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

• URL: http://arxiv.org/abs/2509.15260v2
• Date: Tue, 23 Sep 2025 07:50:08 GMT
• Title: Toxicity Red-Teaming: Benchmarking LLM Safety in Singapore's Low-Resource Languages
• Abstract summary: Large Language Models (LLMs) have transformed natural language processing, but their safety mechanisms remain under-explored in low-resource, multilingual settings.<n>We introduce textsfSGToxicGuard, a novel dataset and evaluation framework for benchmarking LLM safety in Singapore's diverse linguistic context.<n>We conduct extensive experiments with state-of-the-art multilingual LLMs, and the results uncover critical gaps in their safety guardrails.
• Score: 57.059267233093465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 \textsf{SGToxicGuard}, a novel dataset and evaluation framework for benchmarking LLM safety in Singapore's diverse linguistic context, including Singlish, Chinese, Malay, and Tamil. SGToxicGuard adopts a red-teaming approach to systematically probe LLM vulnerabilities in three real-world scenarios: \textit{conversation}, \textit{question-answering}, and \textit{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.\footnote{Link to the dataset: https://github.com/Social-AI-Studio/SGToxicGuard.} \textcolor{red}{Disclaimer: This paper contains sensitive content that may be disturbing to some readers.}

Related papers

• The Illusion of Cross-Lingual Safety in Low-Resource Languages [7.471112976694495]
  Safety alignment in large language models (LLMs) is largely developed in English, assuming these safeguards generalize across multilingual settings.<n>We investigate cross-lingual safety transfer in four African languages, Twi, Hausa, Amharic, and Swahili, using LoDNA, a new safety dataset that pairs literal translations with culturally localized prompts.<n>Our experimental results show that cross-lingual safety transfer is severely limited; harmful prompts retain less than 10% of the English refusal signal across most language-model pairs.
  arXiv  Detail & Related papers  (2026-08-11T17:05:26Z)
• SurakshaEval: An Indic Safety Benchmark for Multilingual LLMs [56.34354368047577]
  Existing safety evaluation datasets for large language models (LLMs) predominantly focus on English and Western contexts.<n>We introduce SurakshaEval, a novel safety benchmark composed of human-written prompts spanning real-world scenarios.<n>We benchmark a broad range of state-of-the-art LLMs on SurakshaEval, establish baseline safety performance, and identify recurring failure modes.
  arXiv  Detail & Related papers  (2026-08-08T02:03:33Z)
• LLM Safety Alignment in Low-Resource Languages: A Systematic Literature Review [9.895109697164283]
  Large Language Models (LLMs) have achieved substantial progress in safety alignment.<n>Their safety guarantees remain significantly weaker in low-resource and multilingual settings.
  arXiv  Detail & Related papers  (2026-07-20T21:11:00Z)
• Schützen: Evaluating LLM Safety in Bulgarian and German Contexts [53.865251738592605]
  This paper introduces Schtzen: a German--Bulgarian safety dataset designed to assess model answerability under risk.<n> Experiments with multilingual and language-specific LLMs reveal pronounced cross-language differences in safety behavior.
  arXiv  Detail & Related papers  (2026-06-09T18:01:19Z)
• Toxicity-Aware Few-Shot Prompting for Low-Resource Singlish Translation [3.7678366606419345]
  Translating toxic content between low-resource language pairs poses challenges due to scarce parallel data and safety filters that sanitize offensive expressions.<n>We propose a two-stage framework for toxicity-preserving translation, demonstrated on a code-mixed Singlish safety corpus.<n>By positioning Singlish as a testbed for inclusive NLP, we underscore the importance of preserving sociolinguistic nuance in real-world applications.
  arXiv  Detail & Related papers  (2025-07-16T06:58:02Z)
• RabakBench: Scaling Human Annotations to Construct Localized Multilingual Safety Benchmarks for Low-Resource Languages [3.7678366606419345]
  RabakBench is a new multilingual safety benchmark localized to Singapore's unique linguistic context.<n>The benchmark dataset, including the human-verified translations, and evaluation code are publicly available.
  arXiv  Detail & Related papers  (2025-07-08T13:37:25Z)
• Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models [0.0]
  We show how surprisingly strong attacks can be created by altering just a few characters and using a small proxy model for word importance calculation.<n>We find that these character and word-level attacks drastically alter the predictions of different LLMs.<n>We validate our attack construction methodology on Polish, a low-resource language, and find potential vulnerabilities of LLMs in this language.
  arXiv  Detail & Related papers  (2025-06-09T11:09:39Z)
• MrGuard: A Multilingual Reasoning Guardrail for Universal LLM Safety [56.79292318645454]
  Large Language Models (LLMs) are susceptible to adversarial attacks such as jailbreaking.<n>This vulnerability is exacerbated in multilingual settings, where multilingual safety-aligned data is often limited.<n>We introduce a multilingual guardrail with reasoning for prompt classification.
  arXiv  Detail & Related papers  (2025-04-21T17:15:06Z)
• LLMs Lost in Translation: M-ALERT uncovers Cross-Linguistic Safety Inconsistencies [63.10843814055688]
  M-ALERT is a benchmark that evaluates the safety of Large Language Models in five languages.<n>M-ALERT includes 15k high-quality prompts per language, totaling 75k, with category-wise annotations.<n>Our experiments on 39 state-of-the-art LLMs highlight the importance of language-specific safety analysis.
  arXiv  Detail & Related papers  (2024-12-19T16:46:54Z)
• Benchmarking LLM Guardrails in Handling Multilingual Toxicity [57.296161186129545]
  We introduce a comprehensive multilingual test suite, spanning seven datasets and over ten languages, to benchmark the performance of state-of-the-art guardrails. We investigate the resilience of guardrails against recent jailbreaking techniques, and assess the impact of in-context safety policies and language resource availability on guardrails' performance. Our findings show that existing guardrails are still ineffective at handling multilingual toxicity and lack robustness against jailbreaking prompts.
  arXiv  Detail & Related papers  (2024-10-29T15:51:24Z)
• Think Carefully and Check Again! Meta-Generation Unlocking LLMs for Low-Resource Cross-Lingual Summarization [108.6908427615402]
  Cross-lingual summarization ( CLS) aims to generate a summary for the source text in a different target language.<n>Currently, instruction-tuned large language models (LLMs) excel at various English tasks.<n>Recent studies have shown that LLMs' performance on CLS tasks remains unsatisfactory even with few-shot settings.
  arXiv  Detail & Related papers  (2024-10-26T00:39:44Z)
• Multilingual Jailbreak Challenges in Large Language Models [96.74878032417054]
  In this study, we reveal the presence of multilingual jailbreak challenges within large language models (LLMs) We consider two potential risky scenarios: unintentional and intentional. We propose a novel textscSelf-Defense framework that automatically generates multilingual training data for safety fine-tuning.
  arXiv  Detail & Related papers  (2023-10-10T09:44:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.