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New system MINIM enhances privacy for LLM agents

Researchers have developed MINIM, a new system designed to enhance privacy for LLM-powered autonomous agents. MINIM acts as a local broker, analyzing UI elements to predict their sensitivity and necessity for a given task. It then selectively transmits only the essential information, abstracting sensitive attributes when necessary and discarding irrelevant data to prevent sensitive context leakage. AI

IMPACT Enhances privacy for LLM agents by minimizing sensitive data transmission.

RANK_REASON The cluster contains a research paper describing a novel system for LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Hexuan Yu, Chaoyu Zhang, Heng Jin, Shanghao Shi, Ning Zhang, Y. Thomas Hou, Wenjing Lou ·

    Minim: Privacy-Aware Minimal View for Agents via Trusted Local Sanitization

    arXiv:2606.13949v1 Announce Type: new Abstract: Modern LLM-powered autonomous agents increasingly rely on rich user interface (UI) state observations to achieve reliable action grounding in complex digital environments. However, many deployments transmit the full UI state to remo…