  Fuzzing, a widely-used technique for bug detection, has seen advancements
through Large Language Models (LLMs). Despite their potential, LLMs face
specific challenges in fuzzing. In this paper, we identified five major
challenges of LLM-assisted fuzzing. To support our findings, we revisited the
most recent papers from top-tier conferences, confirming that these challenges
are widespread. As a remedy, we propose some actionable recommendations to help
improve applying LLM in Fuzzing and conduct preliminary evaluations on DBMS
fuzzing. The results demonstrate that our recommendations effectively address
the identified challenges.
