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arXiv · 2603.01297

I Can't Believe It's Not Robust: Catastrophic Collapse of Safety Classifiers under Embedding Drift

Subramanyam Sahoo·Vinija Jain·Divya Chaudhary·Aman Chadha
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Abstract

Instruction tuned reasoning models are increasingly deployed with safety classifiers trained on frozen embeddings, assuming representation stability across model updates. We systematically investigate this assumption and find it fails: normalized perturbations of magnitude $\sigma=0.02$ (corresponding to $\approx 1^\circ$ angular drift on the embedding sphere) reduce classifier performance from $85\%$ to $50\%$ ROC-AUC. Critically, mean confidence only drops $14\%$, producing dangerous silent failures where $72\%$ of misclassifications occur with high confidence, defeating standard monitoring. We further show that instruction-tuned models exhibit 20$\%$ worse class separability than base models, making aligned systems paradoxically harder to safeguard. Our findings expose a fundamental fragility in production AI safety architectures and challenge the assumption that safety mechanisms transfer across model versions.

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Subramanyam Sahoo, Vinija Jain, Divya Chaudhary, Aman Chadha. 2026-03-01. I Can't Believe It's Not Robust: Catastrophic Collapse of Safety Classifiers under Embedding Drift. https://arxiv.org/abs/2603.01297

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