What is in Your Safe Data? Identifying Benign Data that Breaks Safety

Understand

Current Large Language Models (LLMs), even those tuned for safety and alignment, are susceptible to jailbreaking.

• Some have found that just further fine-tuning an aligned model with benign data (i.e., data without harmful content) surprisingly leads to substantial degradation in safety.
• We delve into the data-centric aspects of why benign fine-tuning inadvertently contributes to jailbreaking.
• First, we represent fine-tuning data through two lenses: representation and gradient spaces.