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About this paper
Yiran Cheng, Hong Jin Kang, Lwin Khin Shar, Chaopeng Dong, Zhiqiang Shi, Shichao Lv, Limin Sun
This paper critically assesses the current state of Large Language Model (LLM)-driven fuzz testing (LLM4Fuzz), highlighting pervasive reliability challenges such as low validity rates for generated drivers and test inputs. It provides a strategic vision and proposes concrete research directions to address these bottlenecks, aiming to make LLM4Fuzz a dependable and widely adoptable security testing technology.
Key insights
- Reliability is the paramount challenge preventing the widespread practical adoption of LLM-driven fuzz testing, outweighing current advancements in automation and generation capabilities.
- A multi-faceted research approach integrating traditional software engineering techniques (e.g., program analysis, formal methods) with advanced LLM capabilities is essential to overcome current reliability limitations.
- Establishing clear, quantitative metrics for evaluating driver validity and seed quality is crucial for guiding future research, benchmarking progress, and ensuring the development of truly dependable LLM4Fuzz tools.
Summary by alphaXiv.
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