Fuzzing AI Systems: Foundations, Techniques, and Open Challenges

<jats:p>Artificial Intelligence (AI)-enabled systems now appear in settings where failures can affect software quality, safety, and security, including autonomous driving, software engineering tools, and large language model services. Fuzzing offers a practical way to stress these systems by generating or mutating test inputs, but the resulting research is scattered across different targets, techniques, oracle designs, and failure definitions. This survey synthesizes 125 primary studies and organizes the literature using a taxonomy of testing targets, system layers, technique families, input-generation and mutation strategies, oracles, and failure types. Our analysis shows rapid but uneven growth, strong target-dependence in technique design, persistent oracle-construction challenges, failures beyond crashes, and the need for reproducible benchmarks and cross-layer evaluation.</jats:p>