Rowhammer.js: A Remote Software-Induced Fault Attack in JavaScript
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Abstract (arXiv)
A fundamental assumption in software security is that a memory location can only be modified by processes that may write to this memory location. However, a recent study has shown that parasitic effects in DRAM can change the content of a memory cell without accessing it, but by accessing other memory locations in a high frequency. This so-called Rowhammer bug occurs in most of today's memory modules and has fatal consequences for the security of all affected systems, e.g., privilege escalation attacks. All studies and attacks related to Rowhammer so far rely on the availability of a cache flush instruction in order to cause accesses to DRAM modules at a sufficiently high frequency. We overcome this limitation by defeating complex cache replacement policies. We show that caches can be forced into fast cache eviction to trigger the Rowhammer bug with only regular memory accesses. This allows to trigger the Rowhammer bug in highly restricted and even scripting environments. We demonstrate a fully automated attack that requires nothing but a website with JavaScript to trigger faults on remote hardware. Thereby we can gain unrestricted access to systems of website visitors. We show that the attack works on off-the-shelf systems. Existing countermeasures fail to protect against this new Rowhammer attack.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Jul 2015.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1; 2025: 1; 2026: 3.
9 papers.
- 2021DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memoriescited by 42
- 2021FooBaR: Fault Fooling Backdoor Attack on Neural Network Trainingcited by 6
- 2024PrisonBreak: Jailbreaking Large Language Models with at Most Twenty-Five Targeted Bit-flipscited by 5
- 2023Compiled Models, Built-In Exploits: Uncovering Pervasive Bit-Flip Attack Surfaces in DNN Executablescited by 3
- 2022Learned-Database Systems Securitycited by 2
- 2026TFL: Targeted Bit-Flip Attack on Large Language Modelcited by 2
- 2025Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clientscited by 0
- 2026Bit-Flip Vulnerability of Shared KV-Cache Blocks in LLM Serving Systemscited by 0
- 2026TEE-X: TEE-aware Acceleration Framework for Large Vision Models at the Edgecited by 0
- 2026TEE-X: TEE-aware Acceleration Framework for Large Vision Models at the Edge24 Aug 2026
- 2026Bit-Flip Vulnerability of Shared KV-Cache Blocks in LLM Serving Systems19 Apr 2026
- 2026TFL: Targeted Bit-Flip Attack on Large Language Model19 Feb 2026
- 2025Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients9 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
- 2022Learned-Database Systems Security20 Dec 2022
- 2021DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories8 Nov 2021
- 2021FooBaR: Fault Fooling Backdoor Attack on Neural Network Training23 Sep 2021