Adversarial Attacks on Linear Contextual Bandits

Understand

Contextual bandit algorithms are applied in a wide range of domains, from advertising to recommender systems, from clinical trials to education.

• In many of these domains, malicious agents may have incentives to attack the bandit algorithm to induce it to perform a desired behavior.
• For instance, an unscrupulous ad publisher may try to increase their own revenue at the expense of the advertisers; a seller may want to increase the exposure of their products, or thwart a competitor's advertising campaign.
• In this paper, we study several attack scenarios and show that a malicious agent can force a linear contextual bandit algorithm to pull any desired arm $T - o(T)$ times over a horizon of $T$ steps, while applying adversarial modifications to either rewards or contexts that only grow logarithmically as $O(\log T)$.