Fuzz4ALL: Universal Fuzzing with Large Language Models

Chunqiu Steven Xia, Matteo Paltenghi, Jia Le Tian, Michael Pradel, Lingming Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Fuzzing has achieved tremendous success in discovering bugs and vulnerabilities in various software systems. Systems under test (SUTs) that take in programming or formal language as inputs, e.g., compilers, runtime engines, constraint solvers, and software libraries with accessible APIs, are especially important as they are fundamental building blocks of software development. However, existing fuzzers for such systems often target a specific language, and thus cannot be easily applied to other languages or even other versions of the same language. Moreover, the inputs generated by existing fuzzers are often limited to specific features of the input language, and thus can hardly reveal bugs related to other or new features. This paper presents Fuzz4ALL, the first fuzzer that is universal in the sense that it can target many different input languages and many different features of these languages. The key idea behind Fuzz4ALL is to leverage large language models (LLMs) as an input generation and mutation engine, which enables the approach to produce diverse and realistic inputs for any practically relevant language. To realize this potential, we present a novel autoprompting technique, which creates LLM prompts that are well- suited for fuzzing, and a novel LLM-powered fuzzing loop, which iteratively updates the prompt to create new fuzzing inputs. We evaluate Fuzz4ALL on nine systems under test that take in six different languages (C, C++, Go, SMT2, Java, and Python) as inputs. The evaluation shows, across all six languages, that universal fuzzing achieves higher coverage than existing, language-specific fuzzers. Furthermore, Fuzz4ALL has identified 98 bugs in widely used systems, such as GCC, Clang, Z3, CVC5, OpenJDK, and the Qiskit quantum computing platform, with 64 bugs already confirmed by developers as previously unknown.

Original languageEnglish (US)
Title of host publicationProceedings - 2024 ACM/IEEE 44th International Conference on Software Engineering, ICSE 2024
PublisherIEEE Computer Society
Pages1547-1559
Number of pages13
ISBN (Electronic)9798400702174
DOIs
StatePublished - 2024
Event44th ACM/IEEE International Conference on Software Engineering, ICSE 2024 - Lisbon, Portugal
Duration: Apr 14 2024Apr 20 2024

Publication series

NameProceedings - International Conference on Software Engineering
ISSN (Print)0270-5257

Conference

Conference44th ACM/IEEE International Conference on Software Engineering, ICSE 2024
Country/TerritoryPortugal
CityLisbon
Period4/14/244/20/24

Keywords

  • fuzzing
  • large language models
  • software engineering

ASJC Scopus subject areas

  • Software

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