3 citations · 9 across the 19 of their papers we have counts for
13 papers · 1 filter
Trigger the Straggler: Load Hijack on Mixture-of-Experts LLMs
Rui Zhang, Wenbo Jiang, Hongwei Li +4
Expert parallelism (EP) is a common strategy for serving large Mixture-of-Experts (MoE) models across multiple GPUs by distributing experts among devices. Router decisions then det…
InkShield: Writing Style Protection Against Unauthorized Handwriting Mimicry
Jian Xiong, Wenbo Jiang, Zihan Wang +4
Recent handwritten text generators can reproduce a writer's style from publicly available references, posing risks of document forgery and identity misuse. An attacker may use a pu…
Black-Box Skill Stealing Attack from Proprietary LLM Agents: An Empirical Study
Zihan Wang, Rui Zhang, Yu Liu +4
Large language model (LLM) agents increasingly rely on skills to package reusable capabilities through instructions, tools, and resources. High-quality skills embed expert knowledg…
The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training
Rui Zhang, Hongwei Li, Yun Shen +6
The deployment of large language models (LLMs) raises significant ethical and safety concerns. While LLM alignment techniques are adopted to improve model safety and trustworthines…
BadTemplate: A Training-Free Backdoor Attack via Chat Template Against Large Language Models
Zihan Wang, Hongwei Li, Rui Zhang +2
Chat template is a common technique used in the training and inference stages of Large Language Models (LLMs). It can transform input and output data into role-based and templated…
Hidden Tail: Adversarial Image Causing Stealthy Resource Consumption in Vision-Language Models
Rui Zhang, Zihan Wang, Tianli Yang +5
Vision-Language Models (VLMs) are increasingly deployed in real-world applications, but their high inference cost makes them vulnerable to resource consumption attacks. Prior attac…
ConfGuard: A Simple and Effective Backdoor Detection for Large Language Models
Zihan Wang, Rui Zhang, Hongwei Li +4
Backdoor attacks pose a significant threat to Large Language Models (LLMs), where adversaries can embed hidden triggers to manipulate LLM's outputs. Most existing defense methods,…
The Ripple Effect: On Unforeseen Complications of Backdoor Attacks
Rui Zhang, Yun Shen, Hongwei Li +5
Recent research highlights concerns about the trustworthiness of third-party Pre-Trained Language Models (PTLMs) due to potential backdoor attacks. These backdoored PTLMs, however,…
BadLingual: A Novel Lingual-Backdoor Attack against Large Language Models
Zihan Wang, Hongwei Li, Rui Zhang +5
In this paper, we present a new form of backdoor attack against Large Language Models (LLMs): lingual-backdoor attacks. The key novelty of lingual-backdoor attacks is that the lang…
MPMA: Preference Manipulation Attack Against Model Context Protocol
Zihan Wang, Rui Zhang, Yu Liu +5
Model Context Protocol (MCP) standardizes interface mapping for large language models (LLMs) to access external data and tools, which revolutionizes the paradigm of tool selection…
Combinational Backdoor Attack against Customized Text-to-Image Models
Wenbo Jiang, Jiaming He, Hongwei Li +6
Recently, Text-to-Image (T2I) synthesis technology has made tremendous strides. Numerous representative T2I models have emerged and achieved promising application outcomes, such as…
Backdoor Attacks against Hybrid Classical-Quantum Neural Networks
Ji Guo, Wenbo Jiang, Rui Zhang +3
Hybrid Quantum Neural Networks (HQNNs) represent a promising advancement in Quantum Machine Learning (QML), yet their security has been rarely explored. In this paper, we present t…
Instruction Backdoor Attacks Against Customized LLMs
Rui Zhang, Hongwei Li, Rui Wen +5
The increasing demand for customized Large Language Models (LLMs) has led to the development of solutions like GPTs. These solutions facilitate tailored LLM creation via natural la…