arXiv subjects

Bojun Yang

Publications and source records attributed to Bojun Yang.

3 recordsLinked to original sources

arXiv2026

When Agents Learn to Be You: Benchmarking Privacy Leakage, Impersonation Risk, and Defenses in Persona Skills

Persona skills distill personal interaction histories into portable and executable artifacts for downstream agents. While enabling flexible personalization, this process concentrates fragmented personal signals, amplifies their impact through reuse, and challenges defenses designed for individual records or retrieval-based memory. To systematically investigate the safety of the persona-skill pipeline, we introduce AntiSkillBench, an end-to-end benchmark for evaluating risks and defenses across the persona-skill pipeline. It comprises: (i) a dataset of 7,500 persona-grounded dialogue traces, constructed from 50 behaviorally rich profiles spanning diverse task scenarios; (ii) an evaluation suite that measures skill-level privacy leakage and agent-level attribute disclosure and behavioral impersonation across three skill-distillation strategies; and (iii) a defense evaluation covering four configurations across online and post-hoc interventions, including active risk suppression and passive provenance protection. Experiments across three frontier agents show that persona-skill risks persist across agent backbones and distillation protocols, extending from explicit attributes to communication styles and personality traits. Existing defenses exhibit limited and distillation-dependent effectiveness, failing to generalize across risk and distillation strategies. These results highlight AntiSkillBench as a challenging benchmark for developing privacy-preserving and authenticity-aware persona skills.

cs.CR↗

arXiv2026

Test-Time Attention Purification for Backdoored Large Vision Language Models

Despite the strong multimodal performance, large vision-language models (LVLMs) are vulnerable during fine-tuning to backdoor attacks, where adversaries insert trigger-embedded samples into the training data to implant behaviors that can be maliciously activated at test time. Existing defenses typically rely on retraining backdoored parameters (e.g., adapters or LoRA modules) with clean data, which is computationally expensive and often degrades model performance. In this work, we provide a new mechanistic understanding of backdoor behaviors in LVLMs: the trigger does not influence prediction through low-level visual patterns, but through abnormal cross-modal attention redistribution, where trigger-bearing visual tokens steal attention away from the textual context - a phenomenon we term attention stealing. Motivated by this, we propose CleanSight, a training-free, plug-and-play defense that operates purely at test time. CleanSight (i) detects poisoned inputs based on the relative visual-text attention ratio in selected cross-modal fusion layers, and (ii) purifies the input by selectively pruning the suspicious high-attention visual tokens to neutralize the backdoor activation. Extensive experiments show that CleanSight significantly outperforms existing pixel-based purification defenses across diverse datasets and backdoor attack types, while preserving the model's utility on both clean and poisoned samples.

cs.CV↗

arXiv2026

MetaBlue: A Metasurface-Assisted Acoustic Underwater Localization System

Underwater localization is essential for marine exploration and autonomous underwater operations, yet existing radio frequency and optical approaches are limited by rapid attenuation or limited visibility. Acoustic sensing remains the most practical choice, but conventional acoustic systems typically rely on large arrays or multiple synchronized anchors, resulting in high hardware costs and complex deployment. This paper introduces a novel low-cost passive acoustic metasurface, MetaBlue , explicitly designed for underwater localization, which, when attached to an ordinary ultrasonic transmitter, transforms it into a directional "super-transmitter." The metasurface embeds direction-dependent spectral patterns into the transmitted waveform, enabling accurate angle-of-arrival (AoA) estimation using only a single hydrophone. For ranging, we present a new EM-acoustic mixed time-of-arrival (ToA) method that leverages the acoustic transducer's inherent low-frequency EM leakage as a timing reference, enabling precise ranging without shared clocks. This allows complete 3D localization with a single low-cost anchor. We evaluate the system across diverse real-world underwater settings, including pools, tanks, and outdoor environments. Experiments show that our design achieves an average AoA error of 8.7 degree and 3D localization error of 0.37 m at distances over 10 m. Even with a single anchor, the system maintains 0.73 m precision.

cs.NI↗