Adversarial Attacks on Stochastic Bandits
This exposes a significant security threat for practical applications like medical treatments, as bandits are widely used.
The paper tackles the problem of adversarial attacks manipulating reward signals to control stochastic multi-armed bandit algorithms, showing that an attacker can hijack behavior to promote or obstruct actions with only logarithmic effort, without knowledge of mean rewards.
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: $ε$-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.