35 citations · 56 across the 42 of their papers we have counts for
42 papers
Towards Scalable Fuzzy PSI via Efficient Fuzzy Matching
Meng Hao, Xinpeng Yang, Hanxiao Chen +5
In this paper, we present scalable fuzzy PSI protocols for general distance, supporting both low- and high-dimensional sets. The core technique is two effic…
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…
EvoTrustRAG: Evolution-Aware Conflict Attribution and Evidence Handling for Reliable Retrieval-Augmented Generation
Xi Nie, Hongwei Li, Shenghao Wu +3
Retrieval-Augmented Generation (RAG) improves the factuality of large language models with external knowledge, yet conflicting evidence remains a fundamental challenge in dynamic a…
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…
When Poison Fails After Retrieval: Revisiting Corpus Poisoning under Chunking and Reranking Pipelines
Xi Nie, Hongwei Li, Shenghao Wu +3
Retrieval-Augmented Generation (RAG) systems are vulnerable to corpus poisoning attacks that manipulate downstream model outputs through malicious knowledge injection. Existing stu…
One Jailbreak, Many Tongues: Learning Language-Insensitive Intention Representations for Multilingual Jailbreak Detection
Shuyu Jiang, Kaiyu Xu, Xingshu Chen +5
Large language models (LLMs) are increasingly deployed in applications for global multilingual users, yet safety training remains concentrated in dominant languages and has not pro…
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…
BioMoTouch: Touch-Based Behavioral Authentication via Biometric-Motion Interaction Modeling
Zijian Ling, Jianbang Chen, Hongwei Li +6
Touch-based authentication is widely deployed on mobile devices due to its convenience and seamless user experience. However, existing systems largely model touch interaction as a…
UTOPIA: Unlearnable Tabular Data via Decoupled Shortcut Embedding
Jiaming He, Fuming Luo, Hongwei Li +5
Unlearnable examples (UE) have emerged as a practical mechanism to prevent unauthorized model training on private vision data, while extending this protection to tabular data is no…
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…
State Backdoor: Towards Stealthy Real-world Poisoning Attack on Vision-Language-Action Model in State Space
Ji Guo, Wenbo Jiang, Yansong Lin +6
Vision-Language-Action (VLA) models are widely deployed in safety-critical embodied AI applications such as robotics. However, their complex multimodal interactions also expose new…
TEAR: Temporal-aware Automated Red-teaming for Text-to-Video Models
Jiaming He, Guanyu Hou, Hongwei Li +6
Text-to-Video (T2V) models are capable of synthesizing high-quality, temporally coherent dynamic video content, but the diverse generation also inherently introduces critical safet…
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,…
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution
Ji Guo, Xiaolei Wen, Wenbo Jiang +3
With the widespread application of super-resolution (SR) in various fields, researchers have begun to investigate its security. Previous studies have demonstrated that SR models ca…
FIGhost: Fluorescent Ink-based Stealthy and Flexible Backdoor Attacks on Physical Traffic Sign Recognition
Shuai Yuan, Guowen Xu, Hongwei Li +5
Traffic sign recognition (TSR) systems are crucial for autonomous driving but are vulnerable to backdoor attacks. Existing physical backdoor attacks either lack stealth, provide in…
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…
DivTrackee versus DynTracker: Promoting Diversity in Anti-Facial Recognition against Dynamic FR Strategy
Wenshu Fan, Minxing Zhang, Hongwei Li +5
The widespread adoption of facial recognition (FR) models raises serious concerns about their potential misuse, motivating the development of anti-facial recognition (AFR) to prote…
TrojanEdit: Multimodal Backdoor Attack Against Image Editing Model
Ji Guo, Peihong Chen, Wenbo Jiang +6
Multimodal diffusion models for image editing generate outputs conditioned on both textual instructions and visual inputs, aiming to modify target regions while preserving the rest…
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 Attack Against Vision Transformers via Attention Gradient-Based Image Erosion
Ji Guo, Hongwei Li, Wenbo Jiang +1
Vision Transformers (ViTs) have outperformed traditional Convolutional Neural Networks (CNN) across various computer vision tasks. However, akin to CNN, ViTs are vulnerable to back…