Detecting Buggy Contracts via Smart Testing
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arXiv
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| Format: | Preprint |
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2024
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| author | Wang, Sally Junsong Yao, Jianan Pei, Kexin Takahashi, Hidedaki Yang, Junfeng |
| author_facet | Wang, Sally Junsong Yao, Jianan Pei, Kexin Takahashi, Hidedaki Yang, Junfeng |
| contents | Smart contracts are susceptible to critical vulnerabilities. Hybrid dynamic analyses, such as concolic execution assisted fuzzing and foundation model assisted fuzzing, have emerged as highly effective testing techniques for smart contract bug detection recently. This hybrid approach has shown initial promise in real-world benchmarks, but it still suffers from low scalability to find deep bugs buried in complex code patterns. We observe that performance bottlenecks of existing dynamic analyses and model hallucination are two main factors limiting the scalability of this hybrid approach in finding deep bugs.
To overcome the challenges, we design an interactive, self-deciding foundation model based system, called SmartSys, to support hybrid smart contract dynamic analyses. The key idea is to teach foundation models about performance bottlenecks of different dynamic analysis techniques, making it possible to forecast the right technique and generates effective fuzz targets that can reach deep, hidden bugs. To prune hallucinated, incorrect fuzz targets, SmartSys feeds foundation models with feedback from dynamic analysis during compilation and at runtime.
The interesting results of SmartSys include: i) discovering a smart contract protocol vulnerability that has escaped eleven tools and survived multiple audits for over a year; ii) improving coverage by up to 14.3\% on real-world benchmarks compared to the baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_04597 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Detecting Buggy Contracts via Smart Testing Wang, Sally Junsong Yao, Jianan Pei, Kexin Takahashi, Hidedaki Yang, Junfeng Software Engineering Machine Learning Programming Languages Smart contracts are susceptible to critical vulnerabilities. Hybrid dynamic analyses, such as concolic execution assisted fuzzing and foundation model assisted fuzzing, have emerged as highly effective testing techniques for smart contract bug detection recently. This hybrid approach has shown initial promise in real-world benchmarks, but it still suffers from low scalability to find deep bugs buried in complex code patterns. We observe that performance bottlenecks of existing dynamic analyses and model hallucination are two main factors limiting the scalability of this hybrid approach in finding deep bugs. To overcome the challenges, we design an interactive, self-deciding foundation model based system, called SmartSys, to support hybrid smart contract dynamic analyses. The key idea is to teach foundation models about performance bottlenecks of different dynamic analysis techniques, making it possible to forecast the right technique and generates effective fuzz targets that can reach deep, hidden bugs. To prune hallucinated, incorrect fuzz targets, SmartSys feeds foundation models with feedback from dynamic analysis during compilation and at runtime. The interesting results of SmartSys include: i) discovering a smart contract protocol vulnerability that has escaped eleven tools and survived multiple audits for over a year; ii) improving coverage by up to 14.3\% on real-world benchmarks compared to the baselines. |
| title | Detecting Buggy Contracts via Smart Testing |
| topic | Software Engineering Machine Learning Programming Languages |
| url | https://arxiv.org/abs/2409.04597 |