  This paper proposes a new framework and several results to quantify the
performance of data-driven state-feedback controllers for linear systems
against targeted perturbations of the training data. We focus on the case where
subsets of the training data are randomly corrupted by an adversary, and derive
lower and upper bounds for the stability of the closed-loop system with
compromised controller as a function of the perturbation statistics, size of
the training data, sensitivity of the data-driven algorithm to perturbation of
the training data, and properties of the nominal closed-loop system. Our
stability and convergence bounds are probabilistic in nature, and rely on a
first-order approximation of the data-driven procedure that designs the
state-feedback controller, which can be computed directly using the training
data. We illustrate our findings via multiple numerical studies.
