LLM-FuzzX
LLM-FuzzX is a user-friendly fuzz testing tool for Large Language Models (e.g., GPT, Claude, LLaMA), featuring advanced task-aware mutation strategies, fine-grained evaluations, and jailbreak detection to uncover potential security vulnerabilities and enhance model robustness.
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LLM-FuzzX is an open-source user-friendly fuzz testing tool for large language models (e.g., GPT, Claude, LLaMA), equipped with advanced task-aware mutation strategies, fine-grained evaluation, and jailbreak detection capabilities. It helps researchers and developers quickly discover potential security vulnerabilities and enhance model robustness. The tool features a user-friendly web interface for visual configuration and real-time monitoring, supports various advanced mutation methods, integrates RoBERTa model for real-time jailbreak detection and evaluation, supports multiple language models like GPT, Claude, LLaMA, provides visualization analysis with seed flowcharts and experiment data statistics, and offers detailed logging support for main, mutation, and jailbreak logs.
README:
LLM-FuzzX 是一款开源的面向大型语言模型(如 GPT、Claude、LLaMA)的用户友好型模糊测试工具,具备高级任务感知变异策略、精细化评估以及越狱检测功能,能够帮助研究人员和开发者快速发现潜在安全漏洞,并增强模型的稳健性。
- 🚀 用户友好的界面: 提供直观的 Web 界面,支持可视化配置和实时监控
- 🔄 多样化变异策略: 支持多种高级变异方法,包括相似变异、交叉变异、扩展变异等
- 📊 实时评估反馈: 集成 RoBERTa 模型进行实时越狱检测和评估
- 🌐 多模型支持: 支持主流大语言模型,包括 GPT、Claude、LLaMA 等
- 📈 可视化分析: 提供种子流程图、实验数据统计等多维度分析功能
- 🔍 细粒度日志: 支持多级日志记录,包括主要日志、变异日志、越狱日志等
LLM-FuzzX 采用前后端分离的架构设计,主要包含以下核心模块:
- Fuzzing Engine: 系统的中枢调度器,协调各组件工作流程
- Seed Management: 负责种子的存储、检索和更新
- Model Interface: 统一的模型调用接口,支持多种模型实现
- Evaluation System: 基于 RoBERTa 的越狱检测和多维度评估
- 相似变异: 保持原始模板风格,生成相似结构的变体
- 交叉变异: 从种子池中选择模板进行交叉组合
- 扩展变异: 在原始模板基础上添加补充内容
- 缩短变异: 通过压缩和精简生成更简洁的变体
- 重述变异: 保持语义不变的情况下重新表述
- 目标感知变异: 根据目标模型特点定向生成
- Python 3.8+
- Node.js 14+
- CUDA 支持 (用于 RoBERTa 评估模型)
- 8GB+ 系统内存
- 稳定的网络连接
# 克隆项目
git clone https://github.com/Windy3f3f3f3f/LLM-FuzzX.git
# 创建虚拟环境
conda create -n llm-fuzzx python=3.10
conda activate llm-fuzzx
# 安装依赖
cd LLM-FuzzX
pip install -r requirements.txt
# 进入前端目录
cd llm-fuzzer-frontend
# 安装依赖
npm install
# 启动开发服务器
npm run serve
- 在项目根目录创建
.env
文件配置 API 密钥:
OPENAI_API_KEY=your-openai-key
CLAUDE_API_KEY=your-claude-key
HUGGINGFACE_API_KEY=your-huggingface-key
- 在
config.py
中配置模型参数:
MODEL_CONFIG = {
'target_model': 'gpt-3.5-turbo',
'mutator_model': 'gpt-3.5-turbo',
'evaluator_model': 'roberta-base',
'temperature': 0.7,
'max_tokens': 2048
}
# 启动后端服务
python app.py # 默认运行在 http://localhost:10003
# 启动前端服务
cd llm-fuzzer-frontend
npm run serve # 默认运行在 http://localhost:10001
- 选择目标测试模型(支持 GPT、Claude、LLaMA 等)
- 准备测试数据
- 使用预置问题集
- 自定义输入问题
- 配置测试参数
- 设置最大迭代次数
- 选择变异策略
- 配置评估阈值
- 启动测试并实时监控
- 查看当前进度
- 监控成功率
- 分析变异效果
系统提供多级日志记录:
-
main.log
: 主要流程和关键事件 -
mutation.log
: 变异操作记录 -
jailbreak.log
: 成功越狱案例 -
error.log
: 错误和异常信息
LLM-FuzzX/
├── src/ # 后端源代码
│ ├── api/ # API 接口
│ ├── evaluation/ # 评估模块
│ ├── fuzzing/ # 模糊测试核心
│ ├── models/ # 模型封装
│ └── utils/ # 工具函数
├── llm-fuzzer-frontend/ # 前端代码
├── scripts/ # 辅助脚本
├── data/ # 数据文件
└── logs/ # 日志文件
-
测试规模设置
- 建议单次测试迭代次数不超过 1000 次
- 新场景先进行小规模试验
- 根据资源情况调整并发度
-
变异策略选择
- 简单场景优先使用单一变异策略
- 复杂场景可组合多种变异方法
- 注意保持变异强度的平衡
-
资源优化
- 合理设置 API 调用间隔
- 适时清理历史记录
- 监控系统资源使用
欢迎通过以下方式参与项目:
- 提交 Issue
- 报告 bug
- 提出新功能建议
- 分享使用经验
- 提交 Pull Request
- 修复问题
- 添加功能
- 改进文档
- 方法论贡献
- 提供新的变异策略
- 设计创新的评估方法
- 分享测试经验
本项目采用 MIT 许可证。详见 LICENSE 文件。
- Issue: GitHub Issues
- Email: [email protected]
[1] Yu, J., Lin, X., Yu, Z., & Xing, X. (2024). LLM-Fuzzer: Scaling Assessment of Large Language Model Jailbreaks. In 33rd USENIX Security Symposium (USENIX Security 24) (pp. 4657-4674). USENIX Association.
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