CoChat
Sign Up
Cited by 229Software Testing and Debugging TechniquesSoftware Engineering ResearchFormal Methods in Verification

The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.

Fuzz4All: Universal Fuzzing with Large Language Models

Open access
Xia, Chunqiu Steven, Paltenghi, Matteo, Le Tian, Jia, Pradel, Michael, Zhang, Lingming·2024·Proceedings of the IEEE/ACM 46th International Conference on Software Engineering
DOI 10.1145/3597503.3639121
Open this paper in CoChat — free accountOpen PDF

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.

Overview

Integrity signals

Registry

Crossref · 10.1145/3597503.3639121

Cites

47 of 47 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 42 references

Research works printed but not linked: 26

  1. Cornelius Aschermann, Tommaso Frassetto, Thorsten Holz, Patrick Jauernig, Ahmad-Reza Sadeghi, and Daniel Teuchert. 2019. NAUTILUS: Fishing for Deep Bugs with Grammars.. In NDSS.
  2. Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712 (2023). · arXiv
  3. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021). · arXiv
  4. Aakanksha Chowdhery Sharan Narang Jacob Devlin Maarten Bosma Gaurav Mishra Adam Roberts Paul Barham Hyung Won Chung Charles Sutton Sebastian Gehrmann Parker Schuh Kensen Shi Sasha Tsvyashchenko Joshua Maynez Abhishek Rao Parker Barnes Yi Tay Noam Shazeer Vinodkumar Prabhakaran Emily Reif Nan Du Ben Hutchinson Reiner Pope James Bradbury Jacob Austin Michael Isard Guy Gur-Ari Pengcheng Yin Toju Duke Anselm Levskaya Sanjay Ghemawat Sunipa Dev Henryk Michalewski Xavier Garcia Vedant Misra Kevin Robinson Liam Fedus Denny Zhou Daphne Ippolito David Luan Hyeontaek Lim Barret Zoph Alexander Spiridonov Ryan Sepassi David Dohan Shivani Agrawal Mark Omernick Andrew M. Dai Thanumalayan Sankaranarayana Pillai Marie Pellat Aitor Lewkowycz Erica Moreira Rewon Child Oleksandr Polozov Katherine Lee Zongwei Zhou Xuezhi Wang Brennan Saeta Mark Diaz Orhan Firat Michele Catasta Jason Wei Kathy Meier-Hellstern Douglas Eck Jeff Dean Slav Petrov and Noah Fiedel. 2022. PaLM: Scaling Language Modeling with Pathways. arXiv:2204.02311 [cs.CL] · arXiv
  5. Andrew W. Cross, Lev S. Bishop, John A. Smolin, and Jay M. Gambetta. 2017. Open Quantum Assembly Language. arXiv:1707.03429 [quant-ph] (July 2017). arXiv:1707.03429 [quant-ph] · arXiv
  6. Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, and Mike Lewis. 2022. Incoder: A generative model for code infilling and synthesis. arXiv preprint arXiv:2204.05999 (2022). · arXiv
  7. Christian Holler, Kim Herzig, and Andreas Zeller. 2012. Fuzzing with code fragments. In 21st USENIX Security Symposium (USENIX Security 12). 445--458.
  8. Ari Holtzman Jan Buys Li Du Maxwell Forbes and Yejin Choi. 2019. The Curious Case of Neural Text Degeneration. arXiv:1904.09751. · arXiv
  9. Bo Jiang, Xiaoyan Wang, Wing Kwong Chan, TH Tse, Na Li, Yongfeng Yin, and Zhenyu Zhang. 2020. Cudasmith: A fuzzer for CUDA compilers. In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 861--871.
  10. Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361 (2020). · arXiv
  11. Suyoung Lee, HyungSeok Han, Sang Kil Cha, and Sooel Son. 2020. Montage: A Neural Network Language {Model-Guided} {JavaScript} Engine Fuzzer. In 29th USENIX Security Symposium (USENIX Security 20). 2613--2630.
  12. Caroline Lemieux, Jeevana Priya Inala, Shuvendu K Lahiri, and Siddhartha Sen. 2023. CODAMOSA: Escaping Coverage Plateaus in Test Generation with Pretrained Large Language Models. In 45th International Conference on Software Engineering.
  13. Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, et al. 2023. StarCoder: may the source be with you! arXiv preprint arXiv:2305.06161 (2023). · arXiv
  14. Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021. Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. CoRR abs/2107.13586 (2021). arXiv:2107.13586 https://arxiv.org/abs/2107.13586 · arXiv
  15. Haoyang Ma. 2023. A Survey of Modern Compiler Fuzzing. arXiv preprint arXiv:2306.06884 (2023). · arXiv
  16. Henry B Mann and Donald R Whitney. 1947. On a test of whether one of two random variables is stochastically larger than the other. The annals of mathematical statistics (1947), 50--60.
  17. Pengyu Nie, Rahul Banerjee, Junyi Jessy Li, Raymond J. Mooney, and Milos Gligoric. 2023. Learning Deep Semantics for Test Completion. In 45th International Conference on Software Engineering.
  18. Max Schäfer Sarah Nadi Aryaz Eghbali and Frank Tip. 2023. Adaptive Test Generation Using a Large Language Model. arXiv:2302.06527 [cs.SE] · arXiv
  19. Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020. Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020). · arXiv
  20. Derek Tam, Rakesh R Menon, Mohit Bansal, Shashank Srivastava, and Colin Raffel. 2021. Improving and simplifying pattern exploiting training. arXiv preprint arXiv:2103.11955 (2021). · arXiv
  21. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).
  22. Vasudev Vikram, Caroline Lemieux, and Rohan Padhye. 2023. Can Large Language Models Write Good Property-Based Tests? arXiv preprint arXiv:2307.04346 (2023). · arXiv
  23. Chunqiu Steven Xia and Lingming Zhang. 2023. Keep the Conversation Going: Fixing 162 out of 337 bugs for $0.42 each using ChatGPT. arXiv preprint arXiv:2304.00385 (2023). · arXiv
  24. Zhiqiang Yuan Yiling Lou Mingwei Liu Shiji Ding Kaixin Wang Yixuan Chen and Xin Peng. 2023. No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation. arXiv:2305.04207 [cs.SE] · arXiv
  25. Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2022. Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022). · arXiv
  26. Daniel M. Ziegler Nisan Stiennon Jeffrey Wu Tom B. Brown Alec Radford Dario Amodei Paul Christiano and Geoffrey Irving. 2019. Fine-Tuning Language Models from Human Preferences. arXiv:1909.08593. · arXiv

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Xia, C. S., Paltenghi, M., Le Tian, J., Pradel, M., & Zhang, L.
Affiliations
  • University of Illinois Urbana-Champaign (US)
  • University of Stuttgart (DE)
  • UIUC, Urbana, Illinois, USA
ORCID
Venue
Proceedings of the IEEE/ACM 46th International Conference on Software Engineering
Publisher
ACM
Published
2024-04-12
Pages
1-13
ISSN
0270-5257 (ISSN-L)
DOI
10.1145/3597503.3639121
OpenAlex
W4394769544
Cited by
229
References
42
Citations by year
2026: 93 · 2025: 103 · 2024: 31
FWCI
61.97
Type
conference paper
Language
en
Open access
Licence
Locations
  • ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
National Science Foundation (CCF-2141474, CCF-2131943)
Topics
  • Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Formal Methods in Verification
Keywords
large language models, input generation, input mutation, compiler bugs, SMT solvers, software vulnerability detection, coverage-guided fuzzing, Qiskit

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review