  Language Models today provide a high accuracy across a large number of
downstream tasks. However, they remain susceptible to adversarial attacks,
particularly against those where the adversarial examples maintain considerable
similarity to the original text. Given the multilingual nature of text, the
effectiveness of adversarial examples across translations and how machine
translations can improve the robustness of adversarial examples remain largely
unexplored. In this paper, we present a comprehensive study on the robustness
of current text adversarial attacks to round-trip translation. We demonstrate
that 6 state-of-the-art text-based adversarial attacks do not maintain their
efficacy after round-trip translation. Furthermore, we introduce an
intervention-based solution to this problem, by integrating Machine Translation
into the process of adversarial example generation and demonstrating increased
robustness to round-trip translation. Our results indicate that finding
adversarial examples robust to translation can help identify the insufficiency
of language models that is common across languages, and motivate further
research into multilingual adversarial attacks.
