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https://github.com/mnns/LLMFuzzer

๐Ÿง  LLMFuzzer - Fuzzing Framework for Large Language Models ๐Ÿง  LLMFuzzer is the first open-source fuzzing framework specifically designed for Large Language Models (LLMs), especially for their integrations in applications via LLM APIs. ๐Ÿš€๐Ÿ’ฅ
https://github.com/mnns/LLMFuzzer

ai cybersecurity llm llmsecurity

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๐Ÿง  LLMFuzzer - Fuzzing Framework for Large Language Models ๐Ÿง  LLMFuzzer is the first open-source fuzzing framework specifically designed for Large Language Models (LLMs), especially for their integrations in applications via LLM APIs. ๐Ÿš€๐Ÿ’ฅ

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# ๐Ÿง  LLMFuzzer - Fuzzing Framework for Large Language Models ๐Ÿง 

![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This project is no longer actively maintained.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.

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LLMFuzzer is the first open-source fuzzing framework specifically designed for Large Language Models (LLMs), especially for their integrations in applications via LLM APIs. ๐Ÿš€๐Ÿ’ฅ

## ๐ŸŽฏ 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. ๐Ÿ•ต๏ธโ€โ™€๏ธ

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

## ๐ŸŒŸ Features

- Robust fuzzing for LLMs ๐Ÿงช
- LLM API integration testing ๐Ÿ› ๏ธ
- Wide range of fuzzing strategies ๐ŸŒ€
- Modular architecture for easy extendability ๐Ÿ“š

## ๐Ÿ”ฅ 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

## ๐Ÿš€ 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
```

## ๐Ÿ“š 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.

## ๐Ÿค Contributing
We welcome all contributors who are passionate about improving LLMFuzzer. See our contributing guidelines for ways to get started. ๐Ÿค—

## ๐Ÿ’ผ License
LLMFuzzer is licensed under the MIT License. See the LICENSE file for more details.

## ๐ŸŽฉ Acknowledgments
LLMFuzzer couldn't exist without the community. We appreciate all our contributors and supporters. Let's make AI safer together! ๐Ÿ’–