As Large Language Models (LLMs) continue to evolve, ensuring their safety across multiple languages has become a critical concern. While LLMs demonstrate impressive capabilities in English, their safety mechanisms may not generalize effectively to other languages, leading to disparities in toxicity detection, bias mitigation, and harm prevention. This systematic review examines the multilingual safety of LLMs by synthesizing findings from recent studies that evaluate their robustness across diverse linguistic and cultural contexts beyond English language. Our review explores the methodologies used to assess multilingual safety, identifies challenges such as dataset availability and evaluation biases. Based on our analysis we highlight gaps in multilingual safety research and provide recommendations for future work. This review aims to contribute to the development of fair and effective safety mechanisms for LLMs across all languages. We provide the extracted data in an interactive Streamlit dashboard, enabling transparent access to the raw data and allowing for continuous updates.

Alignment is the critical process of minimizing harmful outputs by teaching large language models (LLMs) to prefer safe, helpful and appropriate responses. While the majority of alignment research and datasets remain overwhelmingly English-centric, ensuring safety across diverse linguistic and cultural contexts requires localized resources. In this paper, we introduce the first Polish preference dataset PLLuM-Align, created entirely through human annotation to reflect Polish language and cultural nuances. The dataset includes response rating, ranking, and multi-turn dialog data. Designed to reflect the linguistic subtleties and cultural norms of Polish, this resource lays the groundwork for more aligned Polish LLMs and contributes to the broader goal of multilingual alignment in underrepresented languages.

The development of multilingual large language models (LLMs) presents challenges in evaluating their safety across all supported languages. Enhancing safety in one language (e.g., English) may inadvertently introduce vulnerabilities in others. To address this issue, we implement a methodology for the automatic creation of red-teaming datasets for safety evaluation in Polish language. Our approach generates both harmful and non-harmful prompts by sampling different risk categories and attack styles. We test several open-source models, including those trained on Polish data, and evaluate them using metrics such as Attack Success Rate (ASR) and False Reject Rate (FRR). The results reveal clear gaps in safety performance between models and show that better testing across languages is needed.