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.

We survey the most common attacks against web sessions, i.e., attacks which target honest web browser users establishing an authenticated session with a trusted web application. We then review existing security solutions which prevent or mitigate the different attacks, by evaluating them along four different axes: protection, usability, compatibility and ease of deployment. Based on this survey, we identify five guidelines that, to different extents, have been taken into account by the designers of the different proposals we reviewed. We believe that these guidelines can be helpful for the development of innovative solutions approaching web security in a more systematic and comprehensive way.