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.
面向语言模型交互中敏感数据隐私保护的多智能体防火墙架构 / Multi-Agent Firewall Architecture for Privacy Protection of Sensitive Data in Interactions with Language Models
本文提出了一种开源的多智能体防火墙系统，通过浏览器插件和代理服务器拦截用户与大语言模型之间的所有通信，结合规则检测和AI语义分析，有效防止聊天过程中敏感数据（如代码、个人信息）泄露，且在不同配置下最高可达到94.93%的检测准确率。
源自 arXiv: 2607.08282
