Despite active privacy research on sophisticated web tracking techniques (e.g., fingerprinting, cache collusion, bounce tracking, CNAME cloaking), most tracking on the web is basic “stateful” tracking enabled by classical browser storage policies sharing per-site storage across all HTTP contexts. Alternative, privacy-preserving storage policies, especially for third-party contexts, have been proposed and even deployed, but these can break websites that presume traditional, non-partitioned storage. Such breakage discourages privacy-preserving experimentation, cementing the dismal status quo. Our work measures the privacy vs. compatibility trade-offs of representative third-party storage policies to enable design of browsers that are both compatible and privacy respecting. Our contributions include web-scale measurements of page behaviors under multiple third-party storage policies inspired by production browsers. We define metrics for measuring aggregate effects on web privacy and compatibility, including a novel system for quantitatively estimating aggregate website breakage under different policies. We find that making third-party storage partitioned by first-party, and lifetimes by site-session achieves the best privacy and compatibility trade-off. We provide complete measurement datasets and storage policy implementations.

Phishing is a critical threat to Internet users. Although an extensive ecosystem serves to protect users, phishing websites are growing in sophistication, and they can slip past the ecosystem’s detection systems—and subsequently cause real-world damage—with the help of evasion techniques. Sophisticated client-side evasion techniques, known as cloaking, leverage JavaScript to enable complex interactions between potential victims and the phishing website, and can thus be particularly effective in slowing or entirely preventing automated mitigations. Yet, neither the prevalence nor the impact of client-side cloaking has been studied.In this paper, we present CrawlPhish, a framework for automatically detecting and categorizing client-side cloaking used by known phishing websites. We deploy CrawlPhish over 14 months between 2018 and 2019 to collect and thoroughly analyze a dataset of 112,005 phishing websites in the wild. By adapting state-of-the-art static and dynamic code analysis, we find that 35,067 of these websites have 1,128 distinct implementations of client-side cloaking techniques. Moreover, we find that attackers’ use of cloaking grew from 23.32% initially to 33.70% by the end of our data collection period. Detection of cloaking by our framework exhibited low false-positive and false-negative rates of 1.45% and 1.75%, respectively. We analyze the semantics of the techniques we detected and propose a taxonomy of eight types of evasion across three high-level categories: User Interaction, Fingerprinting, and Bot Behavior.Using 150 artificial phishing websites, we empirically show that each category of evasion technique is effective in avoiding browser-based phishing detection (a key ecosystem defense). Additionally, through a user study, we verify that the techniques generally do not discourage victim visits. Therefore, we propose ways in which our methodology can be used to not only improve the ecosystem’s ability to mitigate phishing websites with client-side cloaking, but also continuously identify emerging cloaking techniques as they are launched by attackers.

Accurate web measurement is critical for understanding and improving security and privacy online. Such measurements implicitly assume that automated crawls generalize to typical web user experience. But anecdotal evidence suggests the web behaves differently when seen via well-known measurement endpoints or measurement automation frameworks, for various reasons. Our work improves the state of web privacy and security by investigating how key measurements differ when using naive crawling tool defaults vs. careful attempts to match “real” users across the Tranco top 25k web domains. We find web privacy and security measurements significantly affected by vantage point and browser configuration. We conclude that unless researchers ensure their web measurement tools match real world user experience, the research community is likely missing important signals systematically. For example, we find browser configuration alone causing shifts in 19% of known ad and tracking domains encountered and altering the loading frequency of up to 10% of distinct JavaScript code units executed. We find network vantage point having similar, though less dramatic, effects on the same web metrics. To ensure reproducibility, we carefully document our methodology and publish both our code and collected data.

In this paper, we perform a large-scale measurement study of JavaScript obfuscation of browser APIs in the wild. We rely on a simple, but powerful observation: if dynamic analysis of a script's behavior (specifically, how it interacts with browser APIs) reveals browser API feature usage that cannot be reconciled with static analysis of the script's source code, then that behavior is obfuscated. To quantify and test this observation, we create a hybrid analysis platform using instrumented Chromium to log all browser API accesses by the scripts executed when a user visits a page. We filter the API access traces from our dynamic analysis through a static analysis tool that we developed in order to quantify how much and what kind of functionality is hidden on the web. When applying this methodology across the Alexa top 100k domains, we discover that 95.90% of the domains we successfully visited contain at least one script which invokes APIs that cannot be resolved from static analysis. We observe that eval is no longer the prominent obfuscation method on the web and we uncover families of novel obfuscation techniques that no longer rely on the use of eval.