Cross‐lingual safety asymmetry in open‐weight LLMs

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Safety alignment in large language models is overwhelmingly trained on English data, yet models are deployed across typologically diverse languages. We evaluate six open‐weight large language models (LLMs) across 17 bilingual Korean/Chinese prompt conditions and three translation engines (NLLB‐200, Gemini 3.0 Pro, and GPT‐5.2), generating 53 040 AdvBench responses. We also conduct French/Spanish and HarmBench robustness checks. The regex classifier is cross‐validated against an open‐weight LLM judge (Qwen‐2.5‐72B, 7500 judgments), and we report Wilson 95% confidence intervals. Three findings hold for both classifiers: Safety erosion is language‐specific, not generically non‐English; Chinese‐origin models (Yi, InternLM) exhibit asymmetric vulnerability; and a two‐tier pattern persists, where models with target‐language safety data resist high‐quality translations, and models without such safety data remain vulnerable, regardless of translation quality. These findings reframe cross‐lingual safety as a per‐language training coverage problem.