MultiTagging: A Vulnerable Smart Contract Labeling and Evaluation Framework

Shikah J. Alsunaidi, Hamoud Aljamaan*, Mohammad Hammoudeh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Identifying vulnerabilities in Smart Contracts (SCs) is crucial, as they can lead to significant financial losses if exploited. Although various SC vulnerability identification methods exist, selecting the most effective approach remains challenging. This article examines these challenges and introduces solutions to enhance SC vulnerability identification. It introduces MultiTagging, a modular SC multi-labeling framework designed to overcome limitations in existing SC vulnerability identification approaches. MultiTagging automates SC vulnerability tagging by parsing analysis reports and mapping tool-specific tags to standardized labels, including SC Weakness Classification (SWC) codes and Decentralized Application Security Project (DASP) ranks. Its mapping strategy and the proposed vulnerability taxonomy resolve tool-level labeling inconsistencies, where different tools use distinct labels for identical vulnerabilities. The framework integrates an evaluation module to assess SC vulnerability identification methods. MultiTagging enables both tool-based and vote-based SC vulnerability labeling. To improve labeling accuracy, the article proposes Power-based voting, a method that systematically defines voter roles and voting thresholds for each vulnerability. MultiTagging is used to evaluate labeling across six tools: MAIAN, Mythril, Semgrep, Slither, Solhint, and VeriSmart. The results reveal high coverage for Mythril, Slither, and Solhint, which identified eight, seven, and six DASP classes, respectively. Tool performance varied, underscoring the impracticality of relying on a single tool to identify all vulnerability classes. A comparative evaluation of Power-based voting and two threshold-based methods—AtLeastOne and Majority voting—shows that while voting methods can increase vulnerability identification coverage, they may also reduce detection performance. Power-based voting proved more effective than pure threshold-based methods across all vulnerability classes.

Original languageEnglish
Article number4616
JournalElectronics (Switzerland)
Volume13
Issue number23
DOIs
StatePublished - Dec 2024

Bibliographical note

Publisher Copyright:
© 2024 by the authors.

Keywords

  • blockchain
  • data analysis
  • data annotation
  • smart contract
  • software security
  • vulnerability identification
  • vulnerability taxonomy

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'MultiTagging: A Vulnerable Smart Contract Labeling and Evaluation Framework'. Together they form a unique fingerprint.

Cite this