Fabian Rauscher (Graz University of Technology), Carina Fiedler (Graz University of Technology), Andreas Kogler (Graz University of Technology), Daniel Gruss (Graz University of Technology)

CPU caches are among the most widely studied side-channel targets, with Prime+Probe and Flush+Reload being the most prominent techniques. These generic cache attack techniques can leak cryptographic keys, user input, and are a building block of many microarchitectural attacks.

In this paper, we present the first systematic evaluation using 9 characteristics of the 4 most relevant cache attacks, Flush+Reload, Flush+Flush, Evict+Reload, and Prime+Probe, as well as three new attacks that we introduce: Demote+Reload, Demote+Demote, and DemoteContention. We evaluate hit-miss margins, temporal precision, spatial precision, topological scope, attack time, blind spot length, channel capacity, noise resilience, and detectability on recent Intel microarchitectures. Demote+Reload and Demote+Demote perform similar to previous attacks and slightly better in some cases, e.g., Demote+Reload has a 60.7 % smaller blind spot than Flush+Reload. With 15.48 Mbit/s, Demote+Reload has a 64.3 % higher channel capacity than Flush+Reload. We also compare all attacks in an AES T-table attack and compare Demote+Reload and Flush+Reload in an inter-keystroke timing attack. Beyond the scope of the prior attack techniques, we demonstrate a KASLR break with Demote+Demote and the amplification of power side-channel leakage with Demote+Reload. Finally, Sapphire Rapids and Emerald Rapids CPUs use a non-inclusive L3 cache, effectively limiting eviction-based cross-core attacks, e.g., Prime+Probe and Evict+Reload, to rare cases where the victim’s activity reaches the L3 cache. Hence, we show that in a cross-core attack, DemoteContention can be used as a reliable alternative to Prime+Probe and Evict+Reload that does not require reverse-engineering of addressing functions and cache replacement policy.

View More Papers

RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial...

Dzung Pham (University of Massachusetts Amherst), Shreyas Kulkarni (University of Massachusetts Amherst), Amir Houmansadr (University of Massachusetts Amherst)

Read More

Speak Up, I’m Listening: Extracting Speech from Zero-Permission VR...

Derin Cayir (Florida International University), Reham Mohamed Aburas (American University of Sharjah), Riccardo Lazzeretti (Sapienza University of Rome), Marco Angelini (Link Campus University of Rome), Abbas Acar (Florida International University), Mauro Conti (University of Padua), Z. Berkay Celik (Purdue University), Selcuk Uluagac (Florida International University)

Read More

Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication...

Jung-Woo Chang (University of California, San Diego), Ke Sun (University of California, San Diego), Nasimeh Heydaribeni (University of California, San Diego), Seira Hidano (KDDI Research, Inc.), Xinyu Zhang (University of California, San Diego), Farinaz Koushanfar (University of California, San Diego)

Read More