AI RESEARCH
LLAMAFUZZ: Large Language Model Enhanced Greybox Fuzzing
arXiv CS.AI
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ArXi:2406.07714v4 Announce Type: replace-cross 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 generate new valid inputs.