Papers › Adversarial Attacks on Gaussian Process Bandits

Adversarial Attacks on Gaussian Process Bandits

16 Oct 2021arXiv:2110.08449archive 2025-07-28

Eric Han, Jonathan Scarlett

Gaussian processes (GP) are a widely-adopted tool used to sequentially optimize black-box functions, where evaluations are costly and potentially noisy. Recent works on GP bandits have proposed to move beyond random noise and devise algorithms robust to adversarial attacks. This paper studies this problem from the attacker's perspective, proposing various adversarial attack methods with differing assumptions on the attacker's strength and prior information. Our goal is to understand adversarial attacks on GP bandits from theoretical and practical perspectives. We focus primarily on targeted attacks on the popular GP-UCB algorithm and a related elimination-based algorithm, based on adversarially perturbing the function f to produce another function f̃ whose optima are in some target region ℛ_(target). Based on our theoretical analysis, we devise both white-box attacks (known f) and black-box attacks (unknown f), with the former including a Subtraction attack and Clipping attack, and the latter including an Aggressive subtraction attack. We demonstrate that adversarial attacks on GP bandits can succeed in forcing the algorithm towards ℛ_(target) even with a low attack budget, and we test our attacks' effectiveness on a diverse range of objective functions.

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