Skin-Deep: A Geometric Diagnostic for Alignment Fragility in Large Language Model Representations

Alignment tuning is meant to make harmful-request refusal robust, yet this safety behavior can be erased by a small set of benign fine-tuning examples. This is a deployment risk for open-weight models because a checkpoint can pass refusal tests at release time and later lose refusal under low-cost downstream fine-tuning. Prior work has established these refusal failures, but existing studies do not show how to detect this fragility in the aligned model itself before an attack or fine-tuning inte

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ethics.ai (21 June 2026), “Skin-Deep: A Geometric Diagnostic for Alignment Fragility in Large Language Model Representations,” evidence record 728, https://ethics.ai/record/728 (originally published by arXiv).

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