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Analyzing Popular Smart Contracts with Slither-Analyzer Tool

Summary

We analyzed 15 smart contracts from 3 popular Ethereum projects using the slither-analyzer tool by Trail of Bits. Over 270 detections were found, of which 10 were high-impact and high-confidence and other 17 were high-impact and medium-confidence. Taking into account that these contracts are deployed on the Ethereum mainnet, that they have not been hacked so far, and that they handle significant amounts of money in assets, we consider these detections to be false positives.

We conclude that a Slither detection by itself does not necessarily mean that a contract is insecure. This implies that, while useful, Slither does not replace an audit by a security specialist.

Feel free to contact our smart contract auditing service for a full security assessment of your smart contracts.

Introduction

In this article we study the vulnerabilities detected by Slither on popular smart contracts currently deployed and active on the Ethereum network. For this analysis, we focus on deployments made by some of these major DeFi projects: 1inch, Aave and Uniswap.

Considering the security audits that these projects have undergone in the last few years, as well as their frequent exposure to large numbers of users, we conduct our analysis assuming that these smart contracts are free of vulnerabilities. Under this assumption, our objective with this article is to understand the frequency and severity of false positives obtained with Slither.

Analysis

Analyzed results were classified according to their impact and confidence categories. We observe that, out of 270 potential vulnerabilities detected, 10 (~3.5%) were both high-impact and high-confidence, and other 17 detections were high-impact and medium-confidence.

Furthermore, we observe that these 27 high-impact detections are distributed among 11 out of the 15 analyzed contracts, which include at least 3 contracts for each of the parent projects analyzed (see Methodology section below for the addresses of the analyzed contracts).

Conclusion

With the analysis of these battle-tested smart contracts we conclude that a manual revision of detections produced by static analysis tools is necessary to rule out false positives. This is a part of the work we do when we perform our smart-contract security audits.

Methodology

For this study, we used the current version of Slither (v0.9.1), together with solc-select (v1.0.2) to automatically switch to the expected version of solc used in the compilation of each smart contract we analyzed. These tools were run on Ubuntu 20.04.4 LTS over the list of 15 deployed smart contracts that we summarize below.

Table 1: Analized Projects

 

Parent ProjectAnalysis TagAddress (ETH)
1inch1i-10xbAF9A5d4b0052359326A6CDAb54BABAa3a3A9643
1inch1i-20x111111125434b319222cdbf8c261674adb56f3ae
1inch1i-30xA0446D8804611944F1B527eCD37d7dcbE442caba
1inch1i-40x0F85A912448279111694F4Ba4F85dC641c54b594
1inch1i-50x119c71D3BbAC22029622cbaEc24854d3D32D2828
aaveaa-10x398eC7346DcD622eDc5ae82352F02bE94C62d119
aaveaa-20xEFFC18fC3b7eb8E676dac549E0c693ad50D1Ce31
aaveaa-30xEE56e2B3D491590B5b31738cC34d5232F378a8D5
aaveaa-40x7d2768dE32b0b80b7a3454c06BdAc94A69DDc7A9
uniswapun-10x5BA1e12693Dc8F9c48aAD8770482f4739bEeD696
uniswapun-20x1F98431c8aD98523631AE4a59f267346ea31F984
uniswapun-30xb27308f9F90D607463bb33eA1BeBb41C27CE5AB6
uniswapun-40xE592427A0AEce92De3Edee1F18E0157C05861564
uniswapun-50xC36442b4a4522E871399CD717aBDD847Ab11FE88
uniswapun-60xA5644E29708357803b5A882D272c41cC0dF92B34

In order to build this list, we reviewed the main contracts of each parent company and looked for smart contracts satisfying the following criteria:

  • Smart contracts that were verified on etherscan
  • Smart contracts that received transactions within the last month prior to this article

For each smart contract listed above, we used their deployment address to run the command slither <address> --show-ignored-findings --json <analysis_tag>.json, obtaining a .json file with the results of the static analysis performed by Slither. The inline parameter --show-ignored-findings is used to ensure that all uncommented code is analyzed by the tool.

We used the results in these .json files to analyze the detected issues in the Analysis section. For this analysis, we focused on the confidence and impact categorization of detectors provided by Slither, considering only potential security vulnerabilities and discarding detections deemed only as Informational or Optimization.

Table 2: Impact and Confidence classification of detectors considered in our analysis.

 

Impact \ ConfidenceHighMedium
High
Medium
Low
Informational
Optimization
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