Neuro-Symbolic Execution: Augmenting Symbolic Execution with Neural Constraints

Abstract

Symbolic execution is a powerful technique for program analysis. However, it has many limitations in practical applicability: the path explosion problem encumbers scalability, the need for language-specific implementation, the inability to handle complex dependencies, and the limited expressiveness of theories supported by underlying satisfiability checkers. Often, relationships between variables of interest are not expressible directly as purely symbolic constraints. To this end, we present a new approach-neuro-symbolic execution-which learns an approximation of the relationship between program values of interest, as a neural network. We develop a procedure for checking satisfiability of mixed constraints, involving both symbolic expressions and neural representations. We implement our new approach in a tool called NEUEX as an extension of KLEE, a state-of-the-art dynamic symbolic execution engine. NEUEX finds 33 exploits in a benchmark of 7 programs within 12 hours. This is an improvement in the bug finding efficacy of 94% over vanilla KLEE. We show that this new approach drives execution down difficult paths on which KLEE and other DSE extensions get stuck, eliminating limitations of purely SMT-based techniques.