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.
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.