@inproceedings{bugaud-2026-domain,
    title = "Domain-Dependent Safety Behavior in Open-Weight {LLM}s: An Empirical Study Across Seven Ethical Domains",
    author = "Bugaud, Zacharie",
    editor = "Chang, Kai-Wei  and
      Mehrabi, Ninareh  and
      Krishna, Satyapriya  and
      Das, Anubrata  and
      Dhamala, Jwala  and
      Cao, Yang Trista  and
      Kumarage, Tharindu  and
      Ramakrishna, Anil  and
      Christodoulopoulos, Christos  and
      Wan, Yixin  and
      Galystan, Aram  and
      Kumar, Anoop  and
      Gupta, Rahul",
    booktitle = "Proceedings of the 6th Workshop on Trustworthy {NLP} ({T}rust{NLP} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.trustnlp-main.42/",
    pages = "557--562",
    ISBN = "979-8-89176-418-7",
    abstract = "We present a systematic study of domain-dependent safety behavior in open-weight LLMs: 7 standardized experiments across 7 ethical domains, testing 5 models (12B{--}70B) in 4,200 interactions with dual-judge validation. Using a dual-condition methodology, each scenario tested in both an analytical framing (identify the harm) and an operational framing (help commit the harm), we find compliance rates vary from 14.7{\%} (human trafficking) to 85.7{\%} (surveillance design), a 71-percentage-point span with non-overlapping cluster-bootstrapped 95{\%} CIs. Domain accounts for 36{\%} of pair-level variance in harm scores, with scenario (26{\%}) exceeding model identity (15{\%}). A stable model safety hierarchy persists across domains (mean Spearman {\ensuremath{\rho}} = 0.68). These findings demonstrate that safety alignment is not a general capability: aggregate safety scores mask critical domain-level variation, motivating domain-specific safety auditing for trustworthy deployment."
}