为大模型交互设计多智能体防火墙，防止敏感数据泄露
Multi-Agent Firewall Architecture for Privacy Protection of Sensitive Data in Interactions with Language Models
- 用浏览器插件+代理拦截全量通信流量
- 混合检测达94.93%最高F1分数
- 适合关注隐私安全的开发者与企业
尽管大语言模型已成为重要的生产力工具，但在缺乏充分防护的情况下集成到工作流中会带来显著风险。本文提出一个开源、以隐私保护为核心的用户级防火墙，用于保障基于网页和程序化接口的大语言模型交互安全。该架构结合浏览器扩展与代理，实现对HTTP(S)和WebSocket通信的全面流量拦截。核心是一个灵活的多智能体流水线，通过确定性检测器与大模型驱动的语义分析相结合的方式，实现数据泄露防护、专有代码泄露防护，并具备可扩展性，支持未来安全增强（如提示注入规避）。分层架构支持在异构环境中部署，使组织可在计算成本、检测深度与延迟之间取得平衡。评估结果显示，在最优配置下，系统达到最高94.93%的F1分数。
原文摘要 · Abstract (English)
While Large Language Models (LLMs) have become essential productivity tools, their integration into workflows without adequate safeguards creates significant risks. This paper proposes an open-source, privacy-focused, user-facing firewall designed to secure both web-based and programmatic LLM interactions. The architecture combines a browser extension and a proxy for total traffic interception across both HTTP(S) and WebSocket communications. At its core, a flexible multi-agent pipeline delivers data leakage prevention through a hybrid approach combining deterministic detectors with LLM-driven semantic analysis, proprietary code leakage prevention, and extensible components designed for future security enhancements such as prompt injection evasion. The framework's layered architecture enables deployment across heterogeneous environments, allowing organizations to balance computational cost, detection depth and latency. Evaluation results demonstrate it achieves F1 scores of up to 94.93% on optimal configurations.
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