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.

研究马尔可夫博弈中信息不对称和错误信息导致的安全威胁，通过考虑一名攻击者玩家散布关于其奖励函数的错误信息来影响受害者玩家的行为，我们推导了在最坏情况理性下受害者的策略，并提出了基于线性规划和反向归纳的多项式时间算法来计算攻击者的最优最坏情况策略，在受限的奖励函数集合中找到最优的错误奖励函数，我们的方法利用了理性的普遍假设来高效计算攻击，因此，我们的工作揭示了在错误信息下由标准游戏假设引起的安全漏洞。