1 citations · 1 across the 5 of their papers we have counts for
6 papers
AgentProv: Auditing Agentic LLM API Providers via Tool-use Policy Probes
Xun Wang, Bihe Zhao, Michael Backes +2
Commercial LLM APIs advertise a specific foundation model, but the served backbone may be silently substituted, quantized, or wrapped, for example to save deployment costs. All exi…
Here is a GIFT: Enforcing User Data Isolation in LLM Serving via GPU Information Flow Tracking
Jiacheng Shi, Xunjie Wang, Cheng Tan +1
LLM serving frameworks process large volumes of user data--often containing sensitive information--on shared infrastructure. Ensuring isolation between users who share the same ser…
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…
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…
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…
Structurally Prune Anything: Any Architecture, Any Framework, Any Time
Xun Wang, John Rachwan, Stephan Günnemann +1
Neural network pruning serves as a critical technique for enhancing the efficiency of deep learning models. Unlike unstructured pruning, which only sets specific parameters to zero…