Cryptanalysis on GPUs with the Cube Attack: Design, Optimization and Performances Gains

The cube attack is a flexible cryptanalysis technique, with a simple and fascinating theoretical implant. It combines offline exhaustive searches over selected tweakable public/IV bits (the sides of the “cube“), with an online key-recovery phase. Although virtually applicable to any cipher, and generally praised by the research community, the real potential of the attack is still in question, and no implementation so far succeeded in breaking a real-world strong cipher. In this paper, we present, validate and analyze the first thorough implementation of the cube attack on a GPU cluster. The framework is conceived so as to be usable out-of-the-box for any cipher featuring up to 128-bit key and IV, and easily adaptable to larger key/IV, at just the cost of some fine (performance) tuning, mostly related to memory allocation. As a test case, we consider previous state-of-the-art results against a reduced-round version of a well-known cipher (Trivium). We evaluate the computational speedup with respect to a CPU-parallel benchmark, the performance dependence on system parameters and GPU architectures (Nvidia Kepler vs Nvidia Pascal), and the scalability of our solution on multi-GPU systems. All design choices are carefully described, and their respective advantages and drawbacks are discussed. By exhibiting the benefits of a complete GPU-tailored implementation of the cube attack, we provide novel and strong elements in support of the general feasibility of the attack, thus paving the way for future work in the area.