Cats vs. Spectre: An Axiomatic Approach to Modeling Speculative Execution Attacks

The work

AuthorsHernán Ponce-de-León; Johannes Kinder
Editors
Typeinproceedings
Year2022
Citekeyponcedeleon2022cats

Where it appeared

Published in2022 IEEE Symposium on Security and Privacy (SP)
Pages235--248

Identifiers

arXiv2108.13818 from its landing_page_url
DOI10.1109/sp46214.2022.9833774

Related

Distinct fromducousso2023moving
Distinct fromflur2016modelling
Distinct frommosier2022axiomatic
Distinct fromselfridge2014acl2

From bibtex.

Abstract

The Spectre family of speculative execution attacks have required a rethinking of formal methods for security. Approaches based on operational speculative semantics have made initial inroads towards finding vulnerable code and validating defenses. However, with each new attack grows the amount of microarchitectural detail that has to be integrated into the underlying semantics. We propose an alternative, light-weight and axiomatic approach to specifying speculative semantics that relies on insights from memory models for concurrency. We use the CAT modeling language for memory consistency to specify execution models that capture speculative control flow, store-to-load forwarding, predictive store forwarding, and memory ordering machine clears. We present a bounded model checking framework parametrized by our speculative CAT models and evaluate its implementation against the state of the art. Due to the axiomatic approach, our models can be rapidly extended to allow our framework to detect new types of attacks and validate defenses against them.

A copy is held

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How it got here

How it got hereagent via bibtex
Added2026-09-17 23:25 UTC
Approved bya person 2026-10-05 21:22 UTC

Filed under

hardware-security

Cite it as

@inproceedings{poncedeleon2022cats,
  title        = {Cats vs. Spectre: An Axiomatic Approach to Modeling Speculative Execution Attacks},
  author       = {Hernán Ponce-de-León and Johannes Kinder},
  year         = {2022},
  booktitle    = {2022 IEEE Symposium on Security and Privacy (SP)},
  pages        = {235--248},
  doi          = {10.1109/sp46214.2022.9833774},
}

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