  We study security threats to Markov games due to information asymmetry and
misinformation. We consider an attacker player who can spread misinformation
about its reward function to influence the robust victim player's behavior.
Given a fixed fake reward function, we derive the victim's policy under
worst-case rationality and present polynomial-time algorithms to compute the
attacker's optimal worst-case policy based on linear programming and backward
induction. Then, we provide an efficient inception ("planting an idea in
someone's mind") attack algorithm to find the optimal fake reward function
within a restricted set of reward functions with dominant strategies.
Importantly, our methods exploit the universal assumption of rationality to
compute attacks efficiently. Thus, our work exposes a security vulnerability
arising from standard game assumptions under misinformation.
