Theory and Practice of Finding Eviction Sets

TL;DR

This paper systematically studies algorithms for efficiently finding small eviction sets crucial for cache side-channel and micro-architectural attacks, introducing novel linear-time algorithms with empirical validation.

Contribution

The paper formalizes eviction set finding, introduces novel linear-time algorithms based on threshold group testing, and empirically evaluates their effectiveness under practical conditions.

Findings

Algorithms find small eviction sets faster than previous methods.

Reliability affected by cache replacement and TLB thrashing.

New algorithms outperform quadratic-time approaches.

Abstract

Many micro-architectural attacks rely on the capability of an attacker to efficiently find small eviction sets: groups of virtual addresses that map to the same cache set. This capability has become a decisive primitive for cache side-channel, rowhammer, and speculative execution attacks. Despite their importance, algorithms for finding small eviction sets have not been systematically studied in the literature. In this paper, we perform such a systematic study. We begin by formalizing the problem and analyzing the probability that a set of random virtual addresses is an eviction set. We then present novel algorithms, based on ideas from threshold group testing, that reduce random eviction sets to their minimal core in linear time, improving over the quadratic state-of-the-art. We complement the theoretical analysis of our algorithms with a rigorous empirical evaluation in which we identify and isolate factors that affect their reliability in practice, such as adaptive cache replacement strategies and TLB thrashing. Our results indicate that our algorithms enable finding small eviction sets much faster than before, and under conditions where this was previously deemed impractical.