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MCP Server For Garak LLM Vulnerability Scanner

@EdenYavin

代码仓库

基本信息

分类

AI 与智能体

许可证

MIT license

传输方式

stdio

作者

EdenYavin

关于 MCP Server For Garak LLM Vulnerability Scanner

MCP Server for using Garak LLM vulnerability scanner

MCP Server For Garak LLM Vulnerability Scanner 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "garak-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "path-to/Garak-MCP",
        "run",
        "garak-server"
      ]
    }
  }
}

MCP Server For Garak LLM Vulnerability Scanner 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is MCP Server For Garak LLM Vulnerability Scanner?

A lightweight MCP server that integrates the Garak LLM vulnerability scanner, enabling AI assistants to list model types, models, probes, run attacks, and retrieve reports. It is intended for developers and security researchers assessing LLM robustness.

How to use MCP Server For Garak LLM Vulnerability Scanner?

Install prerequisites (Python 3.11+, uv), clone the repository, then configure your MCP host (e.g., Cursor, Claude Desktop) with the provided JSON entry pointing to the garak-server script via uv run. Invoke tools like run_attack with required parameters (model_type, model_name, probe_name).

Key features of MCP Server For Garak LLM Vulnerability Scanner

  • List available model types (ollama, openai, huggingface, ggml)
  • List all available models for a given type
  • List all available Garak probes/attacks
  • Run an attack with specified model and probe
  • Get the report path of the last run

Use cases of MCP Server For Garak LLM Vulnerability Scanner

  • Security audit of a local Ollama model against known probes
  • Automated vulnerability scanning of OpenAI or HuggingFace models
  • Batch testing of multiple probes on a single model
  • Integration with Cursor or Claude Desktop for on-demand scanning
  • Generating and retrieving detailed attack reports

常见问题

1

What prerequisites are needed?

Python 3.11 or higher, `uv` (install via `pip install uv` or Homebrew), and optionally Ollama running (`ollama serve`) for local models.

2

Where does the report data live?

The `get_report` tool returns the path to the report file from the last run; the README does not specify the exact directory.

3

Which MCP hosts are supported?

Tested on Cursor and Claude Desktop; configuration JSON is provided for these.

4

What transport or authentication is used?

The README does not mention transport or authentication details. It uses the MCP protocol, likely stdio transport via the `uv` command.

5

Are there known limitations?

Future steps include Docker/Smithery support, improved reporting, and validation for OpenAI, HuggingFace, and local GGML models (currently untested).

评论

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