arXiv:2606.01322v1 Announce Type: new Abstract: Safety evaluation of Large Language Models (LLMs) remains heavily English-centric, leaving Low-Resource Languages (LRLs), particularly African ones, critically underexplored. We introduce TUKABENCH, a jailbreak benchmark for seven African languages that extends JailbreakBench (JBB) beyond direct translation through four settings: human translation of JBB prompts, English adaptation to African contexts followed by human translation, human-curated prompts validated through interactions with GPT-5.2, and code-switched prompts combining English and A
The proliferation of advanced LLMs necessitates broader safety evaluations, and the lack of multilingual benchmarks is becoming a critical bottleneck as AI adoption expands globally.
This benchmark highlights the critical need for culturally grounded AI safety, moving beyond English-centric development and addressing the unique risks and requirements of diverse linguistic contexts.
AI safety evaluation is no longer solely an English-language challenge; the development of culturally specific benchmarks for African languages sets a precedent for more inclusive and robust AI development.
- · African AI developers
- · Low-Resource Language communities
- · AI safety researchers
- · LLM developers ignoring linguistic diversity
- · English-only AI safety paradigms
The TukaBench benchmark will enable more effective identification and mitigation of jailbreaking vulnerabilities in LLMs for African languages.
Improved safety in African-language LLMs could accelerate AI adoption and innovation across the continent, fostering local technological ecosystems.
This could lead to a global shift towards 'localized AI safety protocols,' where every significant linguistic or cultural group demands bespoke benchmarking and ethical considerations, creating a complex regulatory and development landscape.
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