1 citations · 1 across the 7 of their papers we have counts for
8 papers
RISA: Response Inspection and Selective Actions for Refusal Calibration in Large Language Models
Wenhan Chang, Tianqing Zhu, Ping Xiong +2
Reliable refusal behavior requires Large Language Models (LLMs) to reject harmful prompts with only answering benign ones. Incorrect refusal behavior can either expose users to har…
Safety Context Injection: Inference-Time Safety Alignment via Static Filtering and Agentic Analysis
Zhenhao Xu, Wenhan Chang, Yichuan Chen +3
Large Reasoning Models (LRMs) improve performance on complex tasks, but they also make safety control harder at deployment time. In black-box settings, defenders cannot modify mode…
Unreal Thinking: Chain-of-Thought Hijacking via Two-stage Backdoor
Wenhan Chang, Tianqing Zhu, Ping Xiong +2
Large Language Models (LLMs) are increasingly deployed in settings where Chain-of-Thought (CoT) is interpreted by users. This creates a new safety risk: attackers may manipulate th…
Chain-of-Lure: A Universal Jailbreak Attack Framework using Unconstrained Synthetic Narratives
Wenhan Chang, Tianqing Zhu, Yu Zhao +3
In the era of rapid generative AI development, interactions with large language models (LLMs) pose increasing risks of misuse. Prior research has primarily focused on attacks using…
Large Language Models Merging for Enhancing the Link Stealing Attack on Graph Neural Networks
Faqian Guan, Tianqing Zhu, Wenhan Chang +2
Graph Neural Networks (GNNs), specifically designed to process the graph data, have achieved remarkable success in various applications. Link stealing attacks on graph data pose a…
Zero-shot Class Unlearning via Layer-wise Relevance Analysis and Neuronal Path Perturbation
Wenhan Chang, Tianqing Zhu, Ping Xiong +3
In the rapid advancement of artificial intelligence, privacy protection has become crucial, giving rise to machine unlearning. Machine unlearning is a technique that removes specif…
Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning
Wenhan Chang, Tianqing Zhu, Heng Xu +2
Machine unlearning aims to remove the influence of specified training data or knowledge from a trained model without requiring full retraining. This capability is particularly impo…
Generative Adversarial Networks Unlearning
Hui Sun, Tianqing Zhu, Wenhan Chang +1
As machine learning continues to develop, and data misuse scandals become more prevalent, individuals are becoming increasingly concerned about their personal information and are a…