Biblio

Cross-Site Request Forgery (CSRF) is one of the oldest and simplest attacks on the Web, yet it is still effective on many websites and it can lead to severe consequences, such as economic losses and account takeovers. Unfortunately, tools and techniques proposed so far to identify CSRF vulnerabilities either need manual reviewing by human experts or assume the availability of the source code of the web application. In this paper we present Mitch, the first machine learning solution for the black-box detection of CSRF vulnerabilities. At the core of Mitch there is an automated detector of sensitive HTTP requests, i.e., requests which require protection against CSRF for security reasons. We trained the detector using supervised learning techniques on a dataset of 5,828 HTTP requests collected on popular websites, which we make available to other security researchers. Our solution outperforms existing detection heuristics proposed in the literature, allowing us to identify 35 new CSRF vulnerabilities on 20 major websites and 3 previously undetected CSRF vulnerabilities on production software already analyzed using a state-of-the-art tool.

Software development and web applications have become fundamental in our lives. Millions of users access these applications to communicate, obtain information and perform transactions. However, these users are exposed to many risks; commonly due to the developer's lack of experience in security protocols. Although there are many researches about web security and hacking protection, there are plenty of vulnerable websites. This article focuses in analyzing 3 main hacking techniques: XSS, CSRF, and SQL Injection over a representative group of Colombian websites. Our goal is to obtain information about how Colombian companies and organizations give (or not) relevance to security; and how the final user could be affected.