# Usenix'23 Extended Version: Smart Learning to Find Dumb Contracts

arXiv: https://arxiv.org/abs/2304.10726 · HTML: https://mlsanity.com/paper/2304.10726/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 98 references that are in the map.
- First posted on arXiv 21 Apr 2023.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Slither: A Static Analysis Framework For Smart Contracts](https://mlsanity.com/paper/1908.09878/) (2019)
- [Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis](https://mlsanity.com/paper/1805.04276/) (2018)
- [Improved Code Summarization via a Graph Neural Network](https://mlsanity.com/paper/2004.02843/) (2020)
- [Securify: Practical Security Analysis of Smart Contracts](https://mlsanity.com/paper/1806.01143/) (2018)
- [When Deep Learning Met Code Search](https://mlsanity.com/paper/1905.03813/) (2019)
- [Manticore: A User-Friendly Symbolic Execution Framework for Binaries and Smart Contracts](https://mlsanity.com/paper/1907.03890/) (2019)
- [Empirical Review of Automated Analysis Tools on 47,587 Ethereum Smart Contracts](https://mlsanity.com/paper/1910.10601/) (2019)
- [ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection](https://mlsanity.com/paper/1807.03932/) (2018)
- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [How Effective are Smart Contract Analysis Tools? Evaluating Smart Contract Static Analysis Tools Using Bug Injection](https://mlsanity.com/paper/2005.11613/) (2020)
- [Finding The Greedy, Prodigal, and Suicidal Contracts at Scale](https://mlsanity.com/paper/1802.06038/) (2018)
- [SmartBugs: A Framework to Analyze Solidity Smart Contracts](https://mlsanity.com/paper/2007.04771/) (2020)
- [Sereum: Protecting Existing Smart Contracts Against Re-Entrancy Attacks](https://mlsanity.com/paper/1812.05934/) (2018)
- [A Semantic Framework for the Security Analysis of Ethereum smart contracts](https://mlsanity.com/paper/1802.08660/) (2018)
- [eThor: Practical and Provably Sound Static Analysis of Ethereum Smart Contracts](https://mlsanity.com/paper/2005.06227/) (2020)
- [SAILFISH: Vetting Smart Contract State-Inconsistency Bugs in Seconds](https://mlsanity.com/paper/2104.08638/) (2021)
- [SmartEmbed: A Tool for Clone and Bug Detection in Smart Contracts through Structural Code Embedding](https://mlsanity.com/paper/1908.08615/) (2019)
- [EtherSolve: Computing an Accurate Control-Flow Graph from Ethereum Bytecode](https://mlsanity.com/paper/2103.09113/) (2021)
- [MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities](https://mlsanity.com/paper/2208.13252/) (2022)
- [The Eye of Horus: Spotting and Analyzing Attacks on Ethereum Smart Contracts](https://mlsanity.com/paper/2101.06204/) (2021)

## Built on by (most cited first, 2 of 2)

- [Two Timin': Repairing Smart Contracts With A Two-Layered Approach](https://mlsanity.com/paper/2309.07841/) (2023)
- [Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Training](https://mlsanity.com/paper/2603.27734/) (2026)

## Abstract (arXiv)

We introduce the Deep Learning Vulnerability Analyzer (DLVA) for Ethereum smart contracts based on neural networks. We train DLVA to judge bytecode even though the supervising oracle can only judge source. DLVA's training algorithm is general: we extend a source code analysis to bytecode without any manual feature engineering, predefined patterns, or expert rules. DLVA's training algorithm is also robust: it overcame a 1.25% error rate mislabeled contracts, and--the student surpassing the teacher--found vulnerable contracts that Slither mislabeled. DLVA is much faster than other smart contract vulnerability detectors: DLVA checks contracts for 29 vulnerabilities in 0.2 seconds, a 10-1,000x speedup. DLVA has three key components. First, Smart Contract to Vector (SC2V) uses neural networks to map smart contract bytecode to a high-dimensional floating-point vector. We benchmark SC2V against 4 state-of-the-art graph neural networks and show that it improves model differentiation by 2.2%. Second, Sibling Detector (SD) classifies contracts when a target contract's vector is Euclidian-close to a labeled contract's vector in a training set; although only able to judge 55.7% of the contracts in our test set, it has a Slither-predictive accuracy of 97.4% with a false positive rate of only 0.1%. Third, Core Classifier (CC) uses neural networks to infer vulnerable contracts regardless of vector distance. We benchmark DLVA's CC with 10 ML techniques and show that the CC improves accuracy by 11.3%. Overall, DLVA predicts Slither's labels with an overall accuracy of 92.7% and associated false positive rate of 7.2%. Lastly, we benchmark DLVA against nine well-known smart contract analysis tools. Despite using much less analysis time, DLVA completed every query, leading the pack with an average accuracy of 99.7%, pleasingly balancing high true positive rates with low false positive rates.