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English(EN) Multi-Agent Firewall Architecture for Privacy Protection of Sensitive Data in Interactions with Language Models

新的多智能体防火墙架构通过LLM保护敏感数据

研究人员开发了一个开源的多智能体防火墙架构,用于在与大型语言模型(LLM)交互时保护敏感数据。该系统由浏览器扩展和代理组成,可拦截HTTP(S)和WebSocket流量,以防止数据泄露。它采用了一种混合方法,结合了确定性检测器、LLM驱动的语义分析和专有代码预防,在评估中达到了高达94.93%的F1分数。 AI

影响 增强了LLM集成的安全性,可能使其在敏感的企业环境中得到更广泛的应用。

排序理由 该集群包含一篇学术论文,详细介绍了LLM交互安全的新技术架构。

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新的多智能体防火墙架构通过LLM保护敏感数据

报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Hugo Garc\'ia Cuesta, Pablo Mateo Torrej\'on, Alfonso S\'anchez-Maci\'an ·

    Multi-Agent Firewall Architecture for Privacy Protection of Sensitive Data in Interactions with Language Models

    arXiv:2607.08282v1 Announce Type: cross Abstract: 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 …

  2. arXiv cs.AI TIER_1 English(EN) · Alfonso Sánchez-Macián ·

    Multi-Agent Firewall Architecture for Privacy Protection of Sensitive Data in Interactions with Language Models

    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 pro…