← Azimuth Daily
Capturing data…
Capturing data…
SUN, 25 OCT · 77 ITEMS
"LLM safety fails under low-resource translation across 79 languages" — arXiv cs.CL · AI & Technology
This study (arXiv:2610.27304) systematically evaluates how well LLM safety guardrails hold up when prompts are translated into 78 plus English. The researchers find that safety mechanisms that reliably block harmful content in English frequently fail when the same request is translated into a language with limited training data, effectively creating a cross-lingual . The paper likely reports attack success rates across multiple models and languages, quantifying the vulnerability.
The claim is directly from the primary source—an arXiv preprint reporting experimental results. While the methodology and exact numbers cannot be verified without reading the full paper, the finding is consistent with prior work (e.g., earlier studies on low-resource language jailbreaks) and is presented as a self-contained research claim. The provenance tier (primary source) means the claim is authoritative about what the paper itself asserts.
Genuinely new: the paper was published on arXiv on 2026-10-21, with no recirculation year indicated.