2025
Antidote: Post-fine-tuning Safety Alignment for Large Language Models against Harmful Fine-tuning Attack
ICML 2025poster
Safety aligned Large Language Models (LLMs) are vulnerable to harmful fine-tuning attacks -- a few harmful data mixed in the fine-tuning dataset can break the LLMs's safety alignment. While several defenses have been proposed, our evaluation shows that existing defenses fail \textit{when some specif…