As GUI-based mobile agents rapidly progress, rigorous safety evaluation of their autonomous decision-making in realistic app interfaces becomes increasingly critical. Existing benchmarks mainly focus on execution-level anomalies using task success or hijack rates, but fail to capture the in-task goal deviation risk in...
Li Hu, Kang-Hua Mo, Ying-Zi Jin et al.· 0 citations
Recent personalized LLM agents increasingly transform information retained in memory into compressed or structured representations, which we call user models, to guide later decisions. When source wording is removed from the state reachable through the ordinary interface, these models are commonly treated as more priva...
Hao-Yang Li, Ya-Xin Xiao, Qing-Qing Ye et al.· 0 citations
Activation steering offers an inference-time defense for vision--language models (VLMs) by modifying intermediate representations without updating backbone parameters. However, protection on benchmark inputs may not persist across alternative expressions of the same harmful request. We investigate this gap using fixed...
Xin-Wei Zhang, Ao-Ting Hu, Hang-Cheng Liu et al.· 0 citations
This work introduces SINGED (Source Integrity and the Nonidentifiability Gap in Execution Decisions for LLM Agents), a controlled benchmark covering five primary and two held-out task families and exposes a rank-, evidence-, and choice-sensitive outcome-to-execution gap.
XiaoYu Xu, Zi Liang, Min-Xin Du et al.· 0 citations
FeatMark is introduced, a watermarking framework that shifts from pixel-level, energy-starved perturbations to inconspicuous semantic features: small, scene-consistent micro- features that remain natural to humans while providing a stronger, machine-verifiable provenance signal.
Hao-Yang Li, Ruo-Xi Sun, Qing-Qing Ye et al.· 0 citations
This work introduces TraceGuard, an adaptive rank-based filtering method that uses agreement among complementary feature rankings to identify suspicious examples and refines the selected set through shared patterns and adapts the removal threshold to each corpus without knowing the attack or poison rate.
Hao-Yang Li, Ya-Xin Xiao, L. Dai et al.· 0 citations
FedSubMuon is proposed, a communication-efficient federated Muon fine-tuning method that optimizes compact coefficient matrices within shared structured subspaces that keeps Muon on a single matrix-valued trainable object, while reducing the client upload to compact coefficient matrices.
Shao-Long Chen, Youming Tao, Shu-Zhen Chen et al.· 0 citations
SEBA is a sample-efficient framework for black-box adversarial attacks on visual RL agents that significantly reduces cumulative rewards, preserves visual fidelity, and greatly decreases environment interactions compared to prior black-box and white-box methods.
Tai-Ran Huang, Yu-Lin Jin, Jun-Xu Liu et al.· arXiv.org· 0 citations
This work proposes Continuous Agents for Injection Threats via Lifelong Yielding Nexus (CAITLYN), an agent-agnostic defense middleware that matches the detection performance of state-of-the-art defenses at lower token overhead than LLM-as-a-judge baselines.
Zi Liang, XiaoYu Xu, Yanyun Wang et al.· 1 citation
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