The Illusion of Cross-Lingual Safety in Low-Resource Languages

2026-08-11

AI Summary

Current safety alignment of large language models is predominantly based on English, and their cross-lingual generalization to low-resource languages remains poorly understood, posing potential risks. This work introduces the LoDNA dataset, comprising both literal translations and culturally localized prompts, to systematically evaluate the transferability of safety mechanisms across four African languages. We propose a probing method grounded in the geometric structure of the model’s latent space to analyze internal representations underlying refusal behaviors. Our study reveals, for the first time, significant limitations in cross-lingual safety alignment: in most language–model combinations, harmful prompts retain less than 10% of the refusal signal observed in English, indicating that semantic alignment does not ensure consistent safety routing. These findings challenge the assumption of a language-invariant harm manifold.

Abstract

Safety alignment in large language models (LLMs) is largely developed in English, assuming these safeguards generalize across multilingual settings. However, this assumption remains underexplored and exposes a vulnerability in low-resource languages. 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. To move beyond generation-based evaluation, we propose a latent geometric framework that probes hidden-state refusal representations in LLMs. 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. Literal and localized prompts are semantically aligned (cosine 0.95-0.996) but drift across layers, suggesting models encode the concepts without routing them to safety mechanisms. These findings demonstrate that current multilingual safety alignment is superficial, providing strong evidence against the assumption of a universal, language-agnostic harm manifold within the specific low-resource languages studied. Warning: This paper contains example data that may be offensive or harmful.

Problem

• cross-lingual safety
• low-resource languages
• safety alignment
• multilingual LLMs
• harm detection

Innovation

• cross-lingual safety transfer
• latent geometric framework
• safety alignment
• low-resource languages
• refusal representations