Global Security Blind Spots: Translation as a Safety Bypass in Low-Resource Languages

Key points

• We evaluate ten widely deployed LLMs across 79 languages (English plus 78 low-resource) under a translation-bypass threat model, using a metric conditioned on English safety, which separates translation bypass from unsafety already present in English.1
• English safety does not transfer reliably: conditioned on safe English completions, mean bypass rates range from 21.9% to 44.4%.1
• Bypass risk concentrates in a subset of language families, a pattern broadly shared across models.1
• We also show that per-model bypass rates do not identify a stable ranking: nine of ten models change rank across three conditioning schemes that estimate the same quantity, with no change to any underlying response.1

Sources (1)

• [1]Global Security Blind Spots: Translation as a Safety Bypass in Low-Resource Languages
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 09:20 PM

  Large language models are deployed globally, yet safety alignment and evaluation remain concentrated in English: harmful requests refused in English can elicit unsafe completions when translated into low-resource languages.

  We evaluate ten widely deployed LLMs across 79 languages (English plus 78 low-resource) under a translation-bypass threat model, using a metric conditioned on English safety, which separates translation bypass from unsafety already present in English.

Extractive summary: sentences quoted from the sources.