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 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
The rapid integration of LLMs into enterprise and personal workflows without sufficient security is creating an urgent need for solutions to protect sensitive data from leakage as these models become essential productivity tools.
This development addresses a critical vulnerability in the widespread adoption of AI, safeguarding intellectual property and personal information, which is essential for maintaining trust and regulatory compliance in AI interactions.
The proposed multi-agent firewall architecture introduces a new layer of user-facing protection for LLM interactions, moving towards a more secure and privacy-conscious integration of AI into daily operations.
- · Cybersecurity firms
- · Enterprises using LLMs
- · Individual AI users
- · Open-source security communities
- · Data brokers seeking LLM-exposed data
- · Malicious actors
- · LLM providers with poor native security
Increased adoption and confidence in LLM integration across sensitive sectors due to enhanced data protection.
Reduced incidence of data breaches and intellectual property loss attributed to LLM interactions, potentially influencing insurance and compliance standards.
The proliferation of similar privacy-focused AI 'firewall' solutions, establishing a new standard for secure AI interaction.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI