# UEChecker: Detecting Unchecked External Call Vulnerabilities in DApps via Graph Analysis

arXiv: https://arxiv.org/abs/2508.01343 · HTML: https://mlsanity.com/paper/2508.01343/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 64 references that are in the map.
- First posted on arXiv 2 Aug 2025.

## Builds on (most cited first, 30 of 38)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Rethinking Attention with Performers](https://mlsanity.com/paper/2009.14794/) (2020)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Slither: A Static Analysis Framework For Smart Contracts](https://mlsanity.com/paper/1908.09878/) (2019)
- [Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications](https://mlsanity.com/paper/2403.16073/) (2024)
- [Empirical Review of Automated Analysis Tools on 47,587 Ethereum Smart Contracts](https://mlsanity.com/paper/1910.10601/) (2019)
- [Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges](https://mlsanity.com/paper/2105.07447/) (2021)
- [Mapping the NFT revolution: market trends, trade networks and visual features](https://mlsanity.com/paper/2106.00647/) (2021)
- [How Effective are Smart Contract Analysis Tools? Evaluating Smart Contract Static Analysis Tools Using Bug Injection](https://mlsanity.com/paper/2005.11613/) (2020)
- [Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification](https://mlsanity.com/paper/2006.08796/) (2020)
- [ACFIX: Guiding LLMs with Mined Common RBAC Practices for Context-Aware Repair of Access Control Vulnerabilities in Smart Contracts](https://mlsanity.com/paper/2403.06838/) (2024)
- [A Survey of Smart Contract Formal Specification and Verification](https://mlsanity.com/paper/2008.02712/) (2020)
- [An analysis of Uniswap markets](https://mlsanity.com/paper/1911.03380/) (2019)
- [Checking Smart Contracts with Structural Code Embedding](https://mlsanity.com/paper/2001.07125/) (2020)
- [Rethinking Smart Contract Fuzzing: Fuzzing With Invocation Ordering and Important Branch Revisiting](https://mlsanity.com/paper/2301.03943/) (2023)
- [Smart Contract Vulnerability Detection Technique: A Survey](https://mlsanity.com/paper/2209.05872/) (2022)
- [Unveiling Wash Trading in Popular NFT Markets](https://mlsanity.com/paper/2403.10361/) (2024)
- [StateGuard: Detecting State Derailment Defects in Decentralized Exchange Smart Contract](https://mlsanity.com/paper/2405.09181/) (2024)
- [Characterizing Code Clones in the Ethereum Smart Contract Ecosystem](https://mlsanity.com/paper/1905.00272/) (2019)
- [Improving Smart Contract Security with Contrastive Learning-based Vulnerability Detection](https://mlsanity.com/paper/2404.17839/) (2024)
- [DeFiTail: DeFi Protocol Inspection through Cross-Contract Execution Analysis](https://mlsanity.com/paper/2405.11035/) (2024)
- [Interaction-Aware Vulnerability Detection in Smart Contract Bytecodes](https://mlsanity.com/paper/2410.20712/) (2024)
- [Malicious Code Detection in Smart Contracts via Opcode Vectorization](https://mlsanity.com/paper/2504.12720/) (2025)
- [FlashSyn: Flash Loan Attack Synthesis via Counter Example Driven Approximation](https://mlsanity.com/paper/2206.10708/) (2022)
- [GasTrace: Detecting Sandwich Attack Malicious Accounts in Ethereum](https://mlsanity.com/paper/2405.19971/) (2024)
- [Detecting Malicious Accounts in Web3 through Transaction Graph](https://mlsanity.com/paper/2410.20713/) (2024)
- [SoK: Security Analysis of Blockchain-based Cryptocurrency](https://mlsanity.com/paper/2503.22156/) (2025)
- [SmartBugBert: BERT-Enhanced Vulnerability Detection for Smart Contract Bytecode](https://mlsanity.com/paper/2504.05002/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Novel GPT-Based Framework for Anomaly Detection in System Logs](https://mlsanity.com/paper/2510.16044/) (2025)

## Abstract (arXiv)

The increasing number of attacks on the contract layer of DApps has resulted in economic losses amounting to 66 billion. Vulnerabilities arise when contracts interact with external protocols without verifying the results of the calls, leading to exploit entry points such as flash loan attacks and reentrancy attacks. In this paper, we propose UEChecker, a deep learning-based tool that utilizes a call graph and a Graph Convolutional Network to detect unchecked external call vulnerabilities. We design the following components: An edge prediction module that reconstructs the feature representation of nodes and edges in the call graph; A node aggregation module that captures structural information from both the node itself and its neighbors, thereby enhancing feature representation between nodes and improving the model's understanding of the global graph structure; A Conformer Block module that integrates multi-head attention, convolutional modules, and feedforward neural networks to more effectively capture dependencies of different scales within the call graph, extending beyond immediate neighbors and enhancing the performance of vulnerability detection. Finally, we combine these modules with Graph Convolutional Network to detect unchecked external call vulnerabilities. By auditing the smart contracts of 608 DApps, our results show that our tool achieves an accuracy of 87.59% in detecting unchecked external call vulnerabilities. Furthermore, we compare our tool with GAT, LSTM, and GCN baselines, and in the comparison experiments, UEChecker consistently outperforms these models in terms of accuracy.