arXiv subjects

Shaoor Munir

Publications and source records attributed to Shaoor Munir.

7 recordsLinked to original sources

arXiv2025

FP-Inconsistent: Measurement and Analysis of Fingerprint Inconsistencies in Evasive Bot Traffic

As browser fingerprinting is increasingly being used for bot detection, bots have started altering their fingerprints for evasion. We conduct the first large-scale evaluation of evasive bots to investigate whether and how altering fingerprints helps bots evade detection. To systematically investigate evasive bots, we deploy a honey site incorporating two anti-bot services (DataDome and BotD) and solicit bot traffic from 20 different bot services that purport to sell "realistic and undetectable traffic". Across half a million requests from 20 different bot services on our honey site, we find an average evasion rate of 52.93% against DataDome and 44.56% evasion rate against BotD. Our comparison of fingerprint attributes from bot services that evade each anti-bot service individually as well as bot services that evade both shows that bot services indeed alter different browser fingerprint attributes for evasion. Further, our analysis reveals the presence of inconsistent fingerprint attributes in evasive bots. Given evasive bots seem to have difficulty in ensuring consistency in their fingerprint attributes, we propose a data-driven approach to discover rules to detect such inconsistencies across space (two attributes in a given browser fingerprint) and time (a single attribute at two different points in time). These rules, which can be readily deployed by anti-bot services, reduce the evasion rate of evasive bots against DataDome and BotD by 48.11% and 44.95% respectively.

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arXiv2025

Every Keystroke You Make: A Tech-Law Measurement and Analysis of Event Listeners for Wiretapping

The privacy community has a long track record of investigating emerging types of web tracking techniques. Recent work has focused on compliance of web trackers with new privacy laws such as Europe's GDPR and California's CCPA. Despite the growing body of research documenting widespread lack of compliance with new privacy laws, there is a lack of robust enforcement. Different from prior work, we conduct a tech-law analysis to map decades-old U.S. laws about interception of electronic communications--so-called wiretapping--to web tracking. Bridging the tech-law gap for older wiretapping laws is important and timely because, in cases where legal harm to privacy is proven, they can provide statutory private right of action, are at the forefront of recent privacy enforcement, and could ultimately lead to a meaningful change in the web tracking landscape. In this paper, we focus on a particularly invasive tracking technique: the use of JavaScript event listeners by third-party trackers for real-time keystroke interception on websites. We use an instrumented web browser to crawl a sample of the top-million websites to investigate the use of event listeners that aligns with the criteria for wiretapping, according to U.S. wiretapping law at the federal level and in California. We find evidence that 38.52% websites installed third-party event listeners to intercept keystrokes, and that at least 3.18% websites transmitted intercepted information to a third-party server, which aligns with the criteria for wiretapping. We further find evidence that the intercepted information such as email addresses typed into form fields are used for unsolicited email marketing. Beyond our work that maps the intersection between technical measurement and U.S. wiretapping law, additional future legal research is required to determine when the wiretapping observed in our paper passes the threshold for illegality.

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arXiv2025

Google's Chrome Antitrust Paradox

This Article examines Google's dominance of the browser market, highlighting how Google's Chrome browser plays a critical role in reinforcing Google's dominance in other markets. While Google portrays Chrome as a neutral platform built on open-source technologies, this Article shows that Chrome is instrumental in Google's strategy to reinforce its dominance in the online advertising, publishing, and browser markets. The examination of Google's strategic acquisitions, anticompetitive practices, and implementation of so-called "privacy controls" underlines that Chrome is far from a neutral gateway to the web. Rather, it serves as a key tool for Google to maintain and extend its market power, often to the detriment of competition and innovation in the digital economy. This Article illustrates how Chrome not only bolsters Google's position in online advertising and publishing through practices such as coercion and self-preferencing, but also leverages its advertising clout to engage in a "pay-to-play" paradigm--the cornerstone of Google's larger strategy of market control. It also outlines potential regulatory interventions and remedies by drawing on historical antitrust precedents. Lastly, this Article proposes a triad of solutions motivated by an analysis of Google's abuse of Chrome, including behavioral remedies targeting specific anticompetitive practices, structural remedies involving an internal separation of Google's divisions, and divestiture of Chrome from Google into an independent organization. (Abstract abridged for arXiv. Full abstract available in published version.)

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arXiv2025

SoK: Advances and Open Problems in Web Tracking

Web tracking is a pervasive and opaque practice that enables personalized advertising, retargeting, and conversion tracking. Over time, it has evolved into a sophisticated and invasive ecosystem, employing increasingly complex techniques to monitor and profile users across the web. The research community has a long track record of analyzing new web tracking techniques, designing and evaluating the effectiveness of countermeasures, and assessing compliance with privacy regulations. Despite a substantial body of work on web tracking, the literature remains fragmented across distinctly scoped studies, making it difficult to identify overarching trends, connect new but related techniques, and identify research gaps in the field. Today, web tracking is undergoing a once-in-a-generation transformation, driven by fundamental shifts in the advertising industry, the adoption of anti-tracking countermeasures by browsers, and the growing enforcement of emerging privacy regulations. This Systematization of Knowledge (SoK) aims to consolidate and synthesize this wide-ranging research, offering a comprehensive overview of the technical mechanisms, countermeasures, and regulations that shape the modern and rapidly evolving web tracking landscape. This SoK also highlights open challenges and outlines directions for future research, aiming to serve as a unified reference and introductory material for researchers, practitioners, and policymakers alike.

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arXiv2024

Blocking Tracking JavaScript at the Function Granularity

Modern websites extensively rely on JavaScript to implement both functionality and tracking. Existing privacy enhancing content blocking tools struggle against mixed scripts, which simultaneously implement both functionality and tracking, because blocking the script would break functionality and not blocking it would allow tracking. We propose Not.js, a fine grained JavaScript blocking tool that operates at the function level granularity. Not.js's strengths lie in analyzing the dynamic execution context, including the call stack and calling context of each JavaScript function, and then encoding this context to build a rich graph representation. Not.js trains a supervised machine learning classifier on a webpage's graph representation to first detect tracking at the JavaScript function level and then automatically generate surrogate scripts that preserve functionality while removing tracking. Our evaluation of Not.js on the top 10K websites demonstrates that it achieves high precision (94%) and recall (98%) in detecting tracking JavaScript functions, outperforming the state of the art while being robust against off the shelf JavaScript obfuscation. Fine grained detection of tracking functions allows Not.js to automatically generate surrogate scripts that remove tracking JavaScript functions without causing major breakage. Our deployment of Not.js shows that mixed scripts are present on 62.3% of the top 10K websites, with 70.6% of the mixed scripts being third party that engage in tracking activities such as cookie ghostwriting. We share a sample of the tracking functions detected by Not.js within mixed scripts not currently on filter lists with filter list authors, who confirm that these scripts are not blocked due to potential functionality breakage, despite being known to implement tracking.

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arXiv2024

PURL: Safe and Effective Sanitization of Link Decoration

While privacy-focused browsers have taken steps to block third-party cookies and mitigate browser fingerprinting, novel tracking techniques that can bypass existing countermeasures continue to emerge. Since trackers need to share information from the client-side to the server-side through link decoration regardless of the tracking technique they employ, a promising orthogonal approach is to detect and sanitize tracking information in decorated links. To this end, we present PURL (pronounced purel-l), a machine-learning approach that leverages a cross-layer graph representation of webpage execution to safely and effectively sanitize link decoration. Our evaluation shows that PURL significantly outperforms existing countermeasures in terms of accuracy and reducing website breakage while being robust to common evasion techniques. PURL's deployment on a sample of top-million websites shows that link decoration is abused for tracking on nearly three-quarters of the websites, often to share cookies, email addresses, and fingerprinting information.

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arXiv2023

COOKIEGRAPH: Understanding and Detecting First-Party Tracking Cookies

As third-party cookie blocking is becoming the norm in browsers, advertisers and trackers have started to use first-party cookies for tracking. We conduct a differential measurement study on 10K websites with third-party cookies allowed and blocked. This study reveals that first-party cookies are used to store and exfiltrate identifiers to known trackers even when third-party cookies are blocked. As opposed to third-party cookie blocking, outright first-party cookie blocking is not practical because it would result in major functionality breakage. We propose CookieGraph, a machine learning-based approach that can accurately and robustly detect first-party tracking cookies. CookieGraph detects first-party tracking cookies with 90.20% accuracy, outperforming the state-of-the-art CookieBlock approach by 17.75%. We show that CookieGraph is fully robust against cookie name manipulation while CookieBlock's acuracy drops by 15.68%. While blocking all first-party cookies results in major breakage on 32% of the sites with SSO logins, and CookieBlock reduces it to 10%, we show that CookieGraph does not cause any major breakage on these sites. Our deployment of CookieGraph shows that first-party tracking cookies are used on 93.43% of the 10K websites. We also find that first-party tracking cookies are set by fingerprinting scripts. The most prevalent first-party tracking cookies are set by major advertising entities such as Google, Facebook, and TikTok.

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