activity
20242026
most citedWatch Out for Your Guidance on Generation! Exploring Conditional Backdoor Attacks against Large Language Models

3 citations · 6 across the 10 of their papers we have counts for

collaborators

11 papers

cs.CR2026

RAPID: A Real-Time Defense Against Unauthorized Model Distillation for Text-to-Image Services

Zihan Wang, Boheng Li, Rui Zhang +5

Diffusion-based text-to-image (T2I) models are increasingly used for visual content creation, making their generation capability a valuable intellectual property asset. However, th…

cs.CR2026

DiffSafeMerge: Mitigating Backdoor Inheritance in Diffusion Model Merging

Jiayang Zhang, Ji Guo, Jiachen Li +2

Unconditional diffusion checkpoint merging assumes benign sources, yet a compromised public checkpoint can transfer a dormant backdoor while clean generation appears normal. Mitiga…

cs.CR2026

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…

cs.CR2026

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…

cs.LG2026

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…

cs.CR2025

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,…

cs.CR2025★ 1 cited

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…

cs.CV2025

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…

cs.CR2024★ 2 cited

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…

cs.CR2024

Stealthy Targeted Backdoor Attacks against Image Captioning

Wenshu Fan, Hongwei Li, Wenbo Jiang +3

In recent years, there has been an explosive growth in multimodal learning. Image captioning, a classical multimodal task, has demonstrated promising applications and attracted ext…

cs.CL2024★ 3 cited

Watch Out for Your Guidance on Generation! Exploring Conditional Backdoor Attacks against Large Language Models

Jiaming He, Wenbo Jiang, Guanyu Hou +3

Mainstream backdoor attacks on large language models (LLMs) typically set a fixed trigger in the input instance and specific responses for triggered queries. However, the fixed tri…