Millions of people use adblockers to remove intrusive
and malicious ads as well as protect themselves against
tracking and pervasive surveillance. Online publishers consider
adblockers a major threat to the ad-powered “free” Web. They
have started to retaliate against adblockers by employing antiadblockers
which can detect and stop adblock users. To counter
this retaliation, adblockers in turn try to detect and filter
anti-adblocking scripts. This back and forth has prompted an
escalating arms race between adblockers and anti-adblockers.
We want to develop a comprehensive understanding of antiadblockers,
with the ultimate aim of enabling adblockers to
bypass state-of-the-art anti-adblockers. In this paper, we present a
differential execution analysis to automatically detect and analyze
anti-adblockers. At a high level, we collect execution traces by
visiting a website with and without adblockers. Through differential
execution analysis, we are able to pinpoint the conditions
that lead to the differences caused by anti-adblocking code. Using
our system, we detect anti-adblockers on 30.5% of the Alexa top10K
websites which is 5-52 times more than reported in prior
literature. Unlike prior work which is limited to detecting visible
reactions (e.g., warning messages) by anti-adblockers, our system
can discover attempts to detect adblockers even when there is no
visible reaction. From manually checking one third of the detected
websites, we find that the websites that have no visible reactions
constitute over 90% of the cases, completely dominating the ones
that have visible warning messages. Finally, based on our findings,
we further develop JavaScript rewriting and API hooking based
solutions (the latter implemented as a Chrome extension) to help
adblockers bypass state-of-the-art anti-adblockers.
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Eluding ML-based Adblockers With Actionable Adversarial Examples
Online advertisers have been quite successful in circumventing traditional adblockers that rely on manually curated rules to detect ads. As a result, adblockers have started to use machine learning (ML) classifiers for more robust detection and blocking of ads. Among these, AdGraph which leverages rich contextual information to classify ads, is arguably, the state of the art ML-based adblocker. In this paper, we present a4, a tool that intelligently crafts adversarial ads to evade AdGraph. Unlike traditional adversarial examples in the computer vision domain that can perturb any pixels (i.e., unconstrained), adversarial ads generated by a4 are actionable in the sense that they preserve the application semantics of the web page. Through a series of experiments we show that a4 can bypass AdGraph about 81% of the time, which surpasses the state-of-the-art attack by a significant margin of 145.5%, with an overhead of <20% and perturbations that are visually imperceptible in the rendered webpage. We envision that a4’s framework can be used to potentially launch adversarial attacks against other ML-based web applications.
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- Award ID(s):
- 1719147
- NSF-PAR ID:
- 10311750
- Date Published:
- Journal Name:
- ACSAC: Annual Computer Security Applications Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3485832.3488008
