MCP Server For Garak LLM Vulnerability Scanner

@EdenYavin

About MCP Server For Garak LLM Vulnerability Scanner

MCP Server for using Garak LLM vulnerability scanner

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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

FAQ from MCP Server For Garak LLM Vulnerability Scanner

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.

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.

Which MCP hosts are supported?

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

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.

Are there known limitations?

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

Frequently asked questions

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.

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.

Which MCP hosts are supported?

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

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.

Are there known limitations?

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

Comments

Basic information

More AI & Agents MCP servers

Mcp Agent

lastmile-ai

Build effective agents using Model Context Protocol and simple workflow patterns

8.4K

Transform MCP

Unstructured

The Unstructured Transform MCP Server connects your ADK agent to Unstructured, a document processing platform that turns raw files into structured, AI-ready data. This integration gives your agent the ability to parse PD

Intervals.icu MCP Server

mvilanova

Model Context Protocol (MCP) server for connecting Claude and ChatGPT with the Intervals.icu API.

311

Solon Ai

opensolon

Java AI application development framework (supports LLM-tool,skill; RAG; MCP; Agent-ReAct,Team-Agent). Compatible with java8 ~ java25. It can also be embedded in SpringBoot, jFinal, Vert.x, Quarkus, and other frameworks.

416

Shell and Coding agent for Claude and other mcp clients

rusiaaman

Shell and coding agent on mcp clients

666