Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey

Understand

Recent research demonstrates that the nascent fine-tuning-as-a-service business model exposes serious safety concerns: fine-tuning with a few harmful data uploaded from the users can compromise the safety alignment of the model.

• The attack, known as harmful fine-tuning attack, has generated broad research interests in both academia and industry.
• In this paper, we first systematically formulate the threat model and basic assumptions of harmful fine-tuning.
• Then, we provide a comprehensive review of harmful fine-tuning from three fundamental perspectives: attack setting, defense design, and evaluation methodology.