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Machine-learning phishing webpage detectors (ML-PWD) have been shown tosuffer from adversarial manipulations of the HTML code of the input webpage.Nevertheless, the attacks recently proposed have demonstrated limitedeffectiveness due to their lack of optimizing the usage of the adoptedmanipulations, and they focus solely on specific elements of the HTML code. Inthis work, we overcome these limitations by first designing a novel set offine-grained manipulations which allow to modify the HTML code of the inputphishing webpage without compromising its maliciousness and visual appearance,i.e., the manipulations are functionality- and rendering-preserving by design.We then select which manipulations should be applied to bypass the targetdetector by a query-efficient black-box optimization algorithm. Our experimentsshow that our attacks are able to raze to the ground the performance of currentstate-of-the-art ML-PWD using just 30 queries, thus overcoming the weakerattacks developed in previous work, and enabling a much fairer robustnessevaluation of ML-PWD.

Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors