Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection

• URL: http://arxiv.org/abs/2505.21938v2
• Date: Sat, 31 May 2025 07:08:47 GMT
• Title: Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection
• Authors: Qirun Zeng, Eric He, Richard Hoffmann, Xuchuang Wang, Jinhang Zuo,
• Abstract summary: Adversarial attacks on bandits have traditionally relied on some unrealistic assumptions.<n>We propose a more practical threat model, which reflects realistic adversarial constraints.<n>We design efficient attack strategies under this model, explicitly addressing both magnitude constraints and temporal constraints.
• Score: 5.311665176634655
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adversarial attacks on stochastic bandits have traditionally relied on some unrealistic assumptions, such as per-round reward manipulation and unbounded perturbations, limiting their relevance to real-world systems. We propose a more practical threat model, Fake Data Injection, which reflects realistic adversarial constraints: the attacker can inject only a limited number of bounded fake feedback samples into the learner's history, simulating legitimate interactions. We design efficient attack strategies under this model, explicitly addressing both magnitude constraints (on reward values) and temporal constraints (on when and how often data can be injected). Our theoretical analysis shows that these attacks can mislead both Upper Confidence Bound (UCB) and Thompson Sampling algorithms into selecting a target arm in nearly all rounds while incurring only sublinear attack cost. Experiments on synthetic and real-world datasets validate the effectiveness of our strategies, revealing significant vulnerabilities in widely used stochastic bandit algorithms under practical adversarial scenarios.

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