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LLMFuzzer

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🧠 LLMFuzzer - Fuzzing Framework for Large Language Models 🧠 LLMFuzzer is the first open-source fuzzing framework specifically designed for Large Language Models (LLMs), especially for their integrations in applications via LLM APIs. 🚀💥

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- promptInjection.atk
- smuggling.atk
LICENSE
llmfuzzer.cfg
llmfuzzer.py
main.py
README.md
requirements.txt

# Installation Guide

1. Get the code

git clone https://github.com/mnns/LLMFuzzer

Downloads the entire project code from GitHub to your computer.

cd LLMFuzzer

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

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<div align="center">

# 🧠 LLMFuzzer - Fuzzing Framework for Large Language Models 🧠

![LLMFuzzer-shell](https://github.com/mnns/LLMFuzzer/assets/1796080/71b006df-706c-43f6-acd1-49646dbcb0e5)

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## Project Status: Unmaintained

This project is no longer actively maintained. You are welcome to fork and continue its development on your own. Thank you for your interest and support.

----------------------------------------------------------------

LLMFuzzer is the first open-source fuzzing framework specifically designed for Large Language Models (LLMs), especially for their integrations in applications via LLM APIs. 🚀💥

## 🎯 Who is this for?

If you're a security enthusiast, a pentester, or a cybersec researcher who loves to find and exploit vulnerabilities in AI systems, LLMFuzzer is the perfect tool for you. It's built to make your testing process streamlined and efficient. 🕵️‍♀️

![Untitled](https://github.com/mnns/LLMFuzzer/assets/1796080/a143897d-383c-4ed9-8b2f-65f4cdc5aa63)

## 🌟 Features

- Robust fuzzing for LLMs 🧪
- LLM API integration testing 🛠️
- Wide range of fuzzing strategies 🌀
- Modular architecture for easy extendability 📚

## 🔥 Roadmap
* Adding more attacks
* HTML Report as output
* Multiple Connectors (JSON-POST, RAW-POST, QUERY-GET)
* Multiple Comparers
* Proxy Support
* Dual-LLM (Side LLM observation)
* Autonomous Attack Mode

## 🚀 Get Started

1. Clone the repo
```bash
git clone https://github.com/mnns/LLMFuzzer.git
```

2. Navigate to the project directory
```bash
cd LLMFuzzer
```

3. Install dependencies
```bash
pip install -r requirements.txt
```

4. Edit **llmfuzzer.cfg** with your LLM API endpoint (LLMFuzzer -> Your Application -> LLM)
```bash
Connection: 
  Type: HTTP-API
  Url: "http://localhost:3000/chat" # Your LLM API
  Content: JSON
  Query-Attribute: "query" # Your JSON query attribute
  Output-Attribute: "answer" # Your JSON response attribute
  Headers: {'enwiki_session': '17ab96bd8ffbe8ca58a78657a918558'} # Add HTTP Headers if needed 
  Cookie: {'enwiki_session': '17ab96bd8ffbe8ca58a78657a918558'} # Add Cookies if needed
```

5. Run LLMFuzzer
```bash
python main.py
```

## 📚 Documentation
We are working on full documentation. It will cover detailed information about the architecture, different fuzzing strategies, examples, and how to extend the tool.

## 🤝 Contributing
We welcome all contributors who are passionate about improving LLMFuzzer. See our contributing guidelines for ways to get started. 🤗

## 💼 License
LLMFuzzer is licensed under the MIT License. See the LICENSE file for more details.

## 🎩 Acknowledgments
LLMFuzzer couldn't exist without the community. We appreciate all our contributors and supporters. Let's make AI safer together! 💖