Retrospective: Flipping Bits in Memory Without Accessing Them: An Experimental Study of DRAM Disturbance Errors
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Abstract (arXiv)
Our ISCA 2014 paper provided the first scientific and detailed characterization, analysis, and real-system demonstration of what is now popularly known as the RowHammer phenomenon (or vulnerability) in modern commodity DRAM chips, which are used as main memory in almost all modern computing systems. It experimentally demonstrated that more than 80% of all DRAM modules we tested from the three major DRAM vendors were vulnerable to the RowHammer read disturbance phenomenon: one can predictably induce bitflips (i.e., data corruption) in real DRAM modules by repeatedly accessing a DRAM row and thus causing electrical disturbance to physically nearby rows. We showed that a simple unprivileged user-level program induced RowHammer bitflips in multiple real systems and suggested that a security attack can be built using this proof-of-concept to hijack control of the system or cause other harm. To solve the RowHammer problem, our paper examined seven different approaches (including a novel probabilistic approach that has very low cost), some of which influenced or were adopted in different industrial products. Many later works from various research communities examined RowHammer, building real security attacks, proposing new defenses, further analyzing the problem at various (e.g., device/circuit, architecture, and system) levels, and exploiting RowHammer for various purposes (e.g., to reverse-engineer DRAM chips). Industry has worked to mitigate the problem, changing both memory controllers and DRAM standards/chips. Two major DRAM vendors finally wrote papers on the topic in 2023, describing their current approaches to mitigate RowHammer. Research & development on RowHammer in both academia & industry continues to be very active and fascinating. This short retrospective provides a brief analysis of our ISCA 2014 paper and its impact.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jun 2023.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 2.
5 papers.
- 2023One-bit Flip is All You Need: When Bit-flip Attack Meets Model Trainingcited by 14
- 2024Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspectivecited by 8
- 2025LM-Fix: Lightweight Bit-Flip Detection and Rapid Recovery Framework for Language Modelscited by 3
- 2024DeepBaR: Fault Backdoor Attack on Deep Neural Network Layerscited by 1
- 2025Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generationcited by 1
- 2025LM-Fix: Lightweight Bit-Flip Detection and Rapid Recovery Framework for Language Models3 Nov 2025
- 2025Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation8 Jul 2025
- 2024Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective4 Dec 2024
- 2024DeepBaR: Fault Backdoor Attack on Deep Neural Network Layers30 Jul 2024
- 2023One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training12 Aug 2023