1 citations · 3 across the 19 of their papers we have counts for
5 papers · 1 filter
Mitigating the Safety-utility Trade-off in LLM Alignment via Adaptive Safe Context Learning
Yanbo Wang, Minzheng Wang, Jian Liang +3
While reasoning models have achieved remarkable success in complex reasoning tasks, their increasing power necessitates stringent safety measures. For safety alignment, the core ch…
Test-Time Immunization: A Universal Defense Framework Against Jailbreaks for (Multimodal) Large Language Models
Yongcan Yu, Yanbo Wang, Ran He +1
While (multimodal) large language models (LLMs) have attracted widespread attention due to their exceptional capabilities, they remain vulnerable to jailbreak attacks. Various defe…
Do We Really Need Curated Malicious Data for Safety Alignment in Multi-modal Large Language Models?
Yanbo Wang, Jiyang Guan, Jian Liang +1
Multi-modal large language models (MLLMs) have made significant progress, yet their safety alignment remains limited. Typically, current open-source MLLMs rely on the alignment inh…
Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks
Yanbo Wang, Jian Liang, Ran He
Gradient inversion attacks aim to reconstruct local training data from intermediate gradients exposed in the federated learning framework. Despite successful attacks, all previous…
Protecting Model Adaptation from Trojans in the Unlabeled Data
Lijun Sheng, Jian Liang, Ran He +2
Model adaptation tackles the distribution shift problem with a pre-trained model instead of raw data, which has become a popular paradigm due to its great privacy protection. Exist…