Fuzz testing is an effective and scalable technique to perform software security assessments. Yet, contemporary fuzzers fall short of thoroughly testing applications with a high degree of control-flow diversity, such as firewalls and network packet analyzers. In this paper, we demonstrate how static program analysis can guide fuzzing by augmenting existing program models maintained by the fuzzer. Based on the insight that code patterns reflect the data format of inputs processed by a program, we automatically construct an input dictionary by statically analyzing program control and data flow. Our analysis is performed before fuzzing commences, and the input dictionary is supplied to an off-the-shelf fuzzer to influence input generation. Evaluations show that our technique not only increases test coverage by 10–15