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 remote inference servers even when most elements are irrelevant to the current task, which can leak sensitive but unnecessary context such as authentication codes, private notifications, and background application states. We propose MINIM, a trusted local broker that performs privacy-aware minimization on the client side b
The increasing deployment of LLM-powered autonomous agents in sensitive environments necessitates immediate solutions for data privacy and security vulnerabilities.
This development addresses a critical security flaw in current AI agent architectures, directly impacting the adoption and trustworthiness of autonomous systems in enterprise and personal use.
Agents can now operate with reduced risk of data leakage, fostering greater user and institutional confidence in their deployment and expanding their utility in privacy-sensitive applications.
- · AI agent developers
- · Enterprises deploying AI agents
- · Individuals using AI agents
- · Cybersecurity firms
- · Malicious actors
- · Unsecured remote inference servers
Increased trust and wider adoption of LLM-powered autonomous agents across various sectors.
Development of industry standards and certifications for privacy-aware agent design and deployment.
Enhanced regulatory scrutiny and potential legal frameworks for AI agent data handling and security across jurisdictions.
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Read at arXiv cs.AI