Deemon: Detecting CSRF with Dynamic Analysis and Property Graphs
This addresses a critical security gap for web developers and testers by automating CSRF detection, which has been largely overlooked compared to other vulnerabilities like XSS and SQLi.
The paper tackles the problem of detecting Cross-Site Request Forgery (CSRF) vulnerabilities in web applications, which are often manually identified, by introducing Deemon, an automated framework that discovered 14 previously unknown vulnerabilities in 10 open-source applications.
Cross-Site Request Forgery (CSRF) vulnerabilities are a severe class of web vulnerabilities that have received only marginal attention from the research and security testing communities. While much effort has been spent on countermeasures and detection of XSS and SQLi, to date, the detection of CSRF vulnerabilities is still performed predominantly manually. In this paper, we present Deemon, to the best of our knowledge the first automated security testing framework to discover CSRF vulnerabilities. Our approach is based on a new modeling paradigm which captures multiple aspects of web applications, including execution traces, data flows, and architecture tiers in a unified, comprehensive property graph. We present the paradigm and show how a concrete model can be built automatically using dynamic traces. Then, using graph traversals, we mine for potentially vulnerable operations. Using the information captured in the model, our approach then automatically creates and conducts security tests, to practically validate the found CSRF issues. We evaluate the effectiveness of Deemon with 10 popular open source web applications. Our experiments uncovered 14 previously unknown CSRF vulnerabilities that can be exploited, for instance, to take over user accounts or entire websites.