GUIGuard: Toward a General Framework for Privacy-Preserving GUI Agents
This work addresses the critical privacy risks posed by GUI agents in automation, which often inadvertently leak sensitive information through uploaded interface screenshots, compounded by the lack of systematic approaches to identify and protect privacy across diverse interaction trajectories. To tackle this challenge, we propose GUIGuard—the first end-to-end privacy-preserving framework specifically designed for GUI agents—comprising three integrated stages: privacy identification, protection, and task execution under privacy constraints. We further introduce GUIGuard-Bench, a cross-platform benchmark encompassing 630 interaction trajectories and 13,830 region-level privacy-annotated screenshots. Experimental results reveal that existing agents exhibit alarmingly low privacy recognition accuracy (13.3% on Android and 1.4% on PC), whereas GUIGuard effectively masks sensitive content while preserving task semantics, demonstrating that robust privacy protection can be achieved without compromising task performance.