We introduce a multilingual benchmark for evaluating large language models (LLMs) on hate speech detection and generation in low-resource Ethiopian languages: Afaan Oromo, Amharic and Tigrigna, and English (both monolingual and code-mixed). Using a balanced and expert-annotated dataset, we assess five state-of-the-art LLM families across both tasks. Our results show that while LLMs perform well on English detection, their performance on low-resource languages is significantly weaker, revealing that increasing model size alone does not ensure multilingual robustness. More critically, we find that all models, including closed and open-source variants, can be prompted to generate profiled hate speech with minimal resistance. These findings underscore the dual risk of exclusion and exploitation: LLMs fail to protect low-resource communities while enabling scalable harm against them. We make our evaluation framework available to facilitate future research on multilingual model safety and ethical robustness.

Hate speech on social media has proliferated in Ethiopia. To support studies aimed at investigating the targets and types of hate speech circulating in the Ethiopian context, we developed a new fine-grained annotation scheme that captures three elements of hate speech: the target (i.e., any groups with protected characteristics), type (i.e., the method of abuse) and nature (i.e., the style of the language used). We also developed a new lexicon of hate speech-related keywords in the four most prominent languages found on Ethiopian social media: Amharic, Afaan Oromo, English and Tigrigna. These keywords enabled us to retrieve social media posts (also in the same four languages) from three platforms (i.e., X, Telegram and Facebook), that are likely to contain hate speech. Experts in the Ethiopian context then manually annotated a sample of those retrieved posts, obtaining fair to moderate inter-annotator agreement. The resulting annotations formed the basis of a case study of which groups tend to be targeted by particular types of hate speech or by particular styles of hate speech language.