3 papers
cs.LG2026
Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning
Weitao Feng, Lixu Wang, Peizhuo Lv +5
As large language models (LLMs) continue to grow in capability, so do the risks of harmful misuse through fine-tuning. While most prior studies assume that attackers rely on superv…
cs.LG2025
Beyond Sharp Minima: Robust LLM Unlearning via Feedback-Guided Multi-Point Optimization
Wenhan Wu, Zheyuan Liu, Chongyang Gao +2
Current LLM unlearning methods face a critical security vulnerability that undermines their fundamental purpose: while they appear to successfully remove sensitive or harmful knowl…
cs.LG2025
On Large Language Model Continual Unlearning
Chongyang Gao, Lixu Wang, Kaize Ding +3
While large language models have demonstrated impressive performance across various domains and tasks, their security issues have become increasingly severe. Machine unlearning has…