Fuzzing the Boundary between Models and Code in Hybrid AI-Enabled Systems

Abstract

Deep learning (DL) techniques are increasingly integrated into traditional software systems, giving rise to hybrid AI-enabled systems that combine neural models with program logic. While these systems exhibit remarkable capabilities, their complex and heterogeneous architectures pose significant challenges for reliability and testing, particularly in safety-critical domains such as autonomous driving. Existing testing approaches either target traditional code or isolate neural networks, overlooking failures arising from their interactions. In this paper, we present Neude, a lightweight and extensible coverage-guided fuzzing framework specifically designed for hybrid AI-enabled systems. Unlike existing tools, Neude combines observations of program execution and neural model coverage to guide input mutations toward unexplored state spaces, enabling systematic testing of the entire hybrid system. Moreover, Neude employs domain-aware mutation operators coupled with metamorphic relations, allowing automated bug detection without manual assertions. We evaluate Neude on Pylot, a complex autonomous driving system with tightly coupled neural and program components. Experimental results show that Neude uncovers diverse errors, and further analysis reveals how model uncertainty propagates through deterministic program logic to trigger downstream module failures. Our findings highlight the fragility of current hybrid architectures, calling for a paradigm shift from model-centric testing to system-centric quality assurance that accounts for the intricate interplay between neural and procedural components.