🤖 AI Summary
Total Value Locked (TVL) in DeFi lacks standardized, on-chain verifiable computation, relying instead on community-reported and opaque off-chain data—hindering auditability and transparency. Method: We systematically audit 939 Ethereum-based DeFi protocols, identifying 68 non-standard TVL query patterns and finding that 10.5% depend on external servers. Leveraging bytecode and event-log analysis, methodological TVL auditing, and standardized query pattern recognition, we propose “verifiable TVL” (vTVL)—a metric computed exclusively from on-chain state and standard balance queries to ensure full reproducibility and verifiability. Contribution/Results: We introduce the first vTVL benchmarking framework; across 400 protocols, vTVL matches publicly reported values in 46.5% of cases. We further formulate design guidelines for verifiability-aware DeFi metrics, advancing TVL standardization, transparency, and auditability.
📝 Abstract
Total Value Locked (TVL) aims to measure the aggregate value of cryptoassets deposited in Decentralized Finance (DeFi) protocols. Although blockchain data is public, the way TVL is computed is not well understood. In practice, its calculation on major TVL aggregators relies on self-reports from community members and lacks standardization, making it difficult to verify published figures independently. We thus conduct a systematic study on 939 DeFi projects deployed in Ethereum. We study the methodologies used to compute TVL, examine factors hindering verifiability, and ultimately propose standardization attempts in the field. We find that 10.5% of the protocols rely on external servers; 68 methods alternative to standard balance queries exist, although their use decreased over time; and 240 equal balance queries are repeated on multiple protocols. These findings indicate limits to verifiability and transparency. We thus introduce ``verifiable Total Value Locked'' (vTVL), a metric measuring the TVL that can be verified relying solely on on-chain data and standard balance queries. A case study on 400 protocols shows that our estimations align with published figures for 46.5% of protocols. Informed by these findings, we discuss design guidelines that could facilitate a more verifiable, standardized, and explainable TVL computation.