Offensive AI
Enhancing Directory Brute-forcing Attack with the Use of Language Models
Alberto Castagnaro (Student TU Delft)
Mauro Conti (Università degli Studi di Padova, TU Delft - Cyber Security)
Luca Pajola (Università degli Studi di Padova, Spritz Matter Srl)
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Abstract
Web Vulnerability Assessment and Penetration Testing (Web VAPT) is a comprehensive cybersecurity process that uncovers a range of vulnerabilities which, if exploited, could compromise the integrity of web applications. In a VAPT, it is common to perform a Directory brute-forcing Attack, aiming at the identification of accessible directories of a target website. Current commercial solutions are inefficient as they are based on brute-forcing strategies that use wordlists, resulting in enormous quantities of trials for a small amount of success.
Offensive AI is a recent paradigm that integrates AI-based technologies in cyber attacks. In this work, we explore whether AI can enhance the directory enumeration process and propose a novel Language Model-based framework. Our experiments -- conducted in a testbed consisting of 1 million URLs from different web application domains (universities, hospitals, government, companies) -- demonstrate the superiority of the LM-based attack, with an average performance increase of 969%.
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