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

Profit: Detecting and Quantifying Side Channels in Networked Applications

Nicolás Rosner (University of California, Santa Barbara), Ismet Burak Kadron (University of California, Santa Barbara), Lucas Bang (Harvey Mudd College), Tevfik Bultan (University of California, Santa Barbara)

Read More

OBFUSCURO: A Commodity Obfuscation Engine on Intel SGX

Adil Ahmad (Purdue), Byunggill Joe (KAIST), Yuan Xiao (Ohio State University), Yinqian Zhang (Ohio State University), Insik Shin (KAIST), Byoungyoung Lee (Purdue/SNU)

Read More

ML-Leaks: Model and Data Independent Membership Inference Attacks and...

Ahmed Salem (CISPA Helmholtz Center for Information Security), Yang Zhang (CISPA Helmholtz Center for Information Security), Mathias Humbert (Swiss Data Science Center, ETH Zurich/EPFL), Pascal Berrang (CISPA Helmholtz Center for Information Security), Mario Fritz (CISPA Helmholtz Center for Information Security), Michael Backes (CISPA Helmholtz Center for Information Security)

Read More

CRCount: Pointer Invalidation with Reference Counting to Mitigate Use-after-free...

Jangseop Shin (Seoul National University and Inter-University Semiconductor Research Center), Donghyun Kwon (Seoul National University and Inter-University Semiconductor Research Center), Jiwon Seo (Seoul National University and Inter-University Semiconductor Research Center), Yeongpil Cho (Soongsil University), Yunheung Paek (Seoul National University and Inter-University Semiconductor Research Center)

Read More