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Garak-MCP

Garak-MCP

Public
EdenYavin/Garak-MCP

Lightweight Model Context Protocol server enabling vulnerability scanning of various AI models through customizable attacks, probes, and detailed reporting across multiple model types including Ollama, OpenAI, HuggingFace, and GGML.

python
0 tools
May 29, 2025
Updated Jun 4, 2025

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MCP Server

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MCP Server For Garak LLM Vulnerability Scanner

A lightweight MCP (Model Context Protocol) server for Garak.

Example:

https://github.com/user-attachments/assets/f6095d26-2b79-4ef7-a889-fd6be27bbbda


Tools Provided

Overview

NameDescription
list_model_typesList all available model types (ollama, openai, huggingface, ggml)
list_modelsList all available models for a given model type
list_garak_probesList all available Garak attacks/probes
get_reportGet the report of the last run
run_attackRun an attack with a given model and probe

Detailed Description

  • list_model_types

    • List all available model types that can be used for attacks
    • Returns a list of supported model types (ollama, openai, huggingface, ggml)
  • list_models

    • List all available models for a given model type
    • Input parameters:
      • model_type (string, required): The type of model to list (ollama, openai, huggingface, ggml)
    • Returns a list of available models for the specified type
  • list_garak_probes

    • List all available Garak attacks/probes
    • Returns a list of available probes/attacks that can be run
  • get_report

    • Get the report of the last run
    • Returns the path to the report file
  • run_attack

    • Run an attack with the given model and probe
    • Input parameters:
      • model_type (string, required): The type of model to use
      • model_name (string, required): The name of the model to use
      • probe_name (string, required): The name of the attack/probe to use
    • Returns a list of vulnerabilities found

Prerequisites

  1. Python 3.11 or higher: This project requires Python 3.11 or newer.

    # Check your Python version python --version
  2. Install uv: A fast Python package installer and resolver.

    pip install uv

    Or use Homebrew:

    brew install uv
  3. Optional: Ollama: If you want to run attacks on ollama models be sure that the ollama server is running.

ollama serve

Installation

  1. Clone this repository:
git clone https://github.com/BIGdeadLock/Garak-MCP.git
  1. Configure your MCP Host (Claude Desktop ,Cursor, etc):
{ "mcpServers": { "garak-mcp": { "command": "uv", "args": ["--directory", "path-to/Garak-MCP", "run", "garak-server"], "env": {} } } }

Tested on:

  • Cursor
  • Claude Desktop

Future Steps

  • Add support for Smithery AI: Docker and config
  • Improve Reporting
  • Test and validate OpenAI models (GPT-3.5, GPT-4)
  • Test and validate HuggingFace models
  • Test and validate local GGML models
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