Toward a Risk Assessment Framework for Institutional DeFi: A Nine-Dimension Approach

TL;DR

This paper introduces a comprehensive nine-dimension risk assessment framework for institutional DeFi, enhancing existing models with novel dimensions and structural analysis to better understand and mitigate risks.

Contribution

It extends prior DeFi risk taxonomies by adding three new dimensions and incorporates an ontology-based analysis for more explainable and composability-aware risk evaluation.

Findings

Retrospective analysis of 12 major DeFi incidents involving USD 2.5 billion in losses.

Five incidents required at least one novel risk dimension for full root-cause analysis.

The framework improves understanding of systemic risks in DeFi protocols.

Abstract

Decentralized finance (DeFi) protocols now intermediate over USD 100 billion in value, including regulated stablecoins and tokenized assets deployed as collateral, yet no widely adopted framework operationalizes risk assessment at the rigor institutional adoption demands. Existing approaches emphasize protocol-specific parameter optimization or conceptual taxonomies without providing explainable, composability-aware, and structurally independent assessment methodologies. We propose a nine-dimension DeFi risk assessment framework extending the six-dimension taxonomy introduced by Moody's Analytics and Gauntlet with three novel dimensions: composability risk, comprehension debt, and temporal risk dynamics. We additionally introduce a transparency confidence modifier separating assessment reliability from risk severity. The framework is grounded in structural analysis of protocol dependencies conducted through an ontology-based protocol intelligence infrastructure covering more than 8,000 DeFi protocols. We retrospectively analyze 12 major DeFi-related incidents from 2024-2026 representing approximately USD 2.5 billion in direct losses. Five of the 12 incidents require at least one novel dimension for complete root-cause characterization, including the two highest-systemic-impact events in the dataset.