Fuzzing has become a widely adopted technique for vulnerability discovery, yet it remains ineffective for structured-input programs due to strict syntactic constraints and limited semantic awareness. Traditional greybox fuzzers rely on mutation-based strategies and coarse-grained coverage feedback, which often fail to generate valid inputs and explore deep execution paths. Recent advances in large language models (LLMs) have shown promise in improving input generation, but existing approaches primarily focus on seed generation and largely overlook the effective use of runtime feedback. In this paper, we propose SDLLMFuzz, a dynamic-static LLM-assisted greybox fuzzing framework for structured-input programs. Our approach integrates LLM-based structure-aware seed generation with static crash analysis, forming a unified feedback loop that iteratively refines test inputs. Specifically, we leverage LLMs to generate syntactically valid and semantically diverse inputs, while extracting rich semantic information from crash artifacts (e.g., core dumps and execution traces) to guide subsequent input generation. This dynamic-static feedback mechanism enables more efficient exploration of complex program behaviors. We evaluate SDLLMFuzz on the Magma benchmark across multiple structured-input programs, including libxml2, libpng, and libsndfile. Experimental results show that SDLLMFuzz significantly outperforms traditional greybox fuzzers and LLM-assisted baselines in terms of bug discovery and time-to-bug. These results demonstrate that combining semantic input generation with feedback-driven refinement is an effective direction for improving fuzzing performance on structured-input programs.

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