Since the discovery of Spectre, a large number of hardware mechanisms for secure speculation has been proposed. Intuitively, more defensive mechanisms are less efficient but can securely execute a larger class of programs, while more permissive mechanisms may offer more performance but require more defensive programming. Unfortunately, there are no hardware-software contracts that would turn this intuition into a basis for principled co-design.In this paper, we put forward a framework for specifying such contracts, and we demonstrate its expressiveness and flexibility.On the hardware side, we use the framework to provide the first formalization and comparison of the security guarantees provided by a representative class of mechanisms for secure speculation.On the software side, we use the framework to characterize program properties that guarantee secure co-design in two scenarios traditionally investigated in isolation: (1) ensuring that a benign program does not leak information while computing on confidential data, and (2) ensuring that a potentially malicious program cannot read outside of its designated sandbox. Finally, we show how the properties corresponding to both scenarios can be checked based on existing tools for software verification, and we use them to validate our findings on executable code.

We show how to infer deterministic cache replacement policies using off-the-shelf automata learning and program synthesis techniques. For this, we construct and chain two abstractions that expose the cache replacement policy of any set in the cache hierarchy as a membership oracle to the learning algorithm, based on timing measurements on a silicon CPU. Our experiments demonstrate an advantage in scope and scalability over prior art and uncover two previously undocumented cache replacement policies.

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.