Biblio

This paper presents a new micro-architectural vulnerability on the power management units of modern computers which creates an electromagnetic-based side-channel. The key observations that enable us to discover this sidechannel are: 1) in an effort to manage and minimize power consumption, modern microprocessors have a number of possible operating modes (power states) in which various sub-systems of the processor are powered down, 2) for some of the transitions between power states, the processor also changes the operating mode of the voltage regulator module (VRM) that supplies power to the affected sub-system, and 3) the electromagnetic (EM) emanations from the VRM are heavily dependent on its operating mode. As a result, these state-dependent EM emanations create a side-channel which can potentially reveal sensitive information about the current state of the processor and, more importantly, the programs currently being executed. To demonstrate the feasibility of exploiting this vulnerability, we create a covert channel by utilizing the changes in the processor's power states. We show how such a covert channel can be leveraged to exfiltrate sensitive information from a secured and completely isolated (air-gapped) laptop system by placing a compact, inexpensive receiver in proximity to that system. To further show the severity of this attack, we also demonstrate how such a covert channel can be established when the target and the receiver are several meters away from each other, including scenarios where the receiver and the target are separated by a wall. Compared to the state-of-the-art, the proposed covert channel has \textbackslashtextgreater3x higher bit-rate. Finally, to demonstrate that this new vulnerability is not limited to being used as a covert channel, we demonstrate how it can be used for attacks such as keystroke logging.

In this paper we examine the use of covert channels based on CPU load in order to achieve persistent user identification through browser sessions. In particular, we demonstrate that an HTML5 video, a GIF image, or CSS animations on a webpage can be used to force the CPU to produce a sequence of distinct load levels, even without JavaScript or any client-side code. These load levels can be then captured either by another browsing session, running on the same or a different browser in parallel to the browsing session we want to identify, or by a malicious app installed on the device. To get a good estimation of the CPU load caused by the target session, the receiver can observe system statistics about CPU activity (app), or constantly measure time it takes to execute a known code segment (app and browser). Furthermore, for mobile devices we propose a sensor-based approach to estimate the CPU load, based on exploiting disturbances of the magnetometer sensor data caused by the high CPU activity. Captured loads can be decoded and translated into an identifying bit string, which is transmitted back to the attacker. Due to the way loads are produced, these methods are applicable even in highly restrictive browsers, such as the Tor Browser, and run unnoticeably to the end user. Therefore, unlike existing ways of web tracking, our methods circumvent most of the existing countermeasures, as they store the identifying information outside the browsing session being targeted. Finally, we also thoroughly evaluate and assess each presented method of generating and receiving the signal, and provide an overview of potential countermeasures.