DSPFA: Generalized Persistent Fault Analysis based on Distributional Similarity
JiaYu Zhang, Shihui Zheng, Xinming Zhu, Yuehua Chen, Changhai Ou · IACR Transactions on Cryptographic Hardware and Embedded Systems · 2026
Under a more practical fault model assumption, namely with no prior knowledge of the fault, conducting ciphertext-only attacks becomes challenging. In the multiple-byte fault setting, the time complexity of existing Persistent Fault Analyses (PFAs) increases polynomially with the the number of fault values (N f) in the S-box. Furthermore, these methods are either inapplicable to the single-byte fault setting or require a large number of ciphertexts to achieve a success rate close to 1 in such a setting. To address these limitations, we propose a new persistent-fault-based analysis method called DSPFA. It exploits the similarity between the statistical distributions of two ciphertext bytes to deduce the difference between the corresponding bytes of the last subkey. Three distinct similarity measurement methods are utilized to construct three types of distinguishers for generating key candidates. DSPFA applies to both settings. More importantly, its time complexity is constant—specifically, O(212) similarity computations—independent of N f . Moreover, when N f is large, our method simultaneously reduces both the number of key candidates and the required amount of ciphertext, thereby further lowering the cost of the unique key recovery process. DSPFA also remains efficient against AES implementations without S-box and against certain Feistel ciphers. Finally, practical experiments conducted on an FPGA confirm the feasibility of instruction modification and validate the effectiveness of our analysis.