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cs.CR2025
TZ-LLM: Protecting On-Device Large Language Models with Arm TrustZone
Xunjie Wang, Jiacheng Shi, Zihan Zhao +3
Large Language Models (LLMs) deployed on mobile devices offer benefits like user privacy and reduced network latency, but introduce a significant security risk: the leakage of prop…
cs.LG2025
Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs
Xun Wang, Jing Xu, Franziska Boenisch +3
Prompting has become a dominant paradigm for adapting large language models (LLMs). While discrete (textual) prompts are widely used for their interpretability, soft (parameter) pr…
cs.CV2025★ 1 cited
Improving the Transferability of Adversarial Attacks by an Input Transpose
Qing Wan, Shilong Deng, Xun Wang
Deep neural networks (DNNs) are highly susceptible to adversarial examples--subtle perturbations applied to inputs that are often imperceptible to humans yet lead to incorrect mode…