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

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

2. 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

Running Vulnerability Scans

You can run Garak vulnerability scans directly using the included CLI tool.

Prerequisites for Scanning

1. Ollama must be running:

   ollama serve
   

2. Pull a model to scan:

   ollama pull llama2
   

Using the CLI Scanner

After installation, you can use the garak-scan command:

# List available Ollama models
uv run garak-scan --list-models

# Scan a specific model with all probes
uv run garak-scan --model llama2

# Scan with specific probes
uv run garak-scan --model llama2 --probes encoding

# Scan with custom output directory
uv run garak-scan --model llama2 --output-dir ./my_scans

# Run multiple parallel attempts
uv run garak-scan --model llama2 --parallel-attempts 4

Scan Results

Scan results are saved in the output/ directory (or your specified directory) as JSONL files. Each scan creates a timestamped report file:

output/scan_llama2_20250125_143022.report.jsonl

GitHub Actions Integration

This repository includes a GitHub Actions workflow that automatically runs vulnerability scans:

• Triggers: Push to main/master, pull requests, weekly schedule (Mondays at 2am UTC)
• Manual runs: Go to Actions → Garak LLM Vulnerability Scan → Run workflow
• Custom options: Specify model and probes when running manually
• Results: Scan results are uploaded as workflow artifacts

To enable automated scanning:

1. Ensure the workflow file exists at .github/workflows/garak-scan.yml
2. Push to your repository
3. Check the Actions tab to view scan results

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