Toxicity Red-Teaming: Benchmarking LLM Safety in Singapore's Low-Resource Languages

The advancement of Large Language Models (LLMs) has transformed natural language processing; however, their safety mechanisms remain under-explored in low-resource, multilingual settings. Here, we aim to bridge this gap. In particular, we introduce SGToxic-Guard, a novel dataset and evaluation framework for benchmarking LLM safety in Singapore's diverse linguistic context, including Singlish, Chinese, Malay, and Tamil. SGToxi-cGuard adopts a red-teaming approach to systematically probe LLM vulnerabilities in three real-world scenarios: conversation, questionanswering, and content composition. We conduct extensive experiments with state-of-the-art multilingual LLMs, and the results uncover critical gaps in their safety guardrails. By offering actionable insights into cultural sensitivity and toxicity mitigation, we lay the foundation for safer and more inclusive AI systems in linguistically diverse environments. 1 Disclaimer: This paper contains sensitive content that may be disturbing to some readers.