We present a new method to bound the performance of controllers for uncertain linear systems with mixed state and input constraints and bounded disturbances. We take as a performance metric either an expected-value or minimax discounted cost over an infinite horizon, and provide a method for computing a lower bound on the achievable performance of any causal control policy in either case. Our lower bound is compared to an upper performance bound provided by restricting the choice of controller to one that is affine in the observed disturbances, and we show that the two bounds are closely related. In particular, the lower bounds have a natural interpretation in terms of affine control policies that are optimal for a problem with a restricted disturbance set. We show that our performance bounds can be computed via solution of a finite-dimensional convex optimization problem, and provide numerical examples to illustrate the efficacy of our method.