Red-Teaming Large Language Models in Low-Resource African Languages: A Case Study of Yoruba and Nigerian Pidgin

Summary

This study tests six large language models with 400 adversarial prompts in Yoruba and Nigerian Pidgin, scored by native-speaker annotators. Models that rejected up to 69% of English prompts rejected as few as 0% in Yoruba, largely because they failed to understand the prompts rather than being safer. Attack success rates rose for most models in the Nigerian languages, showing that English-centric safety alignment does not transfer to these languages.

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