Directory context | Adversarial Machine Learning
Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors
Source code of the paper "Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors" accepted at AISec '23
Direct answer
What is Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors?
Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors is included in the Awesome MLSecOps Adversarial Machine Learning directory. The community-maintained README describes it as: “Source code of the paper "Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors" accepted at AISec '23.” Its MLSecOps relevance is the controlled evaluation of evasion, poisoning, extraction, inversion, privacy attacks, or robustness under an explicit attacker model. The linked first-party source is the advmlphish/raze_to_the_ground_aisec23 repository on GitHub. A technical review should test the project's documented evidence across four criteria: Threat-model fit, Modality and framework support, Adaptive attack evaluation, and Experiment reproducibility. Compare that evidence with the intended architecture and threat model. Catalog inclusion establishes relevance to this security category; it is not a certification, comparative ranking, or endorsement. Confirm current capabilities, maintenance, licensing, limitations, and deployment assumptions in the first-party documentation before adoption.
Source code of the paper "Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors" accepted at AISec '23
Neutral catalog description synchronized from the Awesome MLSecOps README
Before adoption
What should teams verify about Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors?
Answer these questions from current first-party documentation and testing evidence rather than relying on the directory listing alone.
- 01
Which attacker capabilities, access levels, and success conditions are modeled?
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Which data modalities, frameworks, and model interfaces are supported?
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Do defense evaluations include adaptive attackers and meaningful baselines?
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Can experiments be reproduced from versioned configurations, datasets, and metrics?