Practical seed-recovery for the PCG Pseudo-Random Number Generator


  • Charles Bouillaguet Univ. Lille, CNRS, Centrale Lille, UMR 9189 - CRIStAL - Centre de Recherche en Informatique Signal et Automatique de Lille, F-59000 Lille, France
  • Florette Martinez Sorbonne Université, CNRS, LIP6, F-75005 Paris, France
  • Julia Sauvage Sorbonne Université, F-75005 Paris, France



Pseudo-random number generator, guess-and-determine attack, truncated congruential generator, euclidean lattices, closest vector problem, practical attack


The Permuted Congruential Generators (PCG) are popular conventional (non-cryptographic) pseudo-random generators designed in 2014. They are used by default in the NumPy scientific computing package. Even though they are not of cryptographic strength, their designer stated that predicting their output should nevertheless be "challenging".
In this article, we present a practical algorithm that recovers all the hidden parameters and reconstructs the successive internal states of the generator. This enables us to predict the next “random” numbers, and output the seeds of the generator. We have successfully executed the reconstruction algorithm using 512 bytes of challenge input; in the worst case, the process takes 20 000 CPU hours.
This reconstruction algorithm makes use of cryptanalytic techniques, both symmetric and lattice-based. In particular, the most computationally expensive part is a guessand-determine procedure that solves about 252 instances of the Closest Vector Problem on a very small lattice.







How to Cite

Practical seed-recovery for the PCG Pseudo-Random Number Generator. (2020). IACR Transactions on Symmetric Cryptology, 2020(3), 175-196.