Register Shifts Break LLM Safety: A Bengali Benchmark with Culturally Grounded Harms
Abstract
BanglaSafe evaluates Bengali LLM safety across culturally grounded harm categories, revealing high unsafe response rates driven primarily by writing style rather than language, and showing that existing safety classifiers fail on nearly half of Bengali cases.
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.
Get this paper in your agent:
hf papers read 2608.22335 Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash Models citing this paper 0
No model linking this paper
Datasets citing this paper 2
nymtheescobar/BanglaSafe
Spaces citing this paper 0
No Space linking this paper
Collections including this paper 0
No Collection including this paper