Examining the Robustness of Machine Learning-based Phishing Website Detection: Action-Masked Reinforcement Learning for Automated Red Teaming

Yang Gao; Benjamin M. Ampel; Sagar Samtani

doi:10.1109/SPW67851.2025.00041

Examining the Robustness of Machine Learning-based Phishing Website Detection: Action-Masked Reinforcement Learning for Automated Red Teaming

Yang Gao, Benjamin M. Ampel, Sagar Samtani

Last updated on Jun 9, 2026

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In Plain Terms

Attackers can dodge AI tools that detect phishing websites by subtly altering the site's code. This paper builds an automated "red team" that uses reinforcement learning to realistically mimic those evasion tricks, with safeguards so the altered sites still look normal, providing a scalable way to test how robust phishing detectors really are before they're deployed.

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Yang Gao, Benjamin M. Ampel, & Sagar Samtani (2025). Examining the Robustness of Machine Learning-based Phishing Website Detection: Action-Masked Reinforcement Learning for Automated Red Teaming. IEEE SPW https://doi.org/10.1109/SPW67851.2025.00041

Phishing Machine Learning Reinforcement Learning Red Teaming Adversarial Machine Learning

Examining the Robustness of Machine Learning-based Phishing Website Detection: Action-Masked Reinforcement Learning for Automated Red Teaming

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Benjamin M. Ampel

Assistant Professor in Computer Information Systems and Director, Center for CyberAI Research (CCAIR)