dorsal/arxiv
View SchemaUncovering Failures in Cyber-Physical System State Transitions: A Fuzzing-Based Approach Applied to sUAS
| Authors | Theodore Chambers, Arturo Miguel Russell Bernal, Michael Vierhauser, Jane Cleland-Huang |
|---|---|
| Categories | |
| ArXiv ID | 2601.05449vv1 |
| URL | https://arxiv.org/abs/2601.05449 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
The increasing deployment of small Uncrewed Aerial Systems (sUAS) in diverse and often safety-critical environments demands rigorous validation of onboard decision logic under various conditions. In this paper, we present SaFUZZ, a state-aware fuzzing pipeline that validates core behavior associated with state transitions, automated failsafes, and human operator interactions in sUAS applications operating under various timing conditions and environmental disturbances. We create fuzzing specifications to detect behavioral deviations, and then dynamically generate associated Fault Trees to visualize states, modes, and environmental factors that contribute to the failure, thereby helping project stakeholders to analyze the failure and identify its root causes. We validated SaFUZZ against a real-world sUAS system and were able to identify several points of failure not previously detected by the system's development team. The fuzzing was conducted in a high-fidelity simulation environment, and outcomes were validated on physical sUAS in a real-world field testing setting. The findings from the study demonstrated SaFUZZ's ability to provide a practical and scalable approach to uncovering diverse state transition failures in a real-world sUAS application.
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"abstract": "The increasing deployment of small Uncrewed Aerial Systems (sUAS) in diverse and often safety-critical environments demands rigorous validation of onboard decision logic under various conditions. In this paper, we present SaFUZZ, a state-aware fuzzing pipeline that validates core behavior associated with state transitions, automated failsafes, and human operator interactions in sUAS applications operating under various timing conditions and environmental disturbances. We create fuzzing specifications to detect behavioral deviations, and then dynamically generate associated Fault Trees to visualize states, modes, and environmental factors that contribute to the failure, thereby helping project stakeholders to analyze the failure and identify its root causes. We validated SaFUZZ against a real-world sUAS system and were able to identify several points of failure not previously detected by the system\u0027s development team. The fuzzing was conducted in a high-fidelity simulation environment, and outcomes were validated on physical sUAS in a real-world field testing setting. The findings from the study demonstrated SaFUZZ\u0027s ability to provide a practical and scalable approach to uncovering diverse state transition failures in a real-world sUAS application.",
"arxiv_id": "2601.05449",
"authors": [
"Theodore Chambers",
"Arturo Miguel Russell Bernal",
"Michael Vierhauser",
"Jane Cleland-Huang"
],
"categories": [
"cs.SE"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Uncovering Failures in Cyber-Physical System State Transitions: A Fuzzing-Based Approach Applied to sUAS",
"url": "https://arxiv.org/abs/2601.05449",
"version": "v1"
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