Revisiting RowHammer: An Experimental Analysis of Modern DRAM Devices and Mitigation Techniques
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Abstract (arXiv)
In order to shed more light on how RowHammer affects modern and future devices at the circuit-level, we first present an experimental characterization of RowHammer on 1580 DRAM chips (408x DDR3, 652x DDR4, and 520x LPDDR4) from 300 DRAM modules (60x DDR3, 110x DDR4, and 130x LPDDR4) with RowHammer protection mechanisms disabled, spanning multiple different technology nodes from across each of the three major DRAM manufacturers. Our studies definitively show that newer DRAM chips are more vulnerable to RowHammer: as device feature size reduces, the number of activations needed to induce a RowHammer bit flip also reduces, to as few as 9.6k (4.8k to two rows each) in the most vulnerable chip we tested. We evaluate five state-of-the-art RowHammer mitigation mechanisms using cycle-accurate simulation in the context of real data taken from our chips to study how the mitigation mechanisms scale with chip vulnerability. We find that existing mechanisms either are not scalable or suffer from prohibitively large performance overheads in projected future devices given our observed trends of RowHammer vulnerability. Thus, it is critical to research more effective solutions to RowHammer.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 May 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 0; 2025: 2.
5 papers.
- 2025On Jailbreaking Quantized Language Models Through Fault Injection Attackscited by 8
- 2021Don't Knock! Rowhammer at the Backdoor of DNN Modelscited by 6
- 2025LM-Fix: Lightweight Bit-Flip Detection and Rapid Recovery Framework for Language Modelscited by 3
- 2023DNN-Defender: A Victim-Focused In-DRAM Defense Mechanism for Taming Adversarial Weight Attack on DNNscited by 2
- 2021Attacking Deep Learning AI Hardware with Universal Adversarial Perturbationcited by 0
- 2025LM-Fix: Lightweight Bit-Flip Detection and Rapid Recovery Framework for Language Models3 Nov 2025
- 2025On Jailbreaking Quantized Language Models Through Fault Injection Attacks4 Jul 2025
- 2023DNN-Defender: A Victim-Focused In-DRAM Defense Mechanism for Taming Adversarial Weight Attack on DNNs14 May 2023
- 2021Attacking Deep Learning AI Hardware with Universal Adversarial Perturbation18 Nov 2021
- 2021Don't Knock! Rowhammer at the Backdoor of DNN Models14 Oct 2021