RealGUINoise: An Interactive Cross-Platform Benchmark for GUI Agent Robustness under Real-World Interface Noise

Abstract

Graphical User Interface (GUI) agents and Computer-Using Agents (CUAs) are rapidly becoming practical tools. However, real-world deployment increasingly exposes performance failures and safety risks, while a major yet underexplored source of these problems lies in the complex and noisy conditions of everyday interfaces. Existing benchmarks largely assume clean environments or focus narrowly on security-specific settings, and lack a unified framework for consistent, automated end-to-end evaluation across diverse agents and platforms. Hence, we introduce RealGUINoise, an interactive cross-platform, extensible benchmark for systematically evaluating GUI Agents under common realistic interface noise in fully interactive environments. Specifically, RealGUINoise comprises 42 noise types spanning web, desktop, and mobile tasks and integrates 7 representative agent frameworks. It evaluates these agents on real-world daily tasks through real-time interaction, comparing their performance against task-specific golden rubrics and clean-environment trajectories in terms of reliability, safety, and trajectory-level behavior. Our experiments show that these noises not only degrade task performance but also substantially redirect agents' action trajectories and increase their propensity for unsafe behavior. These findings expose a critical gap between capability in clean environments and dependable operation in real-world settings, establishing RealGUINoise as a testbed for developing more robust and trustworthy GUI agents.