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版本更新 · v2arXiv:2609.16732

When Agents See Differently: Exposing UI Desynchronization Threats in Mobile Agents

Heng Li, Fulin Zhao, Zhe Geng, Zhiyuan Yao, Wei Yuan, Xiapu Luo

原文摘要

Mobile agents may act on interface content that users cannot see or easily notice, making their actions harder to oversee. We characterize this gap as human-agent UI desynchronization and investigate how attackers can exploit it through perturbations embedded in applications before deployment. Beyond accessibility metadata, we identify two mechanisms that expose misleading cues to screenshot-based agents: content rendered in display-occluded regions under compatible device configurations, and low-contrast text that remains encoded in screenshots but is difficult for users to notice. We develop an automated framework that searches application-specific perturbation strategies, optimizes their content using representative tasks, and realizes the perturbations in deployable applications. The perturbation content remains fixed at runtime and requires neither access to future user instructions nor detection of agent use. Using a benchmark of 546 tasks across 13 applications, we evaluate five mobile-agent frameworks and three backbone models. The attacks achieve average misleading rates of 77.4% in static observation-space evaluation and 67.2% in dynamic application-level evaluation, where success denotes selection of an attacker-designated decoy control. We invited 195 participants to assess the perceptibility of our visual perturbations and found that none spontaneously noticed the low-contrast text. These findings expose a security gap between human-visible interfaces and agent-observable representations, showing that inconspicuous application content can redirect delegated actions even without runtime adaptation.

cs.CR

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1714ACL Anthology · ACL 2026

ProMedical: Hierarchical Fine-Grained Criteria Modeling for Medical LLM Alignment via Explicit Injection

He Geng, Yangmin Huang IFLYTEK CO.LTD., Lixian Lai Xunfei Healthcare, Qianyun Du IFLYTEK CO.LTD., Hui Chu, Zhiyang He Xunfei Healthcare Technology Co., Ltd., Jiaxue Hu IFLYTEK CO.LTD., Xiaodong Tao Xunfei Healthcare Co. Ltd

原文摘要

Aligning Large Language Models (LLMs) with high-stakes medical standards remains a significant challenge, primarily due to the dissonance between coarse-grained preference signals and the complex, multi-dimensional nature of clinical protocols. To bridge this gap, we introduce \textit{ProMedical}, a unified alignment framework grounded in fine-grained clinical criteria. We first construct \textit{ProMedical-Preference-50k}, a dataset generated via a human-in-the-loop pipeline that augments medical instructions with rigorous, physician-derived rubrics. Leveraging this corpus, we propose the Explicit Criteria Injection paradigm to train a multi-dimensional reward model. Unlike traditional scalar reward models, our approach explicitly disentangles safety constraints from general proficiency, enabling precise guidance during reinforcement learning. To rigorously validate this framework, we establish \textit{ProMedical-Bench}, a held-out evaluation suite anchored by double-blind expert adjudication. Empirical evaluations demonstrate that optimizing the Qwen3-8B base model via \textit{ProMedical-RM}-guided GRPO yields substantial gains, improving overall accuracy by 22.3% and safety compliance by 21.7%, effectively rivaling proprietary frontier models. Furthermore, the aligned policy generalizes robustly to external benchmarks, demonstrating performance comparable to state-of-the-art models on UltraMedical. We publicly release our datasets, reward models, and benchmarks to facilitate reproducible research in safety-aware medical alignment.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表