Alexander Sjösten (Chalmers University of Technology), Steven Van Acker (Chalmers University of Technology), Pablo Picazo-Sanchez (Chalmers University of Technology), Andrei Sabelfeld (Chalmers University of Technology)

Browser extensions enable rich experience for the users of today's web. Being
deployed with elevated privileges, extensions are given the power to overrule
web pages. As a result, web pages often seek to detect the installed extensions,
sometimes for benign adoption of their behavior but sometimes as part of
privacy-violating user fingerprinting.
Researchers have studied a class of attacks that allow detecting extensions by
probing for Web Accessible Resources (WARs) via URLs that include public
extension IDs.
Realizing privacy risks associated with WARs, Firefox has recently moved to
randomize a browser extension's ID, prompting the Chrome team to plan for
following the same path.
However, rather than mitigating the issue, the randomized IDs can in fact
exacerbate the extension detection problem, enabling attackers to use a
randomized ID as a reliable fingerprint of a user.
We study a class of extension revelation attacks, where extensions reveal
themselves by injecting their code on web pages.
We demonstrate how a combination of revelation and probing can uniquely identify
90% out of all extensions injecting content, in spite of a randomization scheme.
We perform a series of large-scale studies to estimate possible implications of
both classes of attacks.
As a countermeasure, we propose a browser-based mechanism that enables control
over which extensions are loaded on which web pages and present a proof of
concept implementation which blocks both classes of attacks.

View More Papers

Cybercriminal Minds: An investigative study of cryptocurrency abuses in...

Seunghyeon Lee (KAIST, S2W LAB Inc.), Changhoon Yoon (S2W LAB Inc.), Heedo Kang (KAIST), Yeonkeun Kim (KAIST), Yongdae Kim (KAIST), Dongsu Han (KAIST), Sooel Son (KAIST), Seungwon Shin (KAIST, S2W LAB Inc.)

Read More

Neural Machine Translation Inspired Binary Code Similarity Comparison beyond...

Fei Zuo (University of South Carolina), Xiaopeng Li (University of South Carolina), Patrick Young (Temple University), Lannan Luo (University of South Carolina), Qiang Zeng (University of South Carolina), Zhexin Zhang (University of South Carolina)

Read More

The Crux of Voice (In)Security: A Brain Study of...

Ajaya Neupane (University of California Riverside), Nitesh Saxena (University of Alabama at Birmingham), Leanne Hirshfield (Syracuse University), Sarah Elaine Bratt (Syracuse University)

Read More

RFDIDS: Radio Frequency-based Distributed Intrusion Detection System for the...

Tohid Shekari (ECE, Georgia Tech), Christian Bayens (ECE, Georgia Tech), Morris Cohen (ECE, Georgia Tech), Lukas Graber (ECE, Georgia Tech), Raheem Beyah (ECE, Georgia Tech)

Read More