Skin-Deep: A Geometric Diagnostic for Alignment Fragility in Large Language Model Representations
Refusal on a safety benchmark does not reveal how stable that behavior will remain after model updates. Benign downstream fine-tuning can weaken refusal, yet behavioral evaluations typically expose this fragility only after an intervention. We introduce SKIN-DEEP, a geometric diagnostic that examines the unmodified model's residual-stream activations. It compares aligned and base checkpoints to identify safety-separating directions, tests their behavioral relevance through ablation, and summarizes the layer-wise pattern in the Geometric Fragility Score (GFS). Across twenty-one instruction-tuned models, harmful requests and benign instructions exhibit a recurring low-rank separation patter...
| Authors | Dongyub Jude Lee, Jungseob Lee, Seungyoon Lee et al. |
|---|---|
| Primary category | cs.AI |
| Published | 21 June 2026 |
| Last updated at source | 1 October 2026 |
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