JJ Wilson

Johannes Wilson, Mikael Asplund, Niklas Johansson

Formal verification is a challenging but important task for ensuring the security of cryptographic protocols. While modern protocol verification tools significantly reduce verification effort, modelling remains challenging to practitioners without a background in formal verification. In addition, transferring verification results to a concrete protocol implementation requires expert knowledge. In this paper, we present a novel language-first method for verification of trace properties using a domain-specific language for protocol implementations. We target the Tamarin prover for verification, and we prove that verified universal trace properties translate back to the implementation. We additionally integrate symbolic execution in order to analyse the memory safety of protocol implementations. We use our tool to implement and generate accurate models for a signed Diffie-Hellman protocol, and for the WireGuard VPN protocol. Our WireGuard implementation is interoperable with existing implementations when using our interpreter, and achieves acceptable performance. We formally prove our implementations secure using a combination of symbolic execution and verification of the generated Tamarin models.

Dobrik Georgiev, JJ Wilson, Davide Buffelli et al. · cambridge

Neural Algorithmic Reasoning (NAR) research has demonstrated that graph neural networks (GNNs) could learn to execute classical algorithms. However, most previous approaches have always used a recurrent architecture, where each iteration of the GNN matches an iteration of the algorithm. In this paper we study neurally solving algorithms from a different perspective: since the algorithm's solution is often an equilibrium, it is possible to find the solution directly by solving an equilibrium equation. Our approach requires no information on the ground-truth number of steps of the algorithm, both during train and test time. Furthermore, the proposed method improves the performance of GNNs on executing algorithms and is a step towards speeding up existing NAR models. Our empirical evidence, leveraging algorithms from the CLRS-30 benchmark, validates that one can train a network to solve algorithmic problems by directly finding the equilibrium. We discuss the practical implementation of such models and propose regularisations to improve the performance of these equilibrium reasoners.