SafeAnchor: Preventing Cumulative Safety Erosion in Continual Domain Adaptation of Large Language Models

Safety alignment in large language models is remarkably shallow: it is concentrated in the first few output tokens and reversible by fine-tuning on as few as 100 adversarial examples. This fragility becomes critical in real-world deployment, where models undergo sequential adaptation across domains such as medicine, law, and code, causing safety guardrails to erode cumulatively. Yet all existing safety-preserving methods target only single-task fine-tuning, leaving the multi-domain sequential se

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ethics.ai (20 April 2026), “SafeAnchor: Preventing Cumulative Safety Erosion in Continual Domain Adaptation of Large Language Models,” evidence record 5642, https://ethics.ai/record/5642 (originally published by arXiv).

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