mlsanityData to 7 Oct 2026

Can LLM Safety Be Ensured by Constraining Parameter Regions?

arXiv 2602.17696 ↗First posted 6 Feb 2026cs.LG cs.AIcited by 0

On the map: LLM safety and security → Safety alignment, refusal and guardrails

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security. Builds on 42 of its 60 references that are in the map. First posted on arXiv 6 Feb 2026.

Builds on

42 of 60 references.

Closest ancestors · 9

Same direction, newest first

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Other references · 33

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