  Recurrent models are frequently being used in online tasks such as autonomous
driving, and a comprehensive study of their vulnerability is called for.
Existing research is limited in generality only addressing application-specific
vulnerability or making implausible assumptions such as the knowledge of future
input. In this paper, we present a general attack framework for online tasks
incorporating the unique constraints of the online setting different from
offline tasks. Our framework is versatile in that it covers time-varying
adversarial objectives and various optimization constraints, allowing for a
comprehensive study of robustness. Using the framework, we also present a novel
white-box attack called Predictive Attack that `hallucinates' the future. The
attack achieves 98 percent of the performance of the ideal but infeasible
clairvoyant attack on average. We validate the effectiveness of the proposed
framework and attacks through various experiments.
