  Recent results in the ML community have revealed that learning algorithms
used to compute the optimal strategy for the leader to commit to in a
Stackelberg game, are susceptible to manipulation by the follower. Such a
learning algorithm operates by querying the best responses or the payoffs of
the follower, who consequently can deceive the algorithm by responding as if
his payoffs were much different than what they actually are. For this strategic
behavior to be successful, the main challenge faced by the follower is to
pinpoint the payoffs that would make the learning algorithm compute a
commitment so that best responding to it maximizes the follower's utility,
according to his true payoffs. While this problem has been considered before,
the related literature only focused on the simplified scenario in which the
payoff space is finite, thus leaving the general version of the problem
unanswered. In this paper, we fill in this gap, by showing that it is always
possible for the follower to compute (near-)optimal payoffs for various
scenarios about the learning interaction between leader and follower.
