Yossi Oren

Tomer Laor, Naif Mehanna, Antonin Durey et al.

Browser fingerprinting aims to identify users or their devices, through scripts that execute in the users' browser and collect information on software or hardware characteristics. It is used to track users or as an additional means of identification to improve security. In this paper, we report on a new technique that can significantly extend the tracking time of fingerprint-based tracking methods. Our technique, which we call DrawnApart, is a new GPU fingerprinting technique that identifies a device based on the unique properties of its GPU stack. Specifically, we show that variations in speed among the multiple execution units that comprise a GPU can serve as a reliable and robust device signature, which can be collected using unprivileged JavaScript. We investigate the accuracy of DrawnApart under two scenarios. In the first scenario, our controlled experiments confirm that the technique is effective in distinguishing devices with similar hardware and software configurations, even when they are considered identical by current state-of-the-art fingerprinting algorithms. In the second scenario, we integrate a one-shot learning version of our technique into a state-of-the-art browser fingerprint tracking algorithm. We verify our technique through a large-scale experiment involving data collected from over 2,500 crowd-sourced devices over a period of several months and show it provides a boost of up to 67% to the median tracking duration, compared to the state-of-the-art method. DrawnApart makes two contributions to the state of the art in browser fingerprinting. On the conceptual front, it is the first work that explores the manufacturing differences between identical GPUs and the first to exploit these differences in a privacy context. On the practical front, it demonstrates a robust technique for distinguishing between machines with identical hardware and software configurations.

Anatoly Shusterman, Ayush Agarwal, Sioli O'Connell et al.

The "eternal war in cache" has reached browsers, with multiple cache-based side-channel attacks and countermeasures being suggested. A common approach for countermeasures is to disable or restrict JavaScript features deemed essential for carrying out attacks. To assess the effectiveness of this approach, in this work we seek to identify those JavaScript features which are essential for carrying out a cache-based attack. We develop a sequence of attacks with progressively decreasing dependency on JavaScript features, culminating in the first browser-based side-channel attack which is constructed entirely from Cascading Style Sheets (CSS) and HTML, and works even when script execution is completely blocked. We then show that avoiding JavaScript features makes our techniques architecturally agnostic, resulting in microarchitectural website fingerprinting attacks that work across hardware platforms including Intel Core, AMD Ryzen, Samsung Exynos, and Apple M1 architectures. As a final contribution, we evaluate our techniques in hardened browser environments including the Tor browser, Deter-Fox (Cao el al., CCS 2017), and Chrome Zero (Schwartz et al., NDSS 2018). We confirm that none of these approaches completely defend against our attacks. We further argue that the protections of Chrome Zero need to be more comprehensively applied, and that the performance and user experience of Chrome Zero will be severely degraded if this approach is taken.

Adar Ovadya, Rom Ogen, Yakov Mallah et al.

Many organizations protect secure networked devices from non-secure networked devices by assigning each class of devices to a different logical network. These two logical networks, commonly called the host network and the guest network, use the same router hardware, which is designed to isolate the two networks in software. In this work we show that logical network isolation based on host and guest networks can be overcome by the use of cross-router covert channels. Using specially-crafted network traffic, these channels make it possible to leak data between the host network and the guest network, and vice versa, through the use of the router as a shared medium. We performed a survey of routers representing multiple vendors and price points, and discovered that all of the routers we surveyed are vulnerable to at least one class of covert channel. Our attack can succeed even if the attacker has very limited permissions on the infected device, and even an iframe hosting malicious JavaScript code can be used for this purpose. We provide several metrics for the effectiveness of such channels, based on their pervasiveness, rate and covertness, and discuss possible ways of identifying and preventing these leakages.

Kevin Sam Tharayil, Benyamin Farshteindiker, Shaked Eyal et al.

Position sensors, such as the gyroscope, the magnetometer and the accelerometer, are found in a staggering variety of devices, from smartphones and UAVs to autonomous robots. Several works have shown how adversaries can mount spoofing attacks to remotely corrupt or even completely control the outputs of these sensors. With more and more critical applications relying on sensor readings to make important decisions, defending sensors from these attacks is of prime importance. In this work we present practical software based defenses against attacks on two common types of position sensors, specifically the gyroscope and the magnetometer. We first characterize the sensitivity of these sensors to acoustic and magnetic adversaries. Next, we present two software-only defenses: a machine learning based single sensor defense, and a sensor fusion defense which makes use of the mathematical relationship between the two sensors. We performed a detailed theoretical analysis of our defenses, and implemented them on a variety of smartphones, as well as on a resource-constrained IoT sensor node. Our defenses do not require any hardware or OS-level modifications, making it possible to use them with existing hardware. Moreover, they provide a high detection accuracy, a short detection time and a reasonable power consumption.

Anatoly Shusterman, Lachlan Kang, Yarden Haskal et al.

Website fingerprinting attacks, which use statistical analysis on network traffic to compromise user privacy, have been shown to be effective even if the traffic is sent over anonymity-preserving networks such as Tor. The classical attack model used to evaluate website fingerprinting attacks assumes an on-path adversary, who can observe all traffic traveling between the user's computer and the Tor network. In this work we investigate these attacks under a different attack model, in which the adversary is capable of running a small amount of unprivileged code on the target user's computer. Under this model, the attacker can mount cache side-channel attacks, which exploit the effects of contention on the CPU's cache, to identify the website being browsed. In an important special case of this attack model, a JavaScript attack is launched when the target user visits a website controlled by the attacker. The effectiveness of this attack scenario has never been systematically analyzed, especially in the open-world model which assumes that the user is visiting a mix of both sensitive and non-sensitive sites. In this work we show that cache website fingerprinting attacks in JavaScript are highly feasible, even when they are run from highly restrictive environments, such as the Tor Browser. Specifically, we use machine learning techniques to classify traces of cache activity. Unlike prior works, which try to identify cache conflicts, our work measures the overall occupancy of the last-level cache. We show that our approach achieves high classification accuracy in both the open-world and the closed-world models. We further show that our techniques are resilient both to network-based defenses and to side-channel countermeasures introduced to modern browsers as a response to the Spectre attack.

Omer Shwartz, Amir Cohen, Asaf Shabtai et al.

Phone touchscreens, and other similar hardware components such as orientation sensors, wireless charging controllers, and NFC readers, are often produced by third-party manufacturers and not by the phone vendors themselves. Third-party driver source code to support these components is integrated into the vendor's source code. In contrast to 'pluggable' drivers, such as USB or network drivers, the component driver's source code implicitly assumes that the component hardware is authentic and trustworthy. As a result of this trust, very few integrity checks are performed on the communications between the component and the device's main processor. In this paper, we call this trust into question, considering the fact that touchscreens are often shattered and then replaced with aftermarket components of questionable origin. We analyze the operation of a commonly used touchscreen controller. We construct two standalone attacks, based on malicious touchscreen hardware, that function as building blocks toward a full attack: a series of touch injection attacks that allow the touchscreen to impersonate the user and exfiltrate data, and a buffer overflow attack that lets the attacker execute privileged operations. Combining the two building blocks, we present and evaluate a series of end-to-end attacks that can severely compromise a stock Android phone with standard firmware. Our results make the case for a hardware-based physical countermeasure.