Safety Measurements for Fine-tuned LLMs Should be Grounded in Capability

Krishnapriya Vishnubhotla; Hillary Dawkins; Isar Nejadgholi; Svetlana Kiritchenko

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arXiv · 2606.03648

Safety Measurements for Fine-tuned LLMs Should be Grounded in Capability

Krishnapriya Vishnubhotla·Hillary Dawkins·Isar Nejadgholi·Svetlana Kiritchenko

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Abstract

Adapting foundation large language models to a user's task or preferred style through fine-tuning can result in compromising the model's safety. Previous works examined the effects of fine-tuning on model safety in limited and seemingly random experimental settings. We argue that anchoring fine-tuning to a specific capability goal is essential for avoiding arbitrary empirical choices, allowing us to draw meaningful conclusions about safety impacts, and to compare mitigation methods on a consistent basis. We conduct a multi-dimensional evaluation of the effects of fine-tuning on model behavior by focusing on capability as well as safety. Our results surface important issues that (1) fine-tuned models can produce incoherent generations in response to safety prompts, (2) automated safety judgments are unreliable for such incoherent outputs, and (3) the conclusions about the effects of fine-tuning can change depending on the choice of safety benchmark as well as the safety evaluator.

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Krishnapriya Vishnubhotla, Hillary Dawkins, Isar Nejadgholi, Svetlana Kiritchenko. 2026-06-02. Safety Measurements for Fine-tuned LLMs Should be Grounded in Capability. https://arxiv.org/abs/2606.03648

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