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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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✦ AI Summary· Claude Sonnet
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
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Submission history
From: Kaustav Goswami [view email]
[v1] Wed, 27 May 2026 00:47:58 UTC (873 KB)
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