Disentangling Intent from Role: Adversarial Self-Play for Persona-Invariant Safety Alignment

The growing capabilities of large language models (LLMs) have driven their widespread deployment across diverse domains, even in potentially high-risk scenarios. Despite advances in safety alignment techniques, current models remain vulnerable to emerging persona-based jailbreak attacks. Existing research on persona-based jailbreak has primarily focused on attack iterations, yet it lacks systemic and mechanistic constraints on the defense side. To address this challenge, we propose Persona-Invar

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ethics.ai (3 May 2026), “Disentangling Intent from Role: Adversarial Self-Play for Persona-Invariant Safety Alignment,” evidence record 5047, https://ethics.ai/record/5047 (originally published by arXiv).

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