微調安全對齊退化

Risk Description

Attackers who obtain open-source or leaked model weights can destroy the models original safety alignment training with minimal malicious fine-tuning data. Research shows that merely dozens to hundreds of malicious fine-tuning samples can significantly degrade model safety guardrails. This attack enables models to bypass content filters, output harmful information, or execute prohibited tasks while normal task performance remains largely unaffected.