The modern web ecosystem relies heavily on the inclusion of third-party scripts as they offer useful, and often necessary, functionality. This inclusion leads to the "blending" of code from different origins, which has significant ramifications. Specifically, the inability to effectively and robustly disambiguate between first-party and embedded third-party scripts can severely undermine the security and privacy guarantees of existing defenses (e.g., blocking trackers or preventing vulnerabilities such as DOM XSS), as well as the validity of web measurement studies. To address that gap we propose StyxJS, a system that is able to provide real-time attribution of third-party scripts while preventing evasive tactics that can be employed by malicious scripts. This is achieved through an automated pipeline consisting of stack walking, script rewriting, browser API overriding, and tamper-proofing mechanisms. Crucially, our system does not require any developer input or prior knowledge about the website and can, thus, be readily incorporated into any countermeasure or web measurement apparatus that requires robust script attribution. We conduct an extensive experimental evaluation of our system and demonstrate that it accurately captures more script inclusion techniques compared to prior work, while incurring a negligible performance overhead, and effectively maintains page-deployed security mechanisms (e.g., CSP). We also detail the straightforward process and benefits of retrofitting a varied set of existing defenses on top of StyxJS, as well as leveraging it to analyze the web ecosystem. We will release our system as an open source project, to allow security researchers and practitioners to benefit from StyxJS’ capabilities.

In this paper, we focus on authentication and authorization flaws in web apps that enable partial or full access to user accounts. Specifically, we develop a novel fully automated black-box auditing framework that analyzes web apps by exploring their susceptibility to various cookie-hijacking attacks while also assessing their deployment of pertinent security mechanisms (e.g., HSTS). Our modular framework is driven by a custom browser automation tool developed to transparently offer fault-tolerance during extended interactions with web apps. We use our framework to conduct the first automated large-scale study of cookie-based account hijacking in the wild. As our framework handles every step of the auditing process in a completely automated manner, including the challenging process of account creation, we are able to fully audit 25K domains. Our framework detects more than 10K domains that expose authentication cookies over unencrypted connections, and over 5K domains that do not protect authentication cookies from JavaScript access while also embedding third party scripts that execute in the first party's origin. Our system also automatically identifies the privacy loss caused by exposed cookies and detects 9,324 domains where sensitive user data can be accessed by attackers (e.g., address, phone number, password). Overall, our study demonstrates that cookie-hijacking is a severe and prevalent threat, as deployment of even basic countermeasures (e.g., cookie security flags) is absent or incomplete, while developers struggle to correctly deploy more demanding mechanisms.