We propose the first general-purpose gradient-based attack against
transformer models. Instead of searching for a single adversarial example, we
search for a distribution of adversarial examples parameterized by a
continuous-valued matrix, hence enabling gradient-based optimization. We
empirically demonstrate that our white-box attack attains state-of-the-art
attack performance on a variety of natural language tasks. Furthermore, we show
that a powerful black-box transfer attack, enabled by sampling from the
adversarial distribution, matches or exceeds existing methods, while only
requiring hard-label outputs.

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