# Finding The Greedy, Prodigal, and Suicidal Contracts at Scale

arXiv: https://arxiv.org/abs/1802.06038 · HTML: https://mlsanity.com/paper/1802.06038/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2018.

## Built on by (most cited first, 19 of 19)

- [Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2107.11598/) (2021)
- [Blockchain Large Language Models](https://mlsanity.com/paper/2304.12749/) (2023)
- [AI Agent Smart Contract Exploit Generation](https://mlsanity.com/paper/2507.05558/) (2025)
- [PoCo: Agentic Proof-of-Concept Exploit Generation for Smart Contracts](https://mlsanity.com/paper/2511.02780/) (2025)
- [Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts](https://mlsanity.com/paper/2101.02377/) (2021)
- [Logic Meets Magic: LLMs Cracking Smart Contract Vulnerabilities](https://mlsanity.com/paper/2501.07058/) (2025)
- [ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning](https://mlsanity.com/paper/2103.12607/) (2021)
- [Prompt to Pwn: Automated Exploit Generation for Smart Contracts](https://mlsanity.com/paper/2508.01371/) (2025)
- [The Role of Transformer Models in Advancing Blockchain Technology: A Systematic Survey](https://mlsanity.com/paper/2409.02139/) (2024)
- [Usenix'23 Extended Version: Smart Learning to Find Dumb Contracts](https://mlsanity.com/paper/2304.10726/) (2023)
- [Deep Smart Contract Intent Detection](https://mlsanity.com/paper/2211.10724/) (2022)
- [SourceP: Detecting Ponzi Schemes on Ethereum with Source Code](https://mlsanity.com/paper/2306.01665/) (2023)
- [Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges](https://mlsanity.com/paper/2508.02773/) (2025)
- [Large Language Model based Smart Contract Auditing with LLMBugScanner](https://mlsanity.com/paper/2512.02069/) (2025)
- [Schooling to Exploit Foolish Contracts](https://mlsanity.com/paper/2304.10737/) (2023)
- [Vulnerability Detection in Smart Contracts: A Comprehensive Survey](https://mlsanity.com/paper/2407.07922/) (2024)
- [A Survey on Blockchain-based Supply Chain Finance with Progress and Future directions](https://mlsanity.com/paper/2408.08915/) (2024)
- [JANUS: A Difference-Oriented Analyzer For Financial Centralization Risks in Smart Contracts](https://mlsanity.com/paper/2412.03938/) (2024)
- [AttackPathGNN: Cross-function vulnerability detection in smart contracts using state interference graphs and conjunction pooling](https://mlsanity.com/paper/2606.05986/) (2026)

## Abstract (arXiv)

Smart contracts---stateful executable objects hosted on blockchains like Ethereum---carry billions of dollars worth of coins and cannot be updated once deployed. We present a new systematic characterization of a class of trace vulnerabilities, which result from analyzing multiple invocations of a contract over its lifetime. We focus attention on three example properties of such trace vulnerabilities: finding contracts that either lock funds indefinitely, leak them carelessly to arbitrary users, or can be killed by anyone. We implemented MAIAN, the first tool for precisely specifying and reasoning about trace properties, which employs inter-procedural symbolic analysis and concrete validator for exhibiting real exploits. Our analysis of nearly one million contracts flags 34,200 (2,365 distinct) contracts vulnerable, in 10 seconds per contract. On a subset of3,759 contracts which we sampled for concrete validation and manual analysis, we reproduce real exploits at a true positive rate of 89%, yielding exploits for3,686 contracts. Our tool finds exploits for the infamous Parity bug that indirectly locked 200 million dollars worth in Ether, which previous analyses failed to capture.