Efficient Action Poisoning Attacks on Linear Contextual Bandits

• URL: http://arxiv.org/abs/2112.05367v1
• Date: Fri, 10 Dec 2021 07:39:07 GMT
• Title: Efficient Action Poisoning Attacks on Linear Contextual Bandits
• Authors: Guanlin Liu and Lifeng Lai
• Abstract summary: We propose a new class of attacks: action poisoning attacks. An adversary can change the action signal selected by the agent. We show that, in both white-box and black-box settings, the proposed attack schemes can force the LinUCB agent to pull a target arm very frequently.
• Score: 41.1063033715314
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contextual bandit algorithms have many applicants in a variety of scenarios. In order to develop trustworthy contextual bandit systems, understanding the impacts of various adversarial attacks on contextual bandit algorithms is essential. In this paper, we propose a new class of attacks: action poisoning attacks, where an adversary can change the action signal selected by the agent. We design action poisoning attack schemes against linear contextual bandit algorithms in both white-box and black-box settings. We further analyze the cost of the proposed attack strategies for a very popular and widely used bandit algorithm: LinUCB. We show that, in both white-box and black-box settings, the proposed attack schemes can force the LinUCB agent to pull a target arm very frequently by spending only logarithm cost.

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