LLAMAFUZZ: Large Language Model Enhanced Greybox Fuzzing

arXiv:2406.07714v1 Announce Type: new
Abstract: Greybox fuzzing has achieved success in revealing bugs and vulnerabilities in programs. However, randomized mutation strategies have limited the fuzzer's performance on structured data. Specialized fuzzers can handle complex structured data, but require additional efforts in grammar and suffer from low throughput.
In this paper, we explore the potential of utilizing the Large Language Model to enhance greybox fuzzing for structured data. We utilize the pre-trained knowledge of LLM about data conversion and format to …

arxiv bugs can cs.ai cs.cr cs.se data fuzzer fuzzing language large large language model low performance strategies structured data vulnerabilities