  We study adversarial attacks that manipulate the reward signals to control
the actions chosen by a stochastic multi-armed bandit algorithm. We propose the
first attack against two popular bandit algorithms: $\epsilon$-greedy and UCB,
\emph{without} knowledge of the mean rewards. The attacker is able to spend
only logarithmic effort, multiplied by a problem-specific parameter that
becomes smaller as the bandit problem gets easier to attack. The result means
the attacker can easily hijack the behavior of the bandit algorithm to promote
or obstruct certain actions, say, a particular medical treatment. As bandits
are seeing increasingly wide use in practice, our study exposes a significant
security threat.
