# DeFiTail: DeFi Protocol Inspection through Cross-Contract Execution Analysis

arXiv: https://arxiv.org/abs/2405.11035 · HTML: https://mlsanity.com/paper/2405.11035/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2024.

## Built on by (most cited first, 7 of 7)

- [SCALM: Detecting Bad Practices in Smart Contracts Through LLMs](https://mlsanity.com/paper/2502.04347/) (2025)
- [DeXposure: A Dataset and Benchmarks for Inter-protocol Credit Exposure in Decentralized Financial Networks](https://mlsanity.com/paper/2511.22314/) (2025)
- [AI-Based Vulnerability Analysis of NFT Smart Contracts](https://mlsanity.com/paper/2504.16113/) (2025)
- [Information Security Based on LLM Approaches: A Review](https://mlsanity.com/paper/2507.18215/) (2025)
- [UEChecker: Detecting Unchecked External Call Vulnerabilities in DApps via Graph Analysis](https://mlsanity.com/paper/2508.01343/) (2025)
- [Slow is Fast! Dissecting Ethereum's Slow Liquidity Drain Scams](https://mlsanity.com/paper/2503.04850/) (2025)
- [On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation](https://mlsanity.com/paper/2510.16024/) (2025)

## Abstract (arXiv)

Decentralized finance (DeFi) protocols are crypto projects developed on the blockchain to manage digital assets. Attacks on DeFi have been frequent and have resulted in losses exceeding 77 billion. However, detection methods for malicious DeFi events are still lacking. In this paper, we propose DeFiTail, the first framework that utilizes deep learning to detect access control and flash loan exploits that may occur on DeFi. Since the DeFi protocol events involve invocations with multi-account transactions, which requires execution path unification with different contracts. Moreover, to mitigate the impact of mistakes in Control Flow Graph (CFG) connections, we validate the data path by employing the symbolic execution stack. Furthermore, we feed the data paths through our model to achieve the inspection of DeFi protocols. Experimental results indicate that DeFiTail achieves the highest accuracy, with 98.39% in access control and 97.43% in flash loan exploits. DeFiTail also demonstrates an enhanced capability to detect malicious contracts, identifying 86.67% accuracy from the CVE dataset.