We study two modes of low-resource language safety: in-language alignment, where models refuse harmful instructions in the target language, and cross-lingual safety borrowing, where they rely on a better-aligned proximate high-resource language, including via response-language fallback. We evaluate 11 commercial APIs on English, Mandarin, Cantonese, Tibetan, and Russian, plus 3 Tibetan-specialized open-source models, using native-speaker-verified datasets. Results reveal two patterns: Tibetan, for which response-language fallback provides limited aggregate protection in our setting, shows a safety cliff with ASR of 12.69–60.96%, far above English; Cantonese, which shares script and vocabulary with Mandarin, keeps aggregate ASR in single digits, but Cantonese-reply ASR exceeds aggregate ASR for all 11 models (sign test, p < 0.001), exposing in-language safety gaps concealed by aggregate metrics. We release Cantonese-AdvBench and Tibetan-AdvBench.