UTopia: Automatic Generation of Fuzz Driver using Unit Tests
Bokdeuk Jeong, Joonun Jang, Hayoon Yi, Jiin Moon, Junsik Kim, Intae Jeon, Taesoo Kim, WooChul Shim, Yong Ho Hwang
Abstract
Fuzzing is arguably the most practical approach for detecting security bugs in software, but a non-trivial extent of efforts is required for its adoption. To be effective, high-quality fuzz drivers should be first formulated with a proper sequence of APIs that can exhaustively explore the program states. To alleviate this burden, existing solutions attempt to generate fuzz drivers either by inferring the valid sequences of APIs from the consumer code (i.e., actual uses of APIs) or by directly extracting them from sample executions. Unfortunately, all existing approaches suffer from a common problem: the observed API sequences, either statically inferred or dynamically monitored, are intermingled with custom application logics. However, we observed that the unit tests are carefully crafted by the actual designer of the APIs to validate their proper usages, and importantly, it is a common practice to write the unit tests during their development (e.g., over 70% of popular GitHub projects).In this paper, we propose, UTopia, an open-source tool and analysis algorithm that can automatically synthesize effective fuzz drivers from existing unit tests with near-zero human involvement. To demonstrate its effectiveness, we applied UTopia to 55 open-source project libraries, including Tizen and Node.js, and automatically generated 5K fuzz drivers from 8K eligible unit tests. In addition, we executed the generated fuzzers for approximately 5 million per-core hours and discovered 123 bugs. More importantly, 2.4K of the generated fuzz drivers were adopted to the continuous integration process of the Tizen project, indicating the quality of the synthesized fuzz driver. The proposed tool and results are publicly available and maintained for a broader adoption among both researchers and practitioners.
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Install the CLIlune papers fulltext 5a884719-6174-4d39-945e-1bcff9f0c302Cited by top-tier papers13
- How Effective Are They? Exploring Large Language Model Based Fuzz Driver GenerationCen Zhang, Yaowen Zheng, Mingqiang Bai, Yeting Li et al.ISSTA 2024 · 27 citations
- Hopper: Interpretative Fuzzing for LibrariesPeng Chen, Yuxuan Xie, Yunlong Lyu, Yuxiao Wang et al.CCS 2023 · 23 citations
- Cottontail: Large Language Model-Driven Concolic Execution for Highly Structured Test Input GenerationHaoxin Tu, Seongmin Lee, Yuxian Li, Peng Chen et al.S&P 2026 · 22 citations
- Atlas: Automating Cross-Language Fuzzing on Android Closed-Source LibrariesHao Xiong, Qinming Dai, Rui Chang, Mingran Qiu et al.ISSTA 2024 · 6 citations
- FRIES: Fuzzing Rust Library Interactions via Efficient Ecosystem-Guided Target GenerationXizhe Yin, Yang Feng, Qingkai Shi, Zixi Liu et al.ISSTA 2024 · 6 citations
Builds on11
- Coverage-based Greybox Fuzzing as Markov ChainMarcel Böhme, Van-Thuan Pham, Abhik RoychoudhuryCCS 2016 · 1,026 citations
- VUzzer: Application-aware Evolutionary FuzzingSanjay Rawat, Vivek Jain, Ashish Kumar, Lucian Cojocar et al.NDSS 2017 · 700 citations
- Angora: Efficient Fuzzing by Principled SearchPeng Chen, Hao ChenS&P 2018 · 616 citations
- CollAFL: Path Sensitive FuzzingShuitao Gan, Chao Zhang, Xiaojun Qin, Xuwen Tu et al.S&P 2018 · 426 citations
- REDQUEEN: Fuzzing with Input-to-State CorrespondenceCornelius Aschermann, Sergej Schumilo, Tim Blazytko, Robert Gawlik et al.NDSS 2019 · 413 citations
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