  As blockchain platforms grow exponentially, millions of lines of smart
contract code are being deployed to manage extensive digital assets. However,
vulnerabilities in this mission-critical code have led to significant
exploitations and asset losses. Thorough automated security analysis of smart
contracts is thus imperative. This paper introduces LLM4Fuzz to optimize
automated smart contract security analysis by leveraging large language models
(LLMs) to intelligently guide and prioritize fuzzing campaigns. While
traditional fuzzing suffers from low efficiency in exploring the vast state
space, LLM4Fuzz employs LLMs to direct fuzzers towards high-value code regions
and input sequences more likely to trigger vulnerabilities. Additionally,
LLM4Fuzz can leverage LLMs to guide fuzzers based on user-defined invariants,
reducing blind exploration overhead. Evaluations of LLM4Fuzz on real-world DeFi
projects show substantial gains in efficiency, coverage, and vulnerability
detection compared to baseline fuzzing. LLM4Fuzz also uncovered five critical
vulnerabilities that can lead to a loss of more than $247k.
