SSRFT trains models to internalize safety, not just refuse attacks
A new arXiv paper proposes SSRFT, a safety-tuning method that aims to make models apply a safe role across unfamiliar jailbreaks, rather than memorize refusal patterns. It builds training examples from psychometric questions, limited jailbreak prompts, and a safe-role description, then expands and validates role-consistent answers. Tests across Base and Instruct models report stronger resistance to prefilling attacks, better generalization to unseen jailbreak domains, less over-refusal on benign queries, and preserved general capabilities than standard SFT.