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
摘要
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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引用它的顶会 Paper13
- How Effective Are They? Exploring Large Language Model Based Fuzz Driver GenerationCen Zhang, Yaowen Zheng, Mingqiang Bai, Yeting Li 等ISSTA 2024 · 被引用 27 次
- Hopper: Interpretative Fuzzing for LibrariesPeng Chen, Yuxuan Xie, Yunlong Lyu, Yuxiao Wang 等CCS 2023 · 被引用 23 次
- Cottontail: Large Language Model-Driven Concolic Execution for Highly Structured Test Input GenerationHaoxin Tu, Seongmin Lee, Yuxian Li, Peng Chen 等S&P 2026 · 被引用 22 次
- Atlas: Automating Cross-Language Fuzzing on Android Closed-Source LibrariesHao Xiong, Qinming Dai, Rui Chang, Mingran Qiu 等ISSTA 2024 · 被引用 6 次
- FRIES: Fuzzing Rust Library Interactions via Efficient Ecosystem-Guided Target GenerationXizhe Yin, Yang Feng, Qingkai Shi, Zixi Liu 等ISSTA 2024 · 被引用 6 次
它引用的顶会 Paper11
- Coverage-based Greybox Fuzzing as Markov ChainMarcel Böhme, Van-Thuan Pham, Abhik RoychoudhuryCCS 2016 · 被引用 1,026 次
- VUzzer: Application-aware Evolutionary FuzzingSanjay Rawat, Vivek Jain, Ashish Kumar, Lucian Cojocar 等NDSS 2017 · 被引用 700 次
- Angora: Efficient Fuzzing by Principled SearchPeng Chen, Hao ChenS&P 2018 · 被引用 616 次
- CollAFL: Path Sensitive FuzzingShuitao Gan, Chao Zhang, Xiaojun Qin, Xuwen Tu 等S&P 2018 · 被引用 426 次
- REDQUEEN: Fuzzing with Input-to-State CorrespondenceCornelius Aschermann, Sergej Schumilo, Tim Blazytko, Robert Gawlik 等NDSS 2019 · 被引用 413 次
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