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 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 f...
| Authors | Hugo García Cuesta, Pablo Mateo Torrejón, Alfonso Sánchez-Macián |
|---|---|
| Primary category | cs.CR |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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