  The increased demand for machine learning applications made companies offer
Machine-Learning-as-a-Service (MLaaS). In MLaaS (a market estimated 8000M USD
by 2025), users pay for well-performing ML models without dealing with the
complicated training procedure. Among MLaaS, text-based applications are the
most popular ones (e.g., language translators). Given this popularity, MLaaS
must provide resiliency to adversarial manipulations. For example, a wrong
translation might lead to a misunderstanding between two parties. In the text
domain, state-of-the-art attacks mainly focus on strategies that leverage ML
models' weaknesses. Unfortunately, not much attention has been given to the
other pipeline' stages, such as the indexing stage (i.e., when a sentence is
converted from a textual to a numerical representation) that, if manipulated,
can significantly affect the final performance of the application.
  In this paper, we propose a novel text evasion technique called
"\textit{Zero-Width} attack" (ZeW) that leverages the injection of human
non-readable characters, affecting indexing stage mechanisms. We demonstrate
that our simple yet effective attack deceives MLaaS of "giants" such as Amazon,
Google, IBM, and Microsoft. Our case study, based on the manipulation of
hateful tweets, shows that out of 12 analyzed services, only one is resistant
to our injection strategy. We finally introduce and test a simple \textit{input
validation} defense that can prevent our proposed attack.
