SCARF is a tweakable block cipher dedicated to cache address randomization, proposed at the USENIX Security conference. It has a 10-bit block, 48-bit tweak, and 240-bit key. SCARF is aggressively optimized to meet the harsh latency constraints of cache address randomization, and uses a dedicated model for its security claim. The full version of SCARF has 8 rounds, and its designers claim security up to \(2^{40}\) queries and \(2^{80}\) computations. In this work we present a distinguisher against 6-round SCARF under the collision model with time and query complexity \(2^{30}\) , and a key-recovery attack against the full 8-round SCARF under the encryption-decryption model with \(2^{39}\) queries and time \(2^{76.2}\) . As part of the attack, we present a novel method to compute the minimal number of right pairs following a differential characteristic when the input pairs are restricted to a subspace of the domain of the primitive.

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Cryptanalysis of Full SCARF

  • Antonio Flórez-Gutiérrez,
  • Eran Lambooij,
  • Gaëtan Leurent,
  • Håvard Raddum,
  • Tyge Tiessen,
  • Michiel Verbauwhede

摘要

SCARF is a tweakable block cipher dedicated to cache address randomization, proposed at the USENIX Security conference. It has a 10-bit block, 48-bit tweak, and 240-bit key. SCARF is aggressively optimized to meet the harsh latency constraints of cache address randomization, and uses a dedicated model for its security claim. The full version of SCARF has 8 rounds, and its designers claim security up to \(2^{40}\) queries and \(2^{80}\) computations. In this work we present a distinguisher against 6-round SCARF under the collision model with time and query complexity \(2^{30}\) , and a key-recovery attack against the full 8-round SCARF under the encryption-decryption model with \(2^{39}\) queries and time \(2^{76.2}\) . As part of the attack, we present a novel method to compute the minimal number of right pairs following a differential characteristic when the input pairs are restricted to a subspace of the domain of the primitive.