  Data-driven predictive control (DPC) is a feedback control method for systems
with unknown dynamics. It repeatedly optimizes a system's future trajectories
based on past input-output data. We develop a numerical method that computes
poisoning attacks that inject additive perturbations to the online output data
to change the trajectories optimized by DPC. This method is based on implicitly
differentiating the solution map of the trajectory optimization in DPC. We
demonstrate that the resulting attacks can cause an output tracking error one
order of magnitude higher than random perturbations in numerical experiments.
