  Cooperative multi-agent multi-armed bandits (CMA2B) consider the
collaborative efforts of multiple agents in a shared multi-armed bandit game.
We study latent vulnerabilities exposed by this collaboration and consider
adversarial attacks on a few agents with the goal of influencing the decisions
of the rest. More specifically, we study adversarial attacks on CMA2B in both
homogeneous settings, where agents operate with the same arm set, and
heterogeneous settings, where agents have distinct arm sets. In the homogeneous
setting, we propose attack strategies that, by targeting just one agent,
convince all agents to select a particular target arm $T-o(T)$ times while
incurring $o(T)$ attack costs in $T$ rounds. In the heterogeneous setting, we
prove that a target arm attack requires linear attack costs and propose attack
strategies that can force a maximum number of agents to suffer linear regrets
while incurring sublinear costs and only manipulating the observations of a few
target agents. Numerical experiments validate the effectiveness of our proposed
attack strategies.
