Today, more and more web browsers and extensions provide anonymity features to hide user details. Primarily used to evade tracking by websites and advertisements, these features are also used by criminals to prevent identification. Thus, not only tracking companies but also law-enforcement agencies have an interest in finding flaws which break these anonymity features. For instance, for targeted exploitation using zero days, it is essential to have as much information about the target as possible. A failed exploitation attempt, e.g., due to a wrongly guessed operating system, can burn the zero-day, effectively costing the attacker money. Also for side-channel attacks, it is of the utmost importance to know certain aspects of the victim's hardware configuration, e.g., the instruction-set architecture. Moreover, knowledge about specific environmental properties, such as the operating system, allows crafting more plausible dialogues for phishing attacks.

In this paper, we present a fully automated approach to find subtle differences in browser engines caused by the environment. Furthermore, we present two new side-channel attacks on browser engines to detect the instruction-set architecture and the used memory allocator. Using these differences, we can deduce information about the system, both about the software as well as the hardware. As a result, we cannot only ease the creation of fingerprints, but we gain the advantage of having a more precise picture for targeted exploitation. Our approach allows automating the cumbersome manual search for such differences. We collect all data available to the JavaScript engine and build templates from these properties. If a property of such a template stays the same on one system but differs on a different system, we found an environment-dependent property.

We found environment-dependent properties in Firefox, Chrome, Edge, and mobile Tor, allowing us to reveal the underlying operating system, CPU architecture, used privacy-enhancing plugins, as well as exact browser version. We stress that our method should be used in the development of browsers and privacy extensions to automatically find flaws in the implementation.

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