Nethammer: Inducing Rowhammer Faults through Network Requests
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Abstract (arXiv)
A fundamental assumption in software security is that memory contents do not change unless there is a legitimate deliberate modification. Classical fault attacks show that this assumption does not hold if the attacker has physical access. Rowhammer attacks showed that local code execution is already sufficient to break this assumption. Rowhammer exploits parasitic effects in DRAM to modify the content of a memory cell without accessing it. Instead, other memory locations are accessed at a high frequency. All Rowhammer attacks so far were local attacks, running either in a scripted language or native code. In this paper, we present Nethammer. Nethammer is the first truly remote Rowhammer attack, without a single attacker-controlled line of code on the targeted system. Systems that use uncached memory or flush instructions while handling network requests, e.g., for interaction with the network device, can be attacked using Nethammer. Other systems can still be attacked if they are protected with quality-of-service techniques like Intel CAT. We demonstrate that the frequency of the cache misses is in all three cases high enough to induce bit flips. We evaluated different bit flip scenarios. Depending on the location, the bit flip compromises either the security and integrity of the system and the data of its users, or it can leave persistent damage on the system, i.e., persistent denial of service. We investigated Nethammer on personal computers, servers, and mobile phones. Nethammer is a security landslide, making the formerly local attack a remote attack.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 May 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 3; 2024: 0; 2025: 2; 2026: 1.
7 papers.
- 2021Don't Knock! Rowhammer at the Backdoor of DNN Modelscited by 6
- 2025Maximal Brain Damage Without Data or Optimization: Disrupting Neural Networks via Sign-Bit Flipscited by 4
- 2023Compiled Models, Built-In Exploits: Uncovering Pervasive Bit-Flip Attack Surfaces in DNN Executablescited by 3
- 2023SAfER: Layer-Level Sensitivity Assessment for Efficient and Robust Neural Network Inferencecited by 1
- 2023Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferentcited by 0
- 2025Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clientscited by 0
- 2026Decision-Level Hijacking: Injecting Cognitive Bias into Large Language Models via Bit-Flip Attackscited by 0
- 2026Decision-Level Hijacking: Injecting Cognitive Bias into Large Language Models via Bit-Flip Attacks28 Jul 2026
- 2025Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients9 May 2025
- 2025Maximal Brain Damage Without Data or Optimization: Disrupting Neural Networks via Sign-Bit Flips11 Feb 2025
- 2023Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent2 Nov 2023
- 2023Compiled Models, Built-In Exploits: Uncovering Pervasive Bit-Flip Attack Surfaces in DNN Executables12 Sep 2023
- 2023SAfER: Layer-Level Sensitivity Assessment for Efficient and Robust Neural Network Inference9 Aug 2023
- 2021Don't Knock! Rowhammer at the Backdoor of DNN Models14 Oct 2021