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.

Program reduction is a widely adopted, indispensable technique for debugging language implementations such as compilers and interpreters. Given a program 𝑃 and a bug triggered by 𝑃, a program reducer can produce a minimized program 𝑃∗ that is derived from 𝑃 and still triggers the same bug. Perses is one of the state-of-the-art program reducers. It leverages the syntax of 𝑃 to guide the reduction process for efficiency and effectiveness. It is language-agnostic as its reduction algorithm is independent of any language-specific syntax. Conceptually to support a new language, Perses only needs the context-free grammar 𝐺 of the language; in practice, it is not easy. One needs to first manually transform 𝐺 into a special grammar form PNF with a tool provided by Perses, second manually change the code base of Perses to integrate the new language, and lastly build a binary of Perses.