Measuring and Disrupting Anti-Adblockers Using Differential Execution Analysis

Abstract

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 anti-adblockers 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 anti-adblockers, 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 top-10K 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.