# The Art of The Scam: Demystifying Honeypots in Ethereum Smart Contracts

arXiv: https://arxiv.org/abs/1902.06976 · HTML: https://mlsanity.com/paper/1902.06976/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2019.

## Built on by (most cited first, 13 of 13)

- [Blockchain Large Language Models](https://mlsanity.com/paper/2304.12749/) (2023)
- [Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts](https://mlsanity.com/paper/2101.02377/) (2021)
- [Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain](https://mlsanity.com/paper/2412.12370/) (2024)
- [Prompt to Pwn: Automated Exploit Generation for Smart Contracts](https://mlsanity.com/paper/2508.01371/) (2025)
- [Do not rug on me: Zero-dimensional Scam Detection](https://mlsanity.com/paper/2201.07220/) (2022)
- [Giving AI Agents Access to Cryptocurrency and Smart Contracts Creates New Vectors of AI Harm](https://mlsanity.com/paper/2507.08249/) (2025)
- [Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis](https://mlsanity.com/paper/2407.18639/) (2024)
- [Deep Smart Contract Intent Detection](https://mlsanity.com/paper/2211.10724/) (2022)
- [Slow is Fast! Dissecting Ethereum's Slow Liquidity Drain Scams](https://mlsanity.com/paper/2503.04850/) (2025)
- [CASPER: Contrastive Approach for Smart Ponzi Scheme Detecter with More Negative Samples](https://mlsanity.com/paper/2507.16840/) (2025)
- [SEASONED: Semantic-Enhanced Self-Counterfactual Explainable Detection of Adversarial Exploiter Contracts](https://mlsanity.com/paper/2509.05681/) (2025)
- [Discovering Persistent Behavioural Patterns for Interpretable Blockchain Forensics](https://mlsanity.com/paper/2608.12864/) (2026)
- [DeFiFusion: Combining Transaction Events with Smart Contracts to Detect Price Manipulation Attacks](https://mlsanity.com/paper/2609.11008/) (2026)

## Abstract (arXiv)

Modern blockchains, such as Ethereum, enable the execution of so-called smart contracts - programs that are executed across a decentralised network of nodes. As smart contracts become more popular and carry more value, they become more of an interesting target for attackers. In the past few years, several smart contracts have been exploited by attackers. However, a new trend towards a more proactive approach seems to be on the rise, where attackers do not search for vulnerable contracts anymore. Instead, they try to lure their victims into traps by deploying seemingly vulnerable contracts that contain hidden traps. This new type of contracts is commonly referred to as honeypots. In this paper, we present the first systematic analysis of honeypot smart contracts, by investigating their prevalence, behaviour and impact on the Ethereum blockchain. We develop a taxonomy of honeypot techniques and use this to build HoneyBadger - a tool that employs symbolic execution and well defined heuristics to expose honeypots. We perform a large-scale analysis on more than 2 million smart contracts and show that our tool not only achieves high precision, but is also highly efficient. We identify 690 honeypot smart contracts as well as 240 victims in the wild, with an accumulated profit of more than 90,000 for the honeypot creators. Our manual validation shows that 87% of the reported contracts are indeed honeypots.