QARMAv2, a family of lightweight block ciphers introduced in ToSC 2023, is an evolution of the original QARMA design, specifically constructed to accommodate more extended tweak values while simultaneously enhancing security measures. In this paper, for the first time, we present differential fault analysis (DFA) of all the QARMAv2 variants by introducing an approach to utilize the fault propagation patterns at the nibble level, with the goal of identifying relevant faulty ciphertexts and vulnerable fault positions. Introducing six random nibble faults strategically into the (r– 1)-th and (r– 2)-th backward rounds of ther-round QARMAv2-64 significantly reduces the secret key space from 2128to 232. Additionally, when targeting QARMAv2-128-128, it demands the introduction of six random nibble faults to effectively reduce the secret key space from 2128to a remarkably reduced 224. To conclude, we also explore the potential extension of our methods to conduct DFA on other versions of QARMAv2. To the best of our knowledge, this marks the first instance of a differential fault attack targeting the QARMAv2 tweakable block cipher family, signifying an important direction in cryptographic analysis.

The ChaCha cipher holds significance due to its widespread use in real-world applications, which is crucial in ensuring secure communication protocols such as TLS and SSH. The cryptanalysis of ChaCha involves a differential-linear attack which exploits the idea of Probabilistic Neutral Bits (PNBs). For a long period, researchers predominantly focused on incorporating single-bit differences at the beginning of differential-linear distinguishers for devising key-recovery attacks on ChaCha. Notably, at ToSC 2023, Belliniet al. introduced an innovative approach: a differential-linear distinguisher spanning five rounds, which takes into account 2-bit differences at the beginning. The aforementioned 5-round distinguisher integrated with the PNB framework, resulting in an enhanced key recovery attack specifically tailored for a 7-round ChaCha cipher. In this paper, first, we revisit the work of Belliniet al. and show that their 7-round key recovery attacks on ChaCha are impractical due to insufficient data. Furthermore, upon a thorough reassessment of the syncopation technique outlined in Wanget al.’s paper, we observe that introducing specific conditions in the computation of backward bias amplifies the data complexity. In response to this hurdle, we introduce a novel technique to effectively leverage rejected data in the backward bias calculation with conditions. Subsequently, we formulate an adjusted data complexity formula incorporating all backward biases for the PNB-based attack approach. Second, we present a strategic data reduction technique to reduce the total data required for backward computation in each guess of non- PNB bits, consequently yielding a notable improvement in time complexity analysis. For the first time since 2008, our analysis reveals an important advancement in backward computation, reducing the number of non-PNB bit guesses and decreasing the amount of data required for each non-PNB bit guess during backward computation. These enhancements significantly elevate the effectiveness of PNB-based key recovery attacks. Finally, utilizing the aforementioned ideas, we propose an enhanced framework for a key recovery attack, specifically formalized for round-reduced ChaCha. Using novel techniques, our approach successfully breaks seven rounds of ChaCha, achieving a data complexity of 2101.15and a time complexity of 2192.15. Along with that, we have successfully presented our improved key recovery attack on ChaCha7.5⊕(7.5 rounds of ChaCha without the last xor and left rotation) with data and time complexity as 2101.14, and 2230.58, respectively.