Register Shifts Break LLM Safety: A Bengali Benchmark with Culturally Grounded Harms
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Computer Science > Computation and Language
arXiv:2608.22335 (cs)
[Submitted on 23 Aug 2026]
Title:Register Shifts Break LLM Safety: A Bengali Benchmark with Culturally Grounded Harms
Authors:Naymul Islam, Nusrat Jahan Lia, Shubhashis Roy Dipta, Sabik Bin Sultan, Abdullah Khan Zehady
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Abstract:Bengali is the seventh-most-spoken language globally, yet LLM safety evaluation remains overwhelmingly English-centric. We introduce BanglaSafe, a benchmark of 879 Bengali prompts combining 309 natively authored prompts with 570 expert-reviewed prompts, spanning 17 culturally grounded harm categories and five prompting conditions that vary language, writing style, and authority framing. Evaluating 18 frontier LLMs, we find that over half of all responses are unsafe or partially unsafe (53.6%) while 14.7% contains strictly harmful content, and that the strongest observed effect is not the switch from English to Bengali but the choice of writing style within Bengali: the same harmful request phrased as a formal newspaper investigation succeeds 17 percentage points more often than the same request phrased as a casual message, with no adversarial engineering involved. We further show that existing safety classifiers struggle to reliably evaluate Bengali content, with even frontier models failing on nearly half of all cases.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.22335 [cs.CL] |
| (or arXiv:2608.22335v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.22335
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Shubhashis Roy Dipta [view email][v1] Sun, 23 Aug 2026 10:09:00 UTC (814 KB)
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View a PDF of the paper titled Register Shifts Break LLM Safety: A Bengali Benchmark with Culturally Grounded Harms, by Naymul Islam and 4 other authors
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