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AutoPRAC: Automating Attack Discovery for PRAC-Based Rowhammer Defenses using Model Checkers

arXiv Security Archived Jun 24, 2026 ✓ Full text saved

arXiv:2606.23905v1 Announce Type: new Abstract: Per-Row Activation Counting (PRAC) in DDR5 is a specification to mitigate Rowhammer attacks by tracking activations per row and triggering mitigative refreshes when needed. However, the security of PRAC designs is currently evaluated using human-crafted attack patterns and we lack formal verification of their security properties, or automated techniques to detect implementation flaws. In this work, we present AutoPRAC, the first automated technique

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    Computer Science > Cryptography and Security [Submitted on 22 Jun 2026] AutoPRAC: Automating Attack Discovery for PRAC-Based Rowhammer Defenses using Model Checkers Joyce Qu, Gururaj Saileshwar Per-Row Activation Counting (PRAC) in DDR5 is a specification to mitigate Rowhammer attacks by tracking activations per row and triggering mitigative refreshes when needed. However, the security of PRAC designs is currently evaluated using human-crafted attack patterns and we lack formal verification of their security properties, or automated techniques to detect implementation flaws. In this work, we present AutoPRAC, the first automated technique to test the security of PRAC-based defenses using model checkers. AutoPRAC models PRAC implementations as bounded state machines and checks security-critical safety properties against a worst-case oracle attacker. If a property is violated, the framework produces a concrete counterexample trace corresponding to a successful attack. Using AutoPRAC, we uncover a previously unreported flaw in MOAT, a state-of-the-art PRAC defense, in its counter-reset policy that allows up to 34 activations to go undetected above the Rowhammer threshold. Our results demonstrate that AutoPRAC can automatically discover subtle security flaws in Rowhammer mitigations and serves as an early-stage design aid for attack discovery on PRAC designs. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.23905 [cs.CR]   (or arXiv:2606.23905v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.23905 Focus to learn more Submission history From: Joyce Qu [view email] [v1] Mon, 22 Jun 2026 20:05:53 UTC (100 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 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
    Jun 24, 2026
    Archived
    Jun 24, 2026
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