  We investigate the complexity of bilevel combinatorial optimization with
uncertainty in the follower's objective, in a robust optimization approach. We
show that the robust counterpart of the bilevel problem under interval
uncertainty can be $\Sigma^{\text P}_2$-hard, even when the certain bilevel
problem is NP-equivalent and the follower's problem is tractable. On the
contrary, in the discrete uncertainty case, the robust bilevel problem is at
most one level harder than the follower's problem.
