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.

翻译：去中心化金融（DeFi）协议如今中介着超过1000亿美元的价值，包括作为抵押品部署的受监管稳定币和代币化资产，然而尚无被广泛采用的框架能以机构采用所需的严谨性操作化风险评估。现有方法侧重于协议特定的参数优化或概念分类学，未能提供可解释、可感知组合性且结构独立的评估方法。我们提出一个九维DeFi风险评估框架，将穆迪分析与Gauntlet公司引入的六维分类法扩展为三个新维度：组合性风险、理解债务及时间风险动态。我们额外引入一个透明度置信度修正量，将评估可靠性从风险严重性中分离出来。该框架基于通过一个覆盖超过8000个DeFi协议的基于本体的协议智能基础设施所进行的协议依赖关系结构分析。我们回顾性分析了2024-2026年间12起主要DeFi相关事件，代表约25亿美元的直接损失。其中5起事件需要至少一个新维度才能完整刻画根本原因，包括数据集中两起系统性影响最高的事件。