On the Incomparability of Cache Algorithms in Terms of Timing Leakage

Modern computer architectures rely on caches to reduce the latency gap between the CPU and main memory. While indispensable for performance, caches pose a serious threat to security because they leak information about memory access patterns of programs via execution time. In this paper, we present a novel approach for reasoning about the security of cache algorithms with respect to timing leaks. The basis of our approach is the notion of leak competitiveness, which compares the leakage of two cache algorithms on every possible program. Based on this notion, we prove the following two results: First, we show that leak competitiveness is symmetric in the cache algorithms. This implies that no cache algorithm dominates another in terms of leakage via a program's total execution time. This is in contrast to performance, where it is known that such dominance relationships exist. Second, when restricted to caches with finite control, the leak-competitiveness relationship between two cache algorithms is either asymptotically linear or constant. No other shapes are possible

Undermining User Privacy on Mobile Devices Using AI

Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to the privacy of mobile phone users. This is because applications leave distinct footprints in the processor, which can be used by malware to infer user activities. In this work, we show that these inference attacks are considerably more practical when combined with advanced AI techniques. In particular, we focus on profiling the activity in the last-level cache (LLC) of ARM processors. We employ a simple Prime+Probe based monitoring technique to obtain cache traces, which we classify with Deep Learning methods including Convolutional Neural Networks. We demonstrate our approach on an off-the-shelf Android phone by launching a successful attack from an unprivileged, zeropermission App in well under a minute. The App thereby detects running applications with an accuracy of 98% and reveals opened websites and streaming videos by monitoring the LLC for at most 6 seconds. This is possible, since Deep Learning compensates measurement disturbances stemming from the inherently noisy LLC monitoring and unfavorable cache characteristics such as random line replacement policies. In summary, our results show that thanks to advanced AI techniques, inference attacks are becoming alarmingly easy to implement and execute in practice. This once more calls for countermeasures that confine microarchitectural leakage and protect mobile phone applications, especially those valuing the privacy of their users