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arXiv · 2512.05501

SEA-SafeguardBench: Evaluating AI Safety in SEA Languages and Cultures

Panuthep Tasawong·Jian Gang Ngui·Alham Fikri Aji·Trevor Cohn·Peerat Limkonchotiwat
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Abstract

Safeguard models help large language models (LLMs) detect and block harmful content, but most evaluations remain English-centric and overlook linguistic and cultural diversity. Existing multilingual safety benchmarks often rely on machine-translated English data, which fails to capture nuances in low-resource languages. Southeast Asian (SEA) languages are underrepresented despite the region's linguistic diversity and unique safety concerns, from culturally sensitive political speech to region-specific misinformation. Addressing these gaps requires benchmarks that are natively authored to reflect local norms and harm scenarios. We introduce SEA-SafeguardBench, the first human-verified safety benchmark for SEA, covering eight languages, 21,640 samples, across three subsets: general, in-the-wild, and content generation. The experimental results from our benchmark demonstrate that even state-of-the-art LLMs and guardrails are challenged by SEA cultural and harm scenarios and underperform when compared to English texts.

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Panuthep Tasawong, Jian Gang Ngui, Alham Fikri Aji, Trevor Cohn, Peerat Limkonchotiwat. 2025-12-05. SEA-SafeguardBench: Evaluating AI Safety in SEA Languages and Cultures. https://arxiv.org/abs/2512.05501

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