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 for1 Input Language Specification Grammars + Constraints mats. Grammar-based fuzzers address this issue by ensuring syntactic correctness, but they frequently lack ne-grained control over generated inputs to trigger speci c behaviors. In this paper, we demonstrate the exibility and e ectiveness 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 speci cation of constraints to explore the module’sbehavior.OurexperimentshighlightFANDANGO’sability 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.