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Beyond Refusal Patterns: Safe-Role Internalization for Robust and Generalizable LLM Safety Alignment

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 08/10/2026, 02:18:24 UTC.

Briefings

This article presents SSRFT, a new framework for LLM safety alignment that replaces explicit refusal mechanisms with the internalization of a predefined safe role. Using psychometric questions and a safe-role description, the authors synthesize role-consistent responses across Base and Instruct models. Experiments demonstrate that SSRFT achieves more robust and generalizable safety alignment than standard SFT, showing greater resistance to prefilling attacks and better generalization to unseen jailbreak domains while reducing over-refusal on benign queries.

Key points

• SSRFT constructs a Safe-Role Question-Answer (SRQA) dataset from psychometric questions, limited jailbreak prompts, and a safe-role description.
• Role-consistent responses are synthesized, validated, and expanded into diverse scenarios to enable models to internalize safety-oriented values rather than explicit refusal patterns.

Context

Large Language Models have achieved remarkable capabilities but remain vulnerable to jailbreak attacks that elicit harmful or unsafe outputs. Existing safety alignment approaches, including Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), often require substantial attack-specific supervision and computational resources, while remaining susceptible to shallow safety alignment and over-refusal. This paper introduces SSRFT as an alternative to these methods.

Limitations

The evidence is from a preprint (arXiv:2610.07023v1) and contains examples of harmful and toxic language. Results are based on experiments across multiple Base and Instruct models, but specific model names, exact performance metrics, and the precise scope of the safe-role description are not detailed in the provided text.

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