Abstract
Ethereum smart contracts are an innovation built on top of the blockchain technology, which provides a platform for automatically executing contracts in an anonymous, distributed, and trusted way. The problem is magnified by the fact that smart contracts, unlike ordinary programs, cannot be patched easily once deployed. It is important for smart contracts to be checked against potential vulnerabilities. In this work, we propose an alternative approach to automatically identify critical program paths (with multiple function calls including inter-contract function calls) in a smart contract, rank the paths according to their criticalness, discard them if they are infeasible or otherwise present them with user friendly warnings for user inspection. We identify paths which involve monetary transaction as critical paths, and prioritize those which potentially violate important properties. For scalability, symbolic execution techniques are only applied to top ranked critical paths. Our approach has been implemented in a tool called sCompile, which has been applied to 36,099 smart contracts. The experiment results show that sCompile is efficient, i.e., 5 s on average for one smart contract. Furthermore, we show that many known vulnerabilities can be captured if user inspects as few as 10 program paths generated by sCompile. Lastly, sCompile discovered 224 unknown vulnerabilities with a false positive rate of 15.4% before user inspection.
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Notes
- 1.
MAIAN sends a value of 256 wei to the contract deployed in the private blockchain network.
- 2.
Note that each opcode instruction in EVM is associated with some gas consumption which technically makes them money-related. Gas [7] is the cost of any transaction that can be utilized to measure actions on Ethereum platform. However, the gas consumption alone in most cases does not constitute vulnerabilities and therefore we do not consider them money-related. In Fig. 3, we visualize money-related nodes with black background (e.g., the node \(Node\_112\_162\) with a
statement
).
- 3.
We hide the names of the contracts as some of them are yet to be fixed.
- 4.
We have informed all developers whose contact info are available about the vulnerabilities in their contracts and several have confirmed the vulnerabilities and deployed new contracts to substitute the vulnerable ones. Some are yet to respond, although the balance in their contracts are typically small.
- 5.
There are about 80 people who tried the test. Most of the respondents however leave the test after the first question, which perhaps evidences the difficulty in analyzing smart contracts.
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Acknowledgement
This work is supported by the Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 1 grant, the Youth Innovation Promotion Association of the Chinese Academy of Sciences (YICAS) (Grant No. 2017151), the Young Elite Scientists Sponsorship Program by CAST (Grant No. 2017QNRC001), and the Blockchain Technology and Application Joint Laboratory, Guiyang Academy of Information Technology (Institute of Software Chinese Academy of Sciences Guiyang Branch).
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Chang, J., Gao, B., Xiao, H., Sun, J., Cai, Y., Yang, Z. (2019). sCompile: Critical Path Identification and Analysis for Smart Contracts. In: Ait-Ameur, Y., Qin, S. (eds) Formal Methods and Software Engineering. ICFEM 2019. Lecture Notes in Computer Science(), vol 11852. Springer, Cham. https://doi.org/10.1007/978-3-030-32409-4_18
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