Infra-Guard/total"> </a> <a href="https://github.com/Tencent/AI-Infra-Guard"> <img alt="docker pulls" src="https://img.shields.io/docker/pulls/zhuquelab/aig-server.svg?color=gold"> </a> <a href="https://github.com/Tencent/AI-Infra-Guard"> <img alt="Release" src="https://img.shields.io/github/v/release/Tencent/AI-Infra-Guard?color=green"> </a> <a href="https://deepwiki.com/Tencent/AI-Infra-Guard"> <img src="https://deepwiki.com/badge.svg" alt="Ask DeepWiki"> </a> </p> <p align="center"> <a href="https://clawhub.ai/aigsec/edgeone-clawscan" target="_blank"> <img src="https://img.shields.io/badge/ClawHub-EdgeOne%20ClawScan-a870dc" alt="EdgeOne ClawScan"> </a> <a href="https://clawhub.ai/aigsec/edgeone-skill-scanner" target="_blank"> <img src="https://img.shields.io/badge/ClawHub-EdgeOne%20Skill%20Scanner-2ea44f" alt="EdgeOne Skill Scanner"> </a> <a href="https://clawhub.ai/aigsec/aig-scanner" target="_blank"> <img src="https://img.shields.io/badge/ClawHub-AIG%20Scanner-e6a817" alt="AIG Scanner"> </a> </p> <p align="center"> <a href="https://trendshift.io/repositories/13637" target="_blank"><picture><source media="(prefers-color-scheme: dark)" srcset="https://trendshift.io/api/badge/repositories/13637"><source media="(prefers-color-scheme: light)" srcset="https://trendshift.io/api/badge/repositories/13637"><img src="https://trendshift.io/api/badge/repositories/13637" alt="Tencent%2FAI-Infra-Guard | Trendshift" width="250" height="55"/></picture></a> <a href="https://www.blackhat.com/eu-25/arsenal/schedule/index.html#aigai-infra-guard-48381" target="_blank"><img src="img/blackhat.png" alt="Tencent%2FAI-Infra-Guard | blackhat" width="175" height="55"/></a> <a href="https://github.com/deepseek-ai/awesome-deepseek-integration" target="_blank"><img src="img/awesome-deepseek.png" alt="Tencent%2FAI-Infra-Guard | awesome-deepseek-integration" width="273" height="55"/></a> </p>
<br>
<p align="center"> <h2 align="center">🚀 AI Red Teaming Platform by Tencent Zhuque Lab</h2> </p>
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.
<p> 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! </p> <p align="center"> <a href="https://github.com/Tencent/AI-Infra-Guard"> <img src="https://img.shields.io/badge/⭐-Give%20us%20a%20Star-yellow?style=for-the-badge&logo=github" alt="Give us a Star"> </a> </p>
<br>
🛡️ 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".
🚀 What's New
- 2026-06-08 · v4.1.12 — Fingerprint library expanded: 39 new AI Web fingerprints added, 18 existing fingerprints enhanced.
- 2026-06-04 · v4.1.11 — New trusted-by endorsements: Wuhan University and Unicom Digital Tech.
- 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.
- 2026-05-21 · v4.1.9 — Prompt Security: 26 new attack operators (20 single-turn + 6 multi-turn); scanning agents hardened against indirect prompt injection.
- 2026-05-14 · v4.1.8 — Coverage expanded to 64 AI components (6 new: InstructLab, LMDeploy, SuperAGI, Pipecat, Paperclip, QnABot); vuln database deduplicated and cleaned.
- 2026-04-23 · v4.1.6 — Coverage expanded to 58 AI components (added FastGPT, Upsonic); vuln database refreshed across 7 components.
- 2026-04-23 · v4.1.5 — Detects exposed AI agent config files (13 paths); manual update for jailbreak datasets and vuln databases.
- 2026-04-17 · v4.1.4 — HTTPS model endpoints with self-signed certificates now supported.
- 2026-04-09 · v4.1.3 — Coverage expanded to 55 AI components; added crewai, kubeai, lobehub.
- 2026-04-03 · v4.1.2 — Three new skills on ClawHub (
edgeone-clawscan,edgeone-skill-scanner,aig-scanner) + manual task stop. - 2026-03-25 · v4.1.1 — ☠️ Detects LiteLLM supply chain attack (CRITICAL); added Blinko & New-API coverage.
- 2026-03-23 · v4.1 — OpenClaw vulnerability database expanded with 281 new CVE/GHSA entries.
- 2026-03-10 · v4.0 — Launched EdgeOne ClawScan (OpenClaw Security Scan) and Agent-Scan framework.
✨ Features
| Feature | More Info |
|---|---|
| **ClawScan(OpenClaw Security Scan)** | Supports one-click evaluation of OpenClaw security risks. It detects insecure configurations, Skill risks, CVE vulnerabilities, and privacy leakage. |
| **Agent Scan** | This is an independent, multi-agent automated scanning framework. It is designed to evaluate the security of AI agent workflows. It seamlessly supports agents running across various platforms, including Dify and Coze. |
| **MCP Server & Agent Skills scan** | It thoroughly detects 14 major categories of security risks. The detection applies to both MCP Servers and Agent Skills. It flexibly supports scanning from both source code and remote URLs. |
| **AI infra vulnerability scan** | This scanner precisely identifies over 100 AI framework components. It covers more than 1600 known CVE vulnerabilities. Supported frameworks include Ollama, ComfyUI, vLLM, n8n, Triton Inference Server and more. |
| **Jailbreak Evaluation** | It assesses prompt security risks using carefully curated datasets. The evaluation applies multiple attack methods to test robustness. It also provides detailed cross-model comparison capabilities. |
<details> <summary><strong>💎 Additional Benefits</strong></summary>
- 🖥️ Modern Web Interface: User-friendly UI with one-click scanning and real-time progress tracking - 🔌 Complete API: Full interface documentation and Swagger specifications for easy integration - 🤖 Agent-Ready: Plug-and-play agent skills on ClawHub — EdgeOne ClawScan, EdgeOne Skill Scanner, and AIG Scanner — seamlessly embed security scanning into any AI agent workflow - 🌐 Multi-Language: Chinese and English interfaces with localized documentation - 🐳 Cross-Platform: Linux, macOS, and Windows support with Docker-based deployment - 🆓 Free & Open Source: Completely free under the Apache 2.0 license </details>
<br />
Deployment with Docker
| Docker | RAM | Disk Space |
|---|---|---|
| 20.10 or higher | 4GB+ | 10GB+ |
```bash
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 <br>
Other Installation Methods
Method 2: One-Click Install Script （Recommended） ```bash
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**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
</details>
🚀 Quick Start
🗺️ Quick Usage Guide
After deployment, open http://localhost:8088 in your browser.
📖 User Guide
Visit our online documentation: https://tencent.github.io/AI-Infra-Guard/
For more detailed FAQs and troubleshooting guides, visit our documentation. <br /> <br>
📝 Contribution Guide
The extensible plugin framework serves as A.I.G's architectural cornerstone, inviting community innovation through Plugin and Feature contributions.
🖼️ Showcase
A.I.G Main Interface
🔧 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. <br /> <br>
Plugin Management
<br />
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. - Jailbreak Evaluation Datasets: Add new Jailbreak evaluation datasets to the
data/evaldirectory.
Please refer to the existing rule formats, create new files, and submit them via a Pull Request.
AI-Infra-Guard是一个开源的MCP工具，用于AI安全评估和红队演练，具有全栈AI红队平台的特点，能够安全地保护AI生态系统。
⚡ 核心功能
- 通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
- 提供结构化工具调用接口，显著降低 AI 集成复杂度
- 支持 Claude Desktop 和 Claude Code 无缝接入，开箱即用
- 可与其他 MCP 工具组合叠加，构建完整 AI 工作站
- 轻量无侵入设计，不影响现有系统架构
👥 适合人群
🎯 使用场景
- 在 Claude Desktop 对话中直接调用本地工具，实现 AI 与系统的深度联动
- 通过自然语言驱动复杂的多步骤自动化任务，代替繁琐手动操作
- 将多个 MCP 工具组合使用，构建个人专属 AI 工作站
⚖️ 优点与不足
- +Apache-2.0 协议，可免费商用
- +标准化 MCP 协议，生态互联性强
- +与 Claude 官方生态无缝对接
- +即插即用，配置简单快捷
- −依赖 Claude 客户端，非 Claude 用户无法使用
- −MCP 协议仍在持续演进，接口可能变更
- −需要一定的配置步骤
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议，可商用，需保留版权声明和 NOTICE 文件，含专利授权条款。
❓ 常见问题 FAQ
AI Skill Hub 点评：AI-Infra-Guard 的核心功能完整，质量良好。对于Claude Desktop / Claude Code 用户来说，这是一个值得纳入个人工具库的选择。建议先在非生产环境试用，再逐步推广。
| 原始名称 | AI-Infra-Guard |
| Topics | mcpagentagent-securityai-infraai-red-teamingai-securitypython |
| GitHub | https://github.com/Tencent/AI-Infra-Guard |
| License | Apache-2.0 |
| 语言 | Python |
收录时间：2026-06-11 · 更新时间：2026-06-11 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。