Author

Qing-Qing Ye

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Preprint Sep 2026

SteerProbe: Learning to Bypass Safety Steering in Vision-Language Models

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

#artificial intelligence Preprint Sep 2026

SINGED: Correct Outputs Do Not Certify Safe Execution in LLM Agents

Tool-using language-model agents select and execute third-party artifacts. Different implementations can return the requested output while producing hidden execution effects that task-, attack-, or choice-based evaluations may miss. We study functional counterfeits: implementations that match benign alternatives on the...

XiaoYu Xu, Zi Liang, Min-Xin Du et al. · 0 citations

Preprint Sep 2026

FeatMark: Feature-level Watermark Protection against Mimicry Attacks with Diffusion Models

Text-to-image diffusion models enable data-efficient"mimicry"attacks, wherein adversaries fine-tune the model on a handful of public photos to synthesize convincing forgeries of a target individual. A common countermeasure is to embed imperceptible, low-energy watermarks, yet recent studies show these signatures are br...

Hao-Yang Li, Ruo-Xi Sun, Qing-Qing Ye et al. · 0 citations

#machine learning Preprint Sep 2026

TraceGuard: Adaptive Multimodal Poison Filtering through Cross-Feature Rank Agreement

Multimodal training relies on image-text corpora collected from external sources, creating opportunities for attackers to poison the data. Stealthy attacks can preserve plausible image-text pairs while concealing the differences used by detectors, so apparently clean data can still redirect the trained model. We theref...

Hao-Yang Li, Ya-Xin Xiao, Lin-Yan Dai et al. · 0 citations

#artificial intelligence Preprint Sep 2026

FedSubMuon: Communication-Efficient Federated LLM Fine-Tuning via Structured Subspace Muon

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

#artificial intelligence Nov 2025

SEBA: Sample-Efficient Black-Box Attacks on Visual Reinforcement Learning

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

#artificial intelligence Preprint Aug 2026

CAITLYN: Can LLM Agents Autonomously Synthesize Defenses against Emerging Injection Attacks?

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