  Several years of research have shown that machine-learning systems are
vulnerable to adversarial examples, both in theory and in practice. Until now,
such attacks have primarily targeted visual models, exploiting the gap between
human and machine perception. Although text-based models have also been
attacked with adversarial examples, such attacks struggled to preserve semantic
meaning and indistinguishability. In this paper, we explore a large class of
adversarial examples that can be used to attack text-based models in a
black-box setting without making any human-perceptible visual modification to
inputs. We use encoding-specific perturbations that are imperceptible to the
human eye to manipulate the outputs of a wide range of Natural Language
Processing (NLP) systems from neural machine-translation pipelines to web
search engines. We find that with a single imperceptible encoding injection --
representing one invisible character, homoglyph, reordering, or deletion -- an
attacker can significantly reduce the performance of vulnerable models, and
with three injections most models can be functionally broken. Our attacks work
against currently-deployed commercial systems, including those produced by
Microsoft and Google, in addition to open source models published by Facebook,
IBM, and HuggingFace. This novel series of attacks presents a significant
threat to many language processing systems: an attacker can affect systems in a
targeted manner without any assumptions about the underlying model. We conclude
that text-based NLP systems require careful input sanitization, just like
conventional applications, and that given such systems are now being deployed
rapidly at scale, the urgent attention of architects and operators is required.
