Personalized Fuzzing: A Case Study with the FANDANGO Fuzzer on a GNSS Module (Short Paper)

Fuzzing is a widely used technique for uncovering vulnerabilities in software systems, but traditional fuzzers often struggle with generating valid and meaningful test cases for complex input formats. Grammar-based fuzzers address this issue by ensuring syntactic correctness, but they frequently lack fine-grained control over generated inputs to trigger specific behaviors. In this paper, we demonstrate the flexibility and effectiveness of FANDANGO,a state-of-the-art grammar-based fuzzer that incorporates constraint solving to produce 100% valid inputs while also guiding the generation process toward desired edge cases. Using a GNSS (Global Navigation Satellite System) module as a case study, we showcase how FANDANGO enables the specification of constraints to explore the module's behavior. Our experiments highlight FANDANGO's ability to generate targeted test cases that expose potential weaknesses. This study reinforces the practical applicability of constraint-guided grammar fuzzing in security testing and reliability analysis.