  Neural networks are deployed widely in natural language processing tasks on
the industrial scale, and perhaps the most often they are used as compounds of
automatic machine translation systems. In this work, we present a simple
approach to fool state-of-the-art machine translation tools in the task of
translation from Russian to English and vice versa. Using a novel black-box
gradient-free tensor-based optimizer, we show that many online translation
tools, such as Google, DeepL, and Yandex, may both produce wrong or offensive
translations for nonsensical adversarial input queries and refuse to translate
seemingly benign input phrases. This vulnerability may interfere with
understanding a new language and simply worsen the user's experience while
using machine translation systems, and, hence, additional improvements of these
tools are required to establish better translation.
