Automated Construction of High-Quality Initial Seed Corpus for Network Protocol Fuzzing

Abstract

Network protocols are foundational to modern communication systems, making vulnerability discovery critical. Stateful protocol fuzzers have been widely adopted due to their high efficiency and low false positives. However, they heavily depend on the quality of the initial seed corpus. Unfortunately, these seeds are typically handcrafted, limited in quantity, and often exercise only shallow protocol behaviors.In this paper, we present AutoSeeder, the first automated framework for constructing high-quality initial seed corpus tailored to stateful protocol fuzzing. Its core idea is to combine lightweight analysis of protocol source code with large language models to get state-handling logic and guide the generation of syntactically valid initial seeds that can reach deeper protocol states. Evaluation on twelve widely-used protocols shows that AutoSeeder can quickly construct a high-quality initial seed corpus, compared with four baselines, resulting in average improvements of 51.95% in state coverage, 152.6% in state transition coverage and 42.51% in code coverage. In addition, AutoSeeder is compatible with three state representation schemes to enhance the performance of the base fuzzers, including AFLNET, StateAFL, and NSFuzz. When integrated with AFLNET, AutoSeeder also discovers seven previously unknown bugs.