| title | SeerGuard: A Safety Framework for Mobile GUI Agents via World Model Prediction |
|---|---|
| authors | Xue Yu, Bo Yuan, Pengshuai Yang, Kailin Zhao, Hong Hu, Junlan Feng |
| date | 2026-07-17 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.15550 |
| upvotes | 20 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| primer | World models |
| abstract | 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. |
cite
@misc{yu2026seerguard,
title={SeerGuard: A Safety Framework for Mobile GUI Agents via World Model Prediction},
author={Xue Yu and Bo Yuan and Pengshuai Yang and Kailin Zhao and Hong Hu and Junlan Feng},
year={2026},
eprint={2607.15550},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.15550}
}