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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. 🚀💥

파일 탐색기

최종 버전 다운로드 (.zip)

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

# 설치 가이드

1. 코드 내려받기

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

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd LLMFuzzer

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// 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This project is no longer actively maintained.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! 💖