Information-Combining Differential Fault Attacks on DEFAULT 📺
Differential fault analysis (DFA) is a very powerful attack vector on implementations of symmetric cryptography. Most countermeasures are applied at the implementation level. At ASIACRYPT 2021, Baksi et al. proposed a design strategy that aims to provide inherent cipher level resistance against DFA by using S-boxes with linear structures. They argue that in their instantiation, the block cipher DEFAULT, a DFA adversary can learn at most 64 of the 128 key bits, so the remaining brute-force complexity of 2^64 is impractical. In this paper, we show that a DFA adversary can combine information across rounds to recover the full key, invalidating their security claim. In particular, we observe that such ciphers exhibit large classes of equivalent keys that can be represented efficiently in normalized form using linear equations. We exploit this in combination with the specifics of DEFAULT's strong key schedule to recover the key using less than 100 faulty computation and negligible time complexity. Moreover, we show that even an idealized version of DEFAULT with independent round keys is vulnerable to our information-combining attacks based on normalized keys.
Throwing Boomerangs into Feistel Structures: Application to CLEFIA, WARP, LBlock, LBlock-s and TWINE
Automatic tools to search for boomerang distinguishers have seen significant advances over the past few years. However, most previous work has focused on ciphers based on a Substitution Permutation Network (SPN), while analyzing the Feistel structure is of great significance. Boukerrou et al. recently provided a theoretical framework to formulate the boomerang switch over multiple Feistel rounds, but they did not provide an automatic tool to find distinguishers. In this paper, by enhancing the recently proposed method by Hadipour et al., we provide an automatic tool to search for boomerang distinguishers and apply it to block ciphers following the Generalized Feistel Structure (GFS). Applying our tool to a wide range of GFS ciphers, we show that it significantly improves the best previous results on boomerang analysis. In particular, we improve the best previous boomerang distinguishers for 20 and 21 rounds of WARP by a factor of 238.28 and 236.56, respectively. Thanks to he effectiveness of our method, we can extend the boomerang distinguishers of WARP by two rounds and distinguish 23 rounds of this cipher from a random permutation. Applying our method to the internationally-standardized cipher CLEFIA, we achieve a 9-round boomerang distinguisher which improves the best previous boomerang distinguisher by one round. Based on this distinguisher, we build a key-recovery attack on 11 rounds of CLEFIA, which improves the best previous sandwich attack on this cipher by one round. We also apply our method to LBlock, LBlock-s, and TWINE and improve the best previous boomerang distinguisher of these ciphers.
Analyzing the Linear Keystream Biases in AEGIS 📺
AEGIS is one of the authenticated encryption designs selected for the final portfolio of the CAESAR competition. It combines the AES round function and simple Boolean operations to update its large state and extract a keystream to achieve an excellent software performance. In 2014, Minaud discovered slight biases in the keystream based on linear characteristics. For family member AEGIS-256, these could be exploited to undermine the confidentiality faster than generic attacks, but this still requires very large amounts of data. For final portfolio member AEGIS-128, these attacks are currently less efficient than generic attacks. We propose improved keystream approximations for the AEGIS family, but also prove upper bounds below 2−128 for the squared correlation contribution of any single suitable linear characteristic.