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Web privacy is challenged by pixel-stealing attacks, which allow attackers to extract content from embedded iframes and to detect visited links. To protect against multiple pixelstealing attacks that exploited timing variations in SVG filters, browser vendors repeatedly adapted their implementations to eliminate timing variations. In this work we demonstrate that past efforts are still not sufficient. We show how web-based attackers can mount cache-based side-channel attacks to monitor data-dependent memory accesses in filter rendering functions. We identify conditions under which browsers elect the non-default CPU implementation of SVG filters, and develop techniques for achieving access to the high-resolution timers required for cache attacks. We then develop efficient techniques to use the pixel-stealing attack for text recovery from embedded pages and to achieve high-speed history sniffing. To the best of our knowledge, our attack is the first to leak multiple bits per screen refresh, achieving an overall rate of 267 bits per second. 

Rowhammer is a hardware vulnerability in DDR memory by which attackers can perform specific access patterns in their own memory to flip bits in adjacent, uncontrolled rows with- out accessing them. Since its discovery by Kim et. al. (ISCA 2014), Rowhammer attacks have emerged as an alarming threat to numerous security mechanisms. In this paper, we show that Rowhammer attacks can in fact be more effective when combined with bank-level parallelism, a technique in which the attacker hammers multiple memory banks simultaneously. This allows us to increase the amount of Rowhammer-induced flips 7-fold and significantly speed up prior Rowhammer attacks relying on native code execution. Furthermore, we tackle the task of mounting browser-based Rowhammer attacks. Here, we develop a self-evicting ver- sion of multi-bank hammering, allowing us to replace clflush instructions with cache evictions. We then develop a novel method for detecting contiguous physical addresses using memory access timings, thereby obviating the need for trans- parent huge pages. Finally, by combining both techniques, we are the first, to our knowledge, to obtain Rowhammer bit flips on DDR4 memory from the Chrome and Firefox browsers running on default Linux configurations, without enabling transparent huge pages. 

Side-channel attacks that leak sensitive information through a computing device's interaction with its physical environment have proven to be a severe threat to devices' security, particularly when adversaries have unfettered physical access to the device. Traditional approaches for leakage detection measure the physical properties of the device. Hence, they cannot be used during the design process and fail to provide root cause analysis. An alternative approach that is gaining traction is to automate leakage detection by modeling the device. The demand to understand the scope, benefits, and limitations of the proposed tools intensifies with the increase in the number of proposals. In this SoK, we classify approaches to automated leakage detection based on the model's source of truth. We classify the existing tools on two main parameters: whether the model includes measurements from a concrete device and the abstraction level of the device specification used for constructing the model. We survey the proposed tools to determine the current knowledge level across the domain and identify open problems. In particular, we highlight the absence of evaluation methodologies and metrics that would compare proposals' effectiveness from across the domain. We believe that our results help practitioners who want to use automated leakage detection and researchers interested in advancing the knowledge and improving automated leakage detection.