Safety Drift After Fine-Tuning: Evidence from High-Stakes Domains

2026-04-27

AI Summary

Current governance paradigms typically assume that the safety properties of foundation models are preserved after fine-tuning; however, this assumption lacks systematic validation in high-stakes domains such as healthcare and law. This study presents the first comprehensive, multidimensional safety evaluation of 100 fine-tuned models—including widely deployed domain-specific models and controlled experimental variants—across both general and domain-specific benchmarks. The findings reveal that fine-tuning frequently induces significant and heterogeneous shifts in safety: improvements along certain metrics often coincide with severe degradation in others. These results challenge the prevailing practice of relying solely on base model safety assessments and underscore the critical need for independent, thorough safety re-evaluation of fine-tuned models before deployment in high-risk applications.

Abstract

Foundation models are routinely fine-tuned for use in particular domains, yet safety assessments are typically conducted only on base models, implicitly assuming that safety properties persist through downstream adaptation. We test this assumption by analyzing the safety behavior of 100 models, including widely deployed fine-tunes in the medical and legal domains as well as controlled adaptations of open foundation models alongside their bases. Across general-purpose and domain-specific safety benchmarks, we find that benign fine-tuning induces large, heterogeneous, and often contradictory changes in measured safety: models frequently improve on some instruments while degrading on others, with substantial disagreement across evaluations. These results show that safety behavior is not stable under ordinary downstream adaptation, raising critical questions about governance and deployment practices centered on base-model evaluations. Without explicit re-evaluation of fine-tuned models in deployment-relevant contexts, such approaches fall short of adequately managing downstream risk, overlooking practical sources of harm -- failures that are especially consequential in high-stakes settings and challenge current accountability paradigms.

Problem

• safety drift
• fine-tuning
• foundation models
• high-stakes domains
• safety evaluation

Innovation

• safety drift
• fine-tuning
• foundation models
• high-stakes domains
• safety evaluation