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HammerSim: A System-Level Tool to Model RowHammer

arXiv Security Archived May 28, 2026 ✓ Full text saved

arXiv:2605.27803v1 Announce Type: new Abstract: Modern architecture research relies on simulators to evaluate system security, yet analyzing emerging hardware vulnerabilities like RowHammer requires full-system visibility. As RowHammer vulnerabilities worsen with continuous technology scaling, existing simulators lack the system-level models needed to study complex OS effects and cross-layer mitigations. This tool deficiency leaves modern computing platforms exposed to severe reliability and sec

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    Computer Science > Cryptography and Security [Submitted on 27 May 2026] HammerSim: A System-Level Tool to Model RowHammer Kaustav Goswami, Ayaz Akram, Hari Venugopalan, Jason Lowe-Power Modern architecture research relies on simulators to evaluate system security, yet analyzing emerging hardware vulnerabilities like RowHammer requires full-system visibility. As RowHammer vulnerabilities worsen with continuous technology scaling, existing simulators lack the system-level models needed to study complex OS effects and cross-layer mitigations. This tool deficiency leaves modern computing platforms exposed to severe reliability and security risks. In this work, we present HammerSim, a gem5-based framework for modeling RowHammer at the full-system level. HammerSim integrates probability-driven bitflip modeling to realistically capture the behavior of RowHammer. It further enables evaluation of hardware and software mitigations such as TRR and selective ECC. We validate HammerSim's bitflip modeling against real DDR4 DIMMs using JS divergence, demonstrating its utility in studying attacks, defenses, and benign workload susceptibility. Our framework provides an extensible platform to bridge the gap between hardware experiments and architectural simulation. Subjects: Cryptography and Security (cs.CR); Hardware Architecture (cs.AR) Cite as: arXiv:2605.27803 [cs.CR]   (or arXiv:2605.27803v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.27803 Focus to learn more Submission history From: Kaustav Goswami [view email] [v1] Wed, 27 May 2026 00:47:58 UTC (873 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.AR 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
    May 28, 2026
    Archived
    May 28, 2026
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