Understanding Gaps in LLM Pipelines Towards Scalable Fuzzing Harness Generation: An Empirical Study and Enhancement

Large language model (LLM)-based techniques have achieved notable progress in fuzz harness generation. However, applying them to arbitrary functions \textit{at scale} remains difficult---generated harnesses often fail to compile or, worse, compile but remain logically ineffective. What factors drive