LLMFuzzer is an open-source fuzzing framework designed for testing Large Language Models (LLMs), particularly their API integrations within applications. It targets security enthusiasts, pentesters, and cybersecurity researchers aiming to discover and exploit vulnerabilities in AI systems, streamlining the testing process.

How It Works

LLMFuzzer employs a modular architecture to support various fuzzing strategies for LLMs. It focuses on testing LLM API integrations by sending crafted inputs to an LLM endpoint and analyzing the responses. The framework is designed for extensibility, allowing users to integrate new attack vectors and comparison methods.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Prerequisites: Python, requires editing llmfuzzer.cfg with LLM API endpoint details (URL, content type, query/output attributes, headers, cookies).
• Documentation: In progress, see README.

Highlighted Details

• First open-source fuzzing framework specifically for LLMs and their API integrations.
• Supports various fuzzing strategies and is designed for extensibility.
• Roadmap includes HTML reports, multiple connectors (JSON-POST, RAW-POST, QUERY-GET), proxy support, and dual-LLM analysis.

Maintenance & Community

The project is marked as "Unmaintained" but welcomes forks and continued development.

Licensing & Compatibility

Licensed under the MIT License, permitting commercial use and integration with closed-source applications.

Limitations & Caveats

The project is explicitly marked as unmaintained, meaning there is no active development or support. Full documentation is still in progress.