As safety remains a crucial concern throughout the development lifecycle of Large Language Models (LLMs), researchers and industrial practitioners have increasingly focused on safeguarding and aligning LLM behaviors with human preferences and ethical standards. LLMs, trained on extensive multilingual corpora, exhibit powerful generalization abilities across diverse languages and domains. However, current safety alignment practices predominantly focus on single-language scenarios, which leaves their effectiveness in complex multilingual contexts, especially for those complex mixed-language formats, largely unexplored. In this study, we introduce Multilingual Blending, a mixed-language query-response scheme designed to evaluate the safety alignment of various state-of-the-art LLMs (e.g., GPT-4o, GPT-3.5, Llama3) under sophisticated, multilingual conditions. We further investigate language patterns such as language availability, morphology, and language family that could impact the effectiveness of Multilingual Blending in compromising the safeguards of LLMs. Our experimental results show that, without meticulously crafted prompt templates, Multilingual Blending significantly amplifies the detriment of malicious queries, leading to dramatically increased bypass rates in LLM safety alignment (67.23% on GPT-3.5 and 40.34% on GPT-4o), far exceeding those of single-language baselines. Moreover, the performance of Multilingual Blending varies notably based on intrinsic linguistic properties, with languages of different morphology and from diverse families being more prone to evading safety alignments. These findings underscore the necessity of evaluating LLMs and developing corresponding safety alignment strategies in a complex, multilingual context to align with their superior cross-language generalization capabilities.

本研究介绍了一种混合语言查询-回复方案，名为多语言融合，用于评估各种最先进的LLMs（例如GPT-4o、GPT-3.5、Llama3）在复杂的多语言环境下的安全对齐性。研究结果表明，没有经过精心制作的提示模板，多语言融合显著放大了恶意查询的危害，导致LLMs在安全对齐方面的绕过率显著增加。此外，多语言融合的性能在内在语言属性上有显著差异，不同形态和不同语系的语言更容易规避安全对齐。这些发现强调了在复杂的多语言环境下评估LLMs并开发相应的安全对齐策略以适应其优越的跨语言泛化能力的必要性。

多语言融合：LLM安全对齐评估与语言混合