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New multi-agent firewall architecture protects sensitive data with LLMs

By PulseAugur Editorial · [3 sources] ·

Researchers have developed an open-source, multi-agent firewall architecture to protect sensitive data when interacting with large-language models (LLMs). This system, comprising a browser extension and a proxy, intercepts HTTP(S) and WebSocket traffic to prevent data leakage. It employs a hybrid approach combining deterministic detectors with LLM-driven semantic analysis and proprietary code prevention, achieving up to 94.93% F1 scores in evaluations. AI

IMPACT Enhances security for LLM integrations, potentially enabling wider adoption in sensitive enterprise environments.

RANK_REASON The cluster contains an academic paper detailing a new technical architecture for LLM interaction security.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New multi-agent firewall architecture protects sensitive data with LLMs

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0 / 100
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Research
The cluster contains an academic paper detailing a new technical architecture for LLM interaction security.
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3 independent sources
Multiple independent publishers reporting the same story raises confidence that it's real and newsworthy.
Topics
paper, safety
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High
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60 days old
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COVERAGE [3]

  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…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    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…

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