Another Flip in the Wall of Rowhammer Defenses
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Abstract (arXiv)
The Rowhammer bug allows unauthorized modification of bits in DRAM cells from unprivileged software, enabling powerful privilege-escalation attacks. Sophisticated Rowhammer countermeasures have been presented, aiming at mitigating the Rowhammer bug or its exploitation. However, the state of the art provides insufficient insight on the completeness of these defenses. In this paper, we present novel Rowhammer attack and exploitation primitives, showing that even a combination of all defenses is ineffective. Our new attack technique, one-location hammering, breaks previous assumptions on requirements for triggering the Rowhammer bug, i.e., we do not hammer multiple DRAM rows but only keep one DRAM row constantly open. Our new exploitation technique, opcode flipping, bypasses recent isolation mechanisms by flipping bits in a predictable and targeted way in userspace binaries. We replace conspicuous and memory-exhausting spraying and grooming techniques with a novel reliable technique called memory waylaying. Memory waylaying exploits system-level optimizations and a side channel to coax the operating system into placing target pages at attacker-chosen physical locations. Finally, we abuse Intel SGX to hide the attack entirely from the user and the operating system, making any inspection or detection of the attack infeasible. Our Rowhammer enclave can be used for coordinated denial-of-service attacks in the cloud and for privilege escalation on personal computers. We demonstrate that our attacks evade all previously proposed countermeasures for commodity systems.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Oct 2017.
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Citing papers per year: 2021: 6; 2022: 1; 2023: 1; 2024: 1; 2025: 3.
12 papers.
- 2021Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bitscited by 49
- 2021DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memoriescited by 42
- 2021RADAR: Run-time Adversarial Weight Attack Detection and Accuracy Recoverycited by 18
- 2022Versatile Weight Attack via Flipping Limited Bitscited by 13
- 2021RA-BNN: Constructing Robust & Accurate Binary Neural Network to Simultaneously Defend Adversarial Bit-Flip Attack and Improve Accuracycited by 11
- 2021Don't Knock! Rowhammer at the Backdoor of DNN Modelscited by 6
- 2024PrisonBreak: Jailbreaking Large Language Models with at Most Twenty-Five Targeted Bit-flipscited by 5
- 2021Ownership Verification of DNN Architectures via Hardware Cache Side Channelscited by 3
- 2023Compiled Models, Built-In Exploits: Uncovering Pervasive Bit-Flip Attack Surfaces in DNN Executablescited by 3
- 2025BitHydra: Towards Bit-flip Inference Cost Attack against Large Language Modelscited by 2
- 2025PaniCar: Securing the Perception of Advanced Driving Assistance Systems Against Emergency Vehicle Lightingcited by 0
- 2025How a Bit Becomes a Story: Semantic Steering via Differentiable Fault Injectioncited by 0
- 2025How a Bit Becomes a Story: Semantic Steering via Differentiable Fault Injection9 Dec 2025
- 2025BitHydra: Towards Bit-flip Inference Cost Attack against Large Language Models22 May 2025
- 2025PaniCar: Securing the Perception of Advanced Driving Assistance Systems Against Emergency Vehicle Lighting8 May 2025
- 2024PrisonBreak: Jailbreaking Large Language Models with at Most Twenty-Five Targeted Bit-flips10 Dec 2024
- 2023Compiled Models, Built-In Exploits: Uncovering Pervasive Bit-Flip Attack Surfaces in DNN Executables12 Sep 2023
- 2022Versatile Weight Attack via Flipping Limited Bits25 Jul 2022
- 2021DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories8 Nov 2021
- 2021Don't Knock! Rowhammer at the Backdoor of DNN Models14 Oct 2021
- 2021RA-BNN: Constructing Robust & Accurate Binary Neural Network to Simultaneously Defend Adversarial Bit-Flip Attack and Improve Accuracy22 Mar 2021
- 2021Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits21 Feb 2021
- 2021Ownership Verification of DNN Architectures via Hardware Cache Side Channels6 Feb 2021
- 2021RADAR: Run-time Adversarial Weight Attack Detection and Accuracy Recovery20 Jan 2021