FP-Rowhammer: DRAM-Based Device Fingerprinting

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Device fingerprinting leverages attributes that capture heterogeneity in hardware and software configurations to extract unique and stable fingerprints. Fingerprinting countermeasures attempt to either present a uniform fingerprint across different devices through normalization or present different fingerprints for the same device each time through obfuscation. We present FP-Rowhammer, a Rowhammer-based device fingerprinting approach that can build unique and stable fingerprints even across devices with normalized or obfuscated hardware and software configurations. To this end, FP-Rowhammer leverages the DRAM manufacturing process variation that gives rise to unique distributions of Rowhammer-induced bit flips across different DRAM modules. Our evaluation on a test bed of 98 DRAM modules shows that FP-Rowhammer achieves 99.91% fingerprinting accuracy. FP-Rowhammer’s fingerprints are also stable, with no degradation in fingerprinting accuracy over a period of ten days. We also demonstrate that FP-Rowhammer is efficient, taking less than five seconds to extract a fingerprint. FP-Rowhammer is the first Rowhammer fingerprinting approach to extract unique and stable fingerprints efficiently and at scale.

Citation

Hari Venugopalan, Kaustav Goswami, Zainul Abi Din, Jason Lowe-Power, Samuel T. King, and Zubair Shafiq. 2025. FP-Rowhammer: DRAM-Based Device Fingerprinting. In Proceedings of the 20th ACM Asia Conference on Computer and Communications Security (ASIA CCS ‘25). Association for Computing Machinery, New York, NY, USA, 1141–1157. https://doi.org/10.1145/3708821.3733880

@inproceedings{10.1145/3708821.3733880,
author = {Venugopalan, Hari and Goswami, Kaustav and Abi Din, Zainul and Lowe-Power, Jason and King, Samuel T. and Shafiq, Zubair},
title = {FP-Rowhammer: DRAM-Based Device Fingerprinting},
year = {2025},
isbn = {9798400714108},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3708821.3733880},
doi = {10.1145/3708821.3733880},
abstract = {Device fingerprinting leverages attributes that capture heterogeneity in hardware and software configurations to extract unique and stable fingerprints. Fingerprinting countermeasures attempt to either present a uniform fingerprint across different devices through normalization or present different fingerprints for the same device each time through obfuscation. We present FP-Rowhammer, a Rowhammer-based device fingerprinting approach that can build unique and stable fingerprints even across devices with normalized or obfuscated hardware and software configurations. To this end, FP-Rowhammer leverages the DRAM manufacturing process variation that gives rise to unique distributions of Rowhammer-induced bit flips across different DRAM modules. Our evaluation on a test bed of 98 DRAM modules shows that FP-Rowhammer achieves 99.91\% fingerprinting accuracy. FP-Rowhammer’s fingerprints are also stable, with no degradation in fingerprinting accuracy over a period of ten days. We also demonstrate that FP-Rowhammer is efficient, taking less than five seconds to extract a fingerprint. FP-Rowhammer is the first Rowhammer fingerprinting approach to extract unique and stable fingerprints efficiently and at scale.},
booktitle = {Proceedings of the 20th ACM Asia Conference on Computer and Communications Security},
pages = {1141–1157},
numpages = {17},
location = {
},
series = {ASIA CCS '25}
}

Older ArXiv Version

Hari Venugopalan, Zainul Abi Din, Jason Lowe-Power, Samuel T. King, and Zubair Shafiq. 2024. Centauri: Practical Rowhammer Fingerprinting. arXiv preprint arXiv:2307.00143, 2023. doi: 10.48550/arXiv.2307.00143.

@misc{venugopalan2023centauri,
      title={Centauri: Practical Rowhammer Fingerprinting}, 
      author={Hari Venugopalan and Kaustav Goswami and Zainul Abi Din and Jason Lowe-Power and Samuel T. King and Zubair Shafiq},
      year={2023},
      eprint={2307.00143},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}