Enhancing Protocol Fuzzing via Diverse Seed Corpus Generation

Abstract

Protocol fuzzing is an effective technique for discovering vulnerabilities in protocol implementations. Although much progress has been made in optimizing input mutation, the initial seed inputs, which serve as the starting point for fuzzing, are still a critical factor in determining the effectiveness of subsequent fuzzing. Existing methods for seed corpus preparation mainly rely on captured network traffic, which suffers from limited diversity due to the biased message distributions present in real-world traffic. Protocol specifications encompass detailed information on diverse messages and thus provide a more comprehensive way for seed corpus preparation. However, these specifications are voluminous and not directly machine-readable. To address this challenge, we introduce PSG, which enhances protocol fuzzing by leveraging large language models (LLMs) to analyze protocol specifications for generating a high-quality seed corpus. First, PSG systematically reorganizes the protocol specification metadata into a structured knowledge base for effective LLM augmentation. Then, PSG employs a grammar-free method to generate target protocol messages and incorporates an iterative refinement process for better accuracy and efficiency. Our evaluation on 7 widely-used protocols and 13 implementations demonstrates that PSG can effectively generate diverse, protocol-compliant message inputs. Moreover, the generated seed corpus significantly improves the performance of state-of-the-art black-box and grey-box protocol fuzzers, achieving higher branch coverage and discovering more zero-day bugs.