# SoK: Decentralized Finance (DeFi) Attacks

arXiv: https://arxiv.org/abs/2208.13035 · HTML: https://mlsanity.com/paper/2208.13035/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2022.

## Built on by (most cited first, 28 of 28)

- [LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning](https://mlsanity.com/paper/2401.16185/) (2024)
- [Large Language Model-Powered Smart Contract Vulnerability Detection: New Perspectives](https://mlsanity.com/paper/2310.01152/) (2023)
- [Blockchain Large Language Models](https://mlsanity.com/paper/2304.12749/) (2023)
- [AI Agent Smart Contract Exploit Generation](https://mlsanity.com/paper/2507.05558/) (2025)
- [Retrieval Augmented Generation Integrated Large Language Models in Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2407.14838/) (2024)
- [Detecting Various DeFi Price Manipulations with LLM Reasoning](https://mlsanity.com/paper/2502.11521/) (2025)
- [SmartLLM: Smart Contract Auditing using Custom Generative AI](https://mlsanity.com/paper/2502.13167/) (2025)
- [Towards Secure Program Partitioning for Smart Contracts with LLM's In-Context Learning](https://mlsanity.com/paper/2502.14215/) (2025)
- [TxRay: Agentic Postmortem of Live Blockchain Attacks](https://mlsanity.com/paper/2602.01317/) (2026)
- [FTSmartAudit: A Knowledge Distillation-Enhanced Framework for Automated Smart Contract Auditing Using Fine-Tuned LLMs](https://mlsanity.com/paper/2410.13918/) (2024)
- [From Rules to Rewards: Reinforcement Learning for Interest Rate Adjustment in DeFi Lending](https://mlsanity.com/paper/2506.00505/) (2025)
- [Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges](https://mlsanity.com/paper/2508.02773/) (2025)
- [Early Rug Pull Warning for BSC Meme Tokens via Multi-Granularity Wash-Trading Pattern Profiling](https://mlsanity.com/paper/2603.13830/) (2026)
- [Knowdit: Agentic Smart Contract Vulnerability Detection with Auditing Knowledge Summarization](https://mlsanity.com/paper/2603.26270/) (2026)
- [PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance](https://mlsanity.com/paper/2608.17220/) (2026)
- [FRAD: Front-Running Attacks Detection on Ethereum using Ternary Classification Model](https://mlsanity.com/paper/2311.14514/) (2023)
- [Slow is Fast! Dissecting Ethereum's Slow Liquidity Drain Scams](https://mlsanity.com/paper/2503.04850/) (2025)
- [SEASONED: Semantic-Enhanced Self-Counterfactual Explainable Detection of Adversarial Exploiter Contracts](https://mlsanity.com/paper/2509.05681/) (2025)
- [On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation](https://mlsanity.com/paper/2510.16024/) (2025)
- [Multi-Agent Collaborative Fuzzing with Continuous Reflection for Smart Contracts Vulnerability Detection](https://mlsanity.com/paper/2511.12164/) (2025)
- [Unlocking the Forecasting Economy: A Suite of Datasets for the Full Lifecycle of Prediction Market: [Experiments & Analysis]](https://mlsanity.com/paper/2604.20421/) (2026)
- [Tailored Prompts, Targeted Protection: Vulnerability-Specific LLM Analysis for Smart Contracts](https://mlsanity.com/paper/2605.03697/) (2026)
- [AttackPathGNN: Cross-function vulnerability detection in smart contracts using state interference graphs and conjunction pooling](https://mlsanity.com/paper/2606.05986/) (2026)
- [CyberChainBench: Can AI Agents Secure Smart Contracts Against Real-World On-Chain Vulnerabilities?](https://mlsanity.com/paper/2606.26216/) (2026)
- [Beyond Detection: Agentic Attack Synthesis and Simulation for Smart Contracts](https://mlsanity.com/paper/2607.15673/) (2026)
- [Compression-Based Behavioral Similarity for Open-World Sybil Discovery on Ethereum](https://mlsanity.com/paper/2607.27370/) (2026)
- [If It Walks Like an Arbitrage: Protocol-Agnostic Detection with Decidable Structural Equivalence](https://mlsanity.com/paper/2608.20377/) (2026)
- [DeFiFusion: Combining Transaction Events with Smart Contracts to Detect Price Manipulation Attacks](https://mlsanity.com/paper/2609.11008/) (2026)

## Abstract (arXiv)

Within just four years, the blockchain-based Decentralized Finance (DeFi) ecosystem has accumulated a peak total value locked (TVL) of more than 253 billion USD. This surge in DeFi's popularity has, unfortunately, been accompanied by many impactful incidents. According to our data, users, liquidity providers, speculators, and protocol operators suffered a total loss of at least 3.24 billion USD from Apr 30, 2018 to Apr 30, 2022. Given the blockchain's transparency and increasing incident frequency, two questions arise: How can we systematically measure, evaluate, and compare DeFi incidents? How can we learn from past attacks to strengthen DeFi security? In this paper, we introduce a common reference frame to systematically evaluate and compare DeFi incidents, including both attacks and accidents. We investigate 77 academic papers, 30 audit reports, and 181 real-world incidents. Our data reveals several gaps between academia and the practitioners' community. For example, few academic papers address "price oracle attacks" and "permissonless interactions", while our data suggests that they are the two most frequent incident types (15% and 10.5% correspondingly). We also investigate potential defenses, and find that: (i) 103 (56%) of the attacks are not executed atomically, granting a rescue time frame for defenders; (ii) SoTA bytecode similarity analysis can at least detect 31 vulnerable/23 adversarial contracts; and (iii) 33 (15.3%) of the adversaries leak potentially identifiable information by interacting with centralized exchanges.