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[2602.17696] Can LLM Safety Be Ensured by Constraining Parameter Regions?
Summary
This article explores the effectiveness of identifying 'safety regions' in large language models (LLMs) by evaluating various methods across different parameter granularities. The findings indicate limited overlap in identified safety regions, raising questions about current t...
Why It Matters
As LLMs become increasingly integrated into various applications, ensuring their safety is paramount. This research highlights the challenges in reliably identifying safety regions, which is crucial for developing safer AI systems and mitigating potential risks associated with their deployment.
Key Takeaways
- Current methods for identifying safety regions in LLMs show low to moderate overlap.
- Refining safety regions with utility datasets significantly reduces overlap.
- The study suggests that existing techniques may not reliably identify stable safety regions.
- Understanding safety regions is essential for improving LLM safety.
- The findings call for further research into more effective identification methods.
Computer Science > Machine Learning arXiv:2602.17696 (cs) [Submitted on 6 Feb 2026] Title:Can LLM Safety Be Ensured by Constraining Parameter Regions? Authors:Zongmin Li, Jian Su, Farah Benamara, Aixin Sun View a PDF of the paper titled Can LLM Safety Be Ensured by Constraining Parameter Regions?, by Zongmin Li and 3 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) are often assumed to contain ``safety regions'' -- parameter subsets whose modification directly influences safety behaviors. We conduct a systematic evaluation of four safety region identification methods spanning different parameter granularities, from individual weights to entire Transformer layers, across four families of backbone LLMs with varying sizes. Using ten safety identification datasets, we find that the identified safety regions exhibit only low to moderate overlap, as measured by IoU. The overlap drops significantly when the safety regions are further refined using utility datasets (\ie non-harmful queries). These results suggest that current techniques fail to reliably identify a stable, dataset-agnostic safety region. Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2602.17696 [cs.LG] (or arXiv:2602.17696v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.17696 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zongmin Li [view email] [v1] Fri, 6 Feb 2026 16:09:45 UTC (861 KB) Ful...