The Geometry of Refusal: Linear Instability in Safety-Aligned LLMs

Modern Large Language Models (LLMs) rely on extensive safety alignment, yet the mechanistic basis of refusal remains opaque. In this work, we investigate whether safety compliance is a deep semantic decision or a manipulable linear feature. We introduce Contrastive Logit Steering (CLS), a zero-optimization framework that isolates the "refusal direction" by contrasting hidden states derived from safe and unrestricted system prompts. Unlike representation engineering methods that intervene on inte

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ethics.ai (21 June 2026), “The Geometry of Refusal: Linear Instability in Safety-Aligned LLMs,” evidence record 727, https://ethics.ai/record/727 (originally published by arXiv).

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