# Detecting Critical Bugs in SMT Solvers Using Blackbox Mutational Fuzzing

arXiv: https://arxiv.org/abs/2004.05934 · HTML: https://mlsanity.com/paper/2004.05934/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [AlgoVeri: An Aligned Benchmark for Verified Code Generation on Classical Algorithms](https://mlsanity.com/paper/2602.09464/) (2026)
- [Agentic Verification of Software Systems](https://mlsanity.com/paper/2511.17330/) (2025)
- [Once4All: Skeleton-Guided SMT Solver Fuzzing with LLM-Synthesized Generators](https://mlsanity.com/paper/2508.20340/) (2025)
- [LeanCSP: A Framework for Certifying Constraint Reformulation and Solving in Lean](https://mlsanity.com/paper/2607.28459/) (2026)

## Abstract (arXiv)

Formal methods use SMT solvers extensively for deciding formula satisfiability, for instance, in software verification, systematic test generation, and program synthesis. However, due to their complex implementations, solvers may contain critical bugs that lead to unsound results. Given the wide applicability of solvers in software reliability, relying on such unsound results may have detrimental consequences. In this paper, we present STORM, a novel blackbox mutational fuzzing technique for detecting critical bugs in SMT solvers. We run our fuzzer on seven mature solvers and find 29 previously unknown critical bugs. STORM is already being used in testing new features of popular solvers before deployment.