Safety alignment in LLMs is largely an English-language phenomenon—models trained to refuse harmful requests in English fail to do so consistently in low-resource languages, creating a critical security gap that current multilingual approaches don't address.
This paper reveals that safety guardrails in large language models don't reliably transfer to low-resource African languages like Twi, Hausa, Amharic, and Swahili.