SeerGuard: A Safety Framework for Mobile GUI Agents via World Model Prediction

Xue Yu, Roger, Pengshuai Yang, Kailin Zhao, Hong Hu, Junlan Feng

SeerGuard: A Safety Framework for Mobile GUI Agents via World Model Prediction: 20 upvotes on Hugging Face Daily Papers, #14 of 37 papers on 2026-07-21. Day-by-day upvote history. It lost 7 votes when the Hub removed votes in bulk.

Mobile graphical user interface (GUI) agents have demonstrated remarkable capabilities in automating complex tasks, yet they introduce critical safety risks where a single erroneous action can lead to irreversible consequences. Existing safety mechanisms are primarily reactive, lacking the ability to assess risks before execution. In this paper, we introduce SeerGuard, a consequence-aware safety framework designed to mitigate these risks through pre-execution instruction-level screening and action-level risk assessment. Specifically, the action-level assessment analyzes agent-proposed actions within current GUI states, anticipating likely outcomes to identify risks before they are executed. To enable these capabilities, we construct a unified safety-augmented world model (SAWM) via multi-task learning, integrating semantic next-state prediction with safety risk assessment. Extensive experiments demonstrate that SeerGuard generalizes effectively across diverse mobile GUI agents. On Qwen3-VL-8B-Instruct, it increases the safety-utility score from 0.191 to 0.596 at ω=0.8 and reduces the risk-cost score from 0.347 to 0.130 at α=0.8. Further analyses on our SAWM validate the effectiveness of the instruction-level screening, alongside the capability of action risk assessment and next-state prediction.

Paper page on Hugging Face · arXiv

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