arXiv subjects

Aurore Fass

Publications and source records attributed to Aurore Fass.

6 recordsLinked to original sources

arXiv2026

X-raying the arXiv: A Large-Scale Analysis of arXiv Submissions' Source Files

arXiv is the largest open-access repository for scientific literature. When submitting a paper, authors upload the manuscript's source files, from which the final PDF is compiled. These source files are also publicly downloadable, potentially exposing data unrelated to the published paper -- such as figures, documents, or comments -- that may unintentionally reveal confidential information or simply waste storage space. We thus ask ourselves: "What can be found within the source files of arXiv submissions?" We present a longitudinal analysis of ~600,000 submissions appeared on arXiv between 2015--2025. For each submission, we examine the uploaded source files to quantify and characterize data not required for producing the respective PDF. On average, 27% of the data in each submission are unnecessary, totaling >580 GB of redundant content across our dataset. Qualitative inspection reveals the presence of offensive/inappropriate text (e.g., "WTF does this mean?") and experimental details that could disclose ongoing research. We have contacted arXiv's leadership team, as well as the authors of affected papers to alert them of these issues. Finally, we propose recommendations and an automated tool to detect and analyze arXiv submissions residual data at scale, aiming to improve data hygiene in the arXiv's ecosystem.

cs.NI↗

arXiv2025

It's not Easy: Applying Supervised Machine Learning to Detect Malicious Extensions in the Chrome Web Store

Google Chrome is the most popular Web browser. Users can customize it with extensions that enhance their browsing experience. The most well-known marketplace of such extensions is the Chrome Web Store (CWS). Developers can upload their extensions on the CWS, but such extensions are made available to users only after a vetting process carried out by Google itself. Unfortunately, some malicious extensions bypass such checks, putting the security and privacy of downstream browser extension users at risk. Here, we scrutinize the extent to which automated mechanisms reliant on supervised machine learning (ML) can be used to detect malicious extensions on the CWS. To this end, we first collect 7,140 malicious extensions published in 2017--2023. We combine this dataset with 63,598 benign extensions published or updated on the CWS before 2023, and we develop three supervised-ML-based classifiers. We show that, in a "lab setting", our classifiers work well (e.g., 98% accuracy). Then, we collect a more recent set of 35,462 extensions from the CWS, published or last updated in 2023, with unknown ground truth. We were eventually able to identify 68 malicious extensions that bypassed the vetting process of the CWS. However, our classifiers also reported >1k likely malicious extensions. Based on this finding (further supported with empirical evidence), we elucidate, for the first time, a strong concept drift effect on browser extensions. We also show that commercial detectors (e.g., VirusTotal) work poorly to detect known malicious extensions. Altogether, our results highlight that detecting malicious browser extensions is a fundamentally hard problem. This requires additional work both by the research community and by Google itself -- potentially by revising their approaches. In the meantime, we informed Google of our discoveries, and we release our artifacts.

cs.CR↗

arXiv2025

COOKIEGUARD: Characterizing and Isolating the First-Party Cookie Jar

As third-party cookies are being phased out or restricted by major browsers, first-party cookies are increasingly repurposed for tracking. Prior work has shown that third-party scripts embedded in the main frame can access and exfiltrate first-party cookies, including those set by other third-party scripts. However, existing browser security mechanisms, such as the Same-Origin Policy, Content Security Policy, and third-party storage partitioning, do not prevent this type of cross-domain interaction within the main frame. While recent studies have begun to highlight this issue, there remains a lack of comprehensive measurement and practical defenses. In this work, we conduct the first large-scale measurement of cross-domain access to first-party cookies across 20,000 websites. We find that 56 percent of websites include third-party scripts that exfiltrate cookies they did not set, and 32 percent allow unauthorized overwriting or deletion, revealing significant confidentiality and integrity risks. To mitigate this, we propose CookieGuard, a browser-based runtime enforcement mechanism that isolates first-party cookies on a per-script-origin basis. CookieGuard blocks all unauthorized cross-domain cookie operations while preserving site functionality in most cases, with Single Sign-On disruption observed on 11 percent of sites. Our results expose critical flaws in current browser models and offer a deployable path toward stronger cookie isolation.

cs.CR↗

arXiv2025

SoK: On the Offensive Potential of AI

Our society increasingly benefits from Artificial Intelligence (AI). Unfortunately, more and more evidence shows that AI is also used for offensive purposes. Prior works have revealed various examples of use cases in which the deployment of AI can lead to violation of security and privacy objectives. No extant work, however, has been able to draw a holistic picture of the offensive potential of AI. In this SoK paper we seek to lay the ground for a systematic analysis of the heterogeneous capabilities of offensive AI. In particular we (i) account for AI risks to both humans and systems while (ii) consolidating and distilling knowledge from academic literature, expert opinions, industrial venues, as well as laypeople -- all of which being valuable sources of information on offensive AI. To enable alignment of such diverse sources of knowledge, we devise a common set of criteria reflecting essential technological factors related to offensive AI. With the help of such criteria, we systematically analyze: 95 research papers; 38 InfoSec briefings (from, e.g., BlackHat); the responses of a user study (N=549) entailing individuals with diverse backgrounds and expertise; and the opinion of 12 experts. Our contributions not only reveal concerning ways (some of which overlooked by prior work) in which AI can be offensively used today, but also represent a foothold to address this threat in the years to come.

cs.CR↗

arXiv2024

What is in the Chrome Web Store? Investigating Security-Noteworthy Browser Extensions

This paper is the first attempt at providing a holistic view of the Chrome Web Store (CWS). We leverage historical data provided by ChromeStats to study global trends in the CWS and security implications. We first highlight the extremely short life cycles of extensions: roughly 60% of extensions stay in the CWS for one year. Second, we define and show that Security-Noteworthy Extensions (SNE) are a significant issue: they pervade the CWS for years and affect almost 350 million users. Third, we identify clusters of extensions with a similar code base. We discuss how code similarity techniques could be used to flag suspicious extensions. By developing an approach to extract URLs from extensions' comments, we show that extensions reuse code snippets from public repositories or forums, leading to the propagation of dated code and vulnerabilities. Finally, we underline a critical lack of maintenance in the CWS: 60% of the extensions in the CWS have never been updated; half of the extensions known to be vulnerable are still in the CWS and still vulnerable 2 years after disclosure; a third of extensions use vulnerable library versions. We believe that these issues should be widely known in order to pave the way for a more secure CWS.

cs.CR↗

arXiv2023

Cloud Watching: Understanding Attacks Against Cloud-Hosted Services

Cloud computing has dramatically changed service deployment patterns. In this work, we analyze how attackers identify and target cloud services in contrast to traditional enterprise networks and network telescopes. Using a diverse set of cloud honeypots in 5~providers and 23~countries as well as 2~educational networks and 1~network telescope, we analyze how IP address assignment, geography, network, and service-port selection, influence what services are targeted in the cloud. We find that scanners that target cloud compute are selective: they avoid scanning networks without legitimate services and they discriminate between geographic regions. Further, attackers mine Internet-service search engines to find exploitable services and, in some cases, they avoid targeting IANA-assigned protocols, causing researchers to misclassify at least 15\% of traffic on select ports. Based on our results, we derive recommendations for researchers and operators.

cs.CR↗