Benjamin Stock’s scientific contributions

The Web's functionality has shifted from purely server-side code to rich client-side applications, which allow the user to interact with a site without the need for a full page load. While server-side attacks, such as SQL or command injection, still pose a threat, this change in the Web's model also increases the impact of vulnerabilities aiming at exploiting the client. The most prominent representative of such client-side attacks is Cross-Site Scripting, where the attacker's goal is to inject code of his choosing into the application, which is subsequently executed by the victimized browsers in the context of the vulnerable site. This thesis provides insights into different aspects of Cross-Site Scripting. First, we show that the concept of password managers, which aim to allow users to choose more secure passwords and, thus, increase the overall security of user accounts, is susceptible to attacks which abuse Cross-Site Scripting flaws. In our analysis, we found that almost all built-in password managers can be leveraged by a Cross-Site Scripting attacker to steal stored credentials. Based on our observations, we present a secure concept for password managers, which does not insert password data into the document such that it is accessible from the injected JavaScript code. We evaluate our approach from a functional and security standpoint and find that our proposal provides additional security benefits while not causing any incompatibilities. Our work then focuses on a sub-class of Cross-Site Scripting, namely Client-Side Cross-Site Scripting. We design, implement and execute a study into the prevalence of this class of flaws at scale. To do so, we implement a taint-aware browsing engine and an exploit generator capable of precisely producing exploits based on our gathered data on suspicious, tainted flows. Our subsequent study of the Alexa top 5,000 domains shows that almost one out of ten of these domains carry at least one Client-Side Cross-Site Scripting vulnerability. We follow up on these flaws by analyzing the gathered flow data in depth in search of the root causes of this class of vulnerability. To do so, we first discuss the complexities inherent to JavaScript and define a set of metrics to measure said complexity. We then classify the vulnerable snippets of code we discovered according to these metrics and present the key insights gained from our analysis. In doing so, we find that the reasons for such flaws are manifold, ranging from simple unawareness of developers to incompatibilities between, otherwise safe, first- and third-party code. In addition, we investigate the capability of the state of the art of Cross-Site Scripting filters in the client, the XSS Auditor, finding that several conceptual issues exist which an attacker to subvert of its protection capabilities. We show that the Auditor can be bypassed on over 80% of the vulnerable domains in our data set, highlighting that it is ill-equipped to stop Client-Side Cross-Site Scripting. Motivated by our findings, we present a concept for a filter targeting Client-Side Cross-Site Scripting, combining taint tracking in the browser in conjunction with taint-aware HTML and JavaScript parsers, allowing us to robustly protect users from such attacks.