8 papers
Dual-Adversarial Safety Alignment: Cultivating Intrinsic Threat Comprehension in LRMs
Hongli Shen, Shaopeng Fu, Qinbo Zhang +2
Large reasoning models (LRMs) achieve remarkable success on complex tasks but remain vulnerable to harmful prompts that induce unsafe outputs. Recent methods align LRMs using direc…
Accelerating Suffix Jailbreak attacks with Prefix-Shared KV-cache
Xinhai Wang, Shaopeng Fu, Shu Yang +3
Suffix jailbreak attacks serve as a systematic method for red-teaming Large Language Models (LLMs) but suffer from prohibitive computational costs, as a large number of candidate s…
Benign Overfitting in Adversarial Training for Vision Transformers
Jiaming Zhang, Meng Ding, Shaopeng Fu +2
Despite the remarkable success of Vision Transformers (ViTs) across a wide range of vision tasks, recent studies have revealed that they remain vulnerable to adversarial examples,…
Understanding and Improving Continuous Adversarial Training for LLMs via In-context Learning Theory
Shaopeng Fu, Di Wang
Adversarial training (AT) is an effective defense for large language models (LLMs) against jailbreak attacks, but performing AT on LLMs is costly. To improve the efficiency of AT f…
CoLA: A Choice Leakage Attack Framework to Expose Privacy Risks in Subset Training
Qi Li, Cheng-Long Wang, Yinzhi Cao +1
Training models on a carefully chosen portion of data rather than the full dataset is now a standard preprocess for modern ML. From vision coreset selection to large-scale filterin…
RefineRL: Advancing Competitive Programming with Self-Refinement Reinforcement Learning
Shaopeng Fu, Xingxing Zhang, Li Dong +2
While large language models (LLMs) have demonstrated strong performance on complex reasoning tasks such as competitive programming (CP), existing methods predominantly focus on sin…