Chat4seed: Semantic-Awareness Highly Structured Seed Generation for Fuzzing

Fuzzing, one of the most popular methods for enhancing software security and quality, relies heavily on the quality of its initial seed corpus to effectively uncover vulnerabilities. Traditional methods for generating initial seed corpora, whether crawl-based or generation-based, struggle with programs that handle highly structured formats with complex semantics, leading to low testing coverage and reduced fuzzer effectiveness. Although researchers have proposed leveraging Large Language Models (LLMs) to create high-quality seed corpora, current approaches are limited to text-based seed files, such as JavaScript code. To address the limitations, we propose Chat4Seed, a novel approach that extends the capabilities of LLMs to produce not only text but also highly-structured binary seed files. Chat4Seed leverages LLMs to extract and interpret the semantic constraints embedded within formatspecific branches of programs. While existing approaches focus solely on generating text-based seeds, Chat4Seed goes further by utilizing LLMs to generate functional library invocation code to produce binary seeds. It also employs binarylevel manipulation to handle unsupported corner cases, achieving robust seed generation for both text and binary formats. Our experiments and evaluations on 12 real-world programs demonstrate that after 48 hours of fuzz testing using AFL++, the seed corpus generated by Chat4Seed achieves an average increase in coverage of 28.78% compared to traditional crawl-based methods and 39.98% compared to generation-based methods. Additionally, it facilitates the discovery of 84.24% and 392.7% more crashes than seed corpus generated by traditional approaches, respectively, underscoring the potential of Chat4Seed to enhance the efficacy of fuzzing.