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Detecting speculative leaks with compositional semantics

arXiv Security Archived Apr 01, 2026 ✓ Full text saved

arXiv:2603.29800v1 Announce Type: new Abstract: Speculative execution enhances processor performance by predicting intermediate results and executing instructions based on these predictions. However, incorrect predictions can lead to security vulnerabilities, as speculative instructions leave traces in microarchitectural components that attackers can exploit. This is demonstrated by the family of Spectre attacks. Unfortunately, existing countermeasures to these attacks lack a formal security cha

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    Computer Science > Cryptography and Security [Submitted on 31 Mar 2026] Detecting speculative leaks with compositional semantics Xaver Fabian, Marco Guarnieri, Boris Köpf, Jose F. Morales, Marco Patrignani, Jan Reineke, Andres Sanchez Speculative execution enhances processor performance by predicting intermediate results and executing instructions based on these predictions. However, incorrect predictions can lead to security vulnerabilities, as speculative instructions leave traces in microarchitectural components that attackers can exploit. This is demonstrated by the family of Spectre attacks. Unfortunately, existing countermeasures to these attacks lack a formal security characterization, making it difficult to verify their effectiveness. In this paper, we propose a novel framework for detecting information flows introduced by speculative execution and reasoning about software defenses. The theoretical foundation of our approach is speculative non-interference (SNI), a novel semantic notion of security against speculative execution attacks. SNI relates information leakage observed under a standard non-speculative semantics to leakage arising under semantics that explicitly model speculative execution. To capture their combined effects, we extend our framework with a mechanism to safely compose multiple speculative semantics, each focussing on a single aspect of speculation. This allows us to analyze the complex interactions and resulting leaks that can arise when multiple speculative mechanisms operate together. On the practical side, we develop Spectector, a symbolic analysis tool that uses our compositional framework and leverages SMT solvers to detect vulnerabilities and verify program security with respect to multiple speculation mechanisms. We demonstrate the effectiveness of Spectector through evaluations on standard security benchmarks and new vulnerability scenarios. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.29800 [cs.CR]   (or arXiv:2603.29800v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.29800 Focus to learn more Submission history From: Xaver Fabian [view email] [v1] Tue, 31 Mar 2026 14:32:12 UTC (128 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Apr 01, 2026
    Archived
    Apr 01, 2026
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