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Detecting Cross-Function Reentrancy from EVM Traces

Authors Semia Guesmi , Carla Piazza , Andrea Gasparetto , Matteo Rizzo , Sabina Rossi

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Subject Classification

ACM Subject Classification
  • Security and privacy → Software and application security
Keywords
  • Blockchain
  • smart contract
  • Reentrancy detection
  • EVM
  • design Patterns
  • logic rules

Metrics

Abstract

Reentrancy remains one of the most critical vulnerabilities affecting Ethereum smart contracts. While many existing analysis tools focus on detecting classical single-function reentrancy, more complex forms such as cross-function reentrancy are harder to identify because they depend on execution semantics and interactions between multiple functions. In this work, we study reentrancy at the level of Ethereum Virtual Machine (EVM) execution traces. We extend the TxSpector framework with new Datalog-based detection rules designed to capture cross-function reentrancy patterns. To support this analysis, we also modernize the trace extraction component by adapting it to recent versions of the Ethereum client and updated EVM instructions. The proposed approach is evaluated on real Ethereum on-chain transaction traces. The results show that our method is able to detect cross-function reentrancy behaviors that are not captured by the original TxSpector rules, demonstrating the effectiveness of pattern-based logic detection at the EVM execution level.

Cite As Get BibTex

Semia Guesmi, Carla Piazza, Andrea Gasparetto, Matteo Rizzo, and Sabina Rossi. Detecting Cross-Function Reentrancy from EVM Traces. In 7th International Workshop on Formal Methods for Blockchains (FMBC 2026). Open Access Series in Informatics (OASIcs), Volume 142, pp. 8:1-8:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/OASIcs.FMBC.2026.8

Author Details

Semia Guesmi
  • Ca’ Foscari University of Venice, Italy
  • University of Camerino, Italy
Carla Piazza
  • University of Udine, Italy
Andrea Gasparetto
  • Ca’ Foscari University of Venice, Italy
Matteo Rizzo
  • Ca’ Foscari University of Venice, Italy
Sabina Rossi
  • Ca’ Foscari University of Venice, Italy

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