Fault injection attacks on finite field arithmetic implementations
*Aabha Amey PatilCorresponding authoraabha.patil@slrtce.inDepartment of Information TechnologyShree L. R. Tiwari College of EngineeringThane, Maharashtra, 401107, IndiaView full profile → , Nilesh Jagdish Visputevisputenilesh47@gmail.comDepartment of Artificial Intelligence and Data ScienceVidyavardhini’s College of Engineering & TechnologyVasai, Maharashtra, 401202, IndiaView full profile → , Uma Bhavin Goradiyauma.goradiya@slrtce.inDepartment of Computer EngineeringShree L. R. Tiwari College of EngineeringThane, Maharashtra, 401107, IndiaView full profile → , Prachi Wagdeprachi.wagde@sbmp.ac.inDepartment of Artificial Intelligence & Machine LearningShri Bhaghubhai Mafatlal Polytechnic and College of EngineeringVille Parle, Mumbai, Maharashtra, 400056, IndiaView full profile → , Swapna Nikhil Ambekarswapna.ambekar@sbmp.ac.inDepartment of Information TechnologyShri Bhaghubhai Mafatlal Polytechnic and College of EngineeringVille Parle, Mumbai, Maharashtra, 400056, IndiaView full profile → , Saurav Vermasaurav.verma@nmims.eduDepartment of Information TechnologyMukesh Patel School of Technology Management & EngineeringMumbai, Maharashtra, 400056, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2026
- Published Online:
- 31 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-3041
- Pages:
- 4169–4178
Abstract
Fault injection attacks are a major threat on the reliability and security of cryptographic systems using finite field arithmetic. This paper explores how arithmetic processes like addition, multiplication and inversion in finite fields are affected by deliberate errors. The paper examines the different fault injection methods such as voltage glitching, clock manipulation, and laser-based fault injection methods to discuss its usefulness in interference with computations. An in-depth analysis of vulnerabilities points to the short-term and long-term faults that might cause leakage of information and key recovery in cryptographic implementations. Also, attack methods, common fault, on symmetric and asymmetric schemes are discussed. Experimental evaluation reveals that there is quantifiable deterioration in computational accuracy and there is higher vulnerability to side-channeling. The results provide an insight into the importance of effective fault detection and mitigation measures to increase the resiliency of cryptographic hardware and software systems.
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References
[1] X. Hu and Z. Qin, “A secure architecture for modular division over a prime field against fault injection attacks,” Appl. Sci., vol. 10, no. 5, Art. no. 1700 (2020), doi: 10.3390/app10051700.
[2] A. Gangolli, Q. H. Mahmoud, and A. Azim, “A systematic review of fault injection attacks on IoT systems,” Electronics, vol. 11, no. 13, Art. no. 2023 (2022), doi: 10.3390/electronics11132023
[3] R. Sheikh and R. Cammarota, “Improving performance and mitigating fault attacks using value prediction,” Cryptography, vol. 2, no. 4, Art. no. 27 (2018), doi: 10.3390/cryptography2040027.
[4] H. Jiang, X. Zhu, and J. Han, “Instruction-fetching attack and practice in collision fault attack on AES,” Symmetry, vol. 14, no. 10, Art. no. 2201 (2022), doi: 10.3390/sym14102201.
[5] H. Li, K. Qiao, Y. Xu, C. Ou, and A. Wang, “General extensions and improvements of algebraic persistent fault analysis,” Cryptography, vol. 9, no. 2, Art. no. 30 (2025), doi: 10.3390/cryptography9020030.
[6] H. Mestiri, I. Barraj, M. Bedoui, and M. Machhout, “An ASCON AOP-SystemC environment for security fault analysis,” Symmetry, vol. 16, no. 3, Art. no. 348 (2024), doi: 10.3390/sym16030348.
[7] S. Narasimhan, M. J. Ellis, and N. H. El-Farra, “Detection of multiplicative false data injection cyberattacks on process control systems via randomized control mode switching,” Processes, vol. 12, no. 2, Art. no. 327 (2024), doi: 10.3390/pr12020327.
[8] M. I. Huilgol and S. Asok, “PBIB-designs with constant block sums arising from different graph parameters,” J. Discrete Math. Sci. Cryptogr., vol. 28, no. 7, pp. 2697–2721 (2025), doi: 10.47974/JDMSC-2064.
[9] Z. Ahmed, M. Nasir, D. M. Alsekait, M. Z. H. Shah, D. S. AbdElminaam, and F. Ahmad, “Control conditions for equal power sharing in multi-area power systems for resilience against false data injection attacks,” Energies, vol. 17, no. 22, Art. no. 5757 (2024), doi: 10.3390/en17225757.
[10] H. P. Morgan, N. Dimitrov, and P. Laurent, “A systematic review of graph-theoretic approaches to blockchain consensus mechanisms: Methods, architectures, and future research directions,” Int. J. Recent Adv. Eng. Technol., vol. 14, no. 2, pp. 182–190 (2025).
[11] K. Mahmoud and N. Nicolici, “ALPRI-FI: A framework for early assessment of hardware fault resiliency of DNN accelerators,” Electronics, vol. 13, no. 16, Art. no. 3243 (2024), doi: 10.3390/electronics13163243.
[12] R. C. Poonia, R. Almakki, A. K. J. Saudagar, A. Altameem, and M. Albathan, “An optimization approach for real-time object detection in IoT devices through edge computing and deep learning,” J. Inf. Optim. Sci., vol. 45, no. 5, pp. 1465–1475 (2024), doi: 10.47974/JIOS-1768.
[13] R. Gangarde, A. Julian, M. S. Murali Dhar, G. I. Mary, M. Rele, and M. Vaithianathan, “Applications of partial differential equations and Green’s functions in climate change modeling,” J. Interdiscip. Math., vol. 28, no. 6, pp. 2079–2088 (2025), doi: 10.47974/JIM-2349.
