What question did this study set out to answer?

The aim is to create a detailed classification of DeFi attack vectors to improve security understanding.

July 19, 20260 citationsOpen Access

A Comprehensive Taxonomy of DeFi Attack Patterns: 50 Vectors from 824 Incidents (2017-2026)

Key Points

The aim is to create a detailed classification of DeFi attack vectors to improve security understanding.
Developed a taxonomy of 50 distinct DeFi attack patterns based on 824 confirmed incidents from 2017-2026.
Included real-world examples, detection methodologies, and Slither detection rules for each pattern.
Achieved 97.6% coverage of incidents, significantly outperforming previous classifications.
Identified that 8 patterns account for 76% of losses, with flash loan and oracle manipulation leading to 24% of cases and $6B+ in losses.
Discovered that 12 patterns lack any Slither detection rules.
Noted that 18 patterns necessitate business-logic understanding beyond static analysis methods.

Abstract

We present the first comprehensive taxonomy of 50 distinct DeFi attack vectors, empirically validated against 824 confirmed incidents spanning 2017-2026. Our classification achieves 97. 6% coverage (804/824 cases categorized) compared to 58% for the best prior taxonomy. Each pattern includes a canonical real-world example, detection methodology, and Slither detection rule. We find that 8 patterns account for 76% of all losses, with flash loan + oracle manipulation alone responsible for 24% of cases and 60% of total losses (6B+). The taxonomy reveals that 12 patterns (24%) lack Slither rules entirely, and 18 patterns (36%) require business-logic understanding beyond static analysis. We release the complete taxonomy, detection rules, and an open-source 50-rule DeFi scanner.

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