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MCP Server For Garak LLM Vulnerability Scanner

@EdenYavin

リポジトリ

基本情報

カテゴリ

AI とエージェント

ライセンス

MIT license

トランスポート

stdio

作者

EdenYavin

MCP Server For Garak LLM Vulnerability Scanner について

MCP Server for using Garak LLM vulnerability scanner

MCP Server For Garak LLM Vulnerability Scanner の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

MCP Server For Garak LLM Vulnerability Scanner のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

よくある質問

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).

コメント

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