New Framework GUIGuard Tackles Privacy Risks in GUI Automation Agents

Global: Privacy Risks in GUI Automation Agents and the GUIGuard Framework

Researchers have unveiled GUIGuard, a three-stage framework designed to mitigate privacy concerns associated with graphical user interface (GUI) automation agents, and have released a corresponding benchmark, GUIGuard-Bench, to evaluate privacy‑preserving capabilities.

Privacy Challenges in GUI Agents

GUI agents operate by perceiving on‑screen content and transmitting screenshots to remote models, which can expose sensitive personal data. The risk is amplified because GUIs often display richer private information than text‑based interfaces, and the privacy impact varies across interaction trajectories.

Introducing GUIGuard

GUIGuard addresses these concerns through a sequential process: (1) privacy recognition, identifying potentially sensitive regions; (2) privacy protection, applying masking or obfuscation techniques; and (3) task execution, allowing the agent to continue operating under the imposed safeguards.

Benchmark Dataset: GUIGuard-Bench

The authors compiled GUIGuard‑Bench, a cross‑platform collection comprising 630 interaction trajectories and 13,830 screenshots. Each image is annotated with region‑level privacy grounding, risk‑level labels, privacy categories, and an assessment of task necessity.

Evaluation Findings

Testing on the benchmark revealed that current agents struggle with privacy recognition, with the best‑performing models achieving only 13.3 % accuracy on Android interfaces and 1.4 % on PC environments.

Protection Strategies and Task Performance

Despite privacy safeguards, task‑planning semantics can be retained. Closed‑source models demonstrated stronger semantic consistency compared with open‑source alternatives, and carefully designed protection strategies improved overall task accuracy while preserving user privacy.

Implications and Future Work

The study highlights privacy recognition as a critical bottleneck for practical GUI agents and suggests that further research should focus on enhancing detection mechanisms and developing more effective protection techniques.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.