A Lifecycle-Oriented Detection and Defense Framework for Price Manipulation Attacks in DeFi

Abstract

Aiming at frequent oracle price manipulation attacks in Decentralized Finance (DeFi), this paper investigates attack patterns, vulnerability types, risk assessment, automated detection, and defense strategies. Based on Oracle lifecycle theory, a three-layer attack tree covering the physical, protocol, and application layers is constructed to analyze the attack chain and identify the data election stage as a key intrusion point. Four major contract-level vulnerabilities are summarized: insufficient validation of price data flow, oracle call risks, uncontrolled cross-contract calls, and defects in AMM price reading logic. Fuzzy-AHP and Value-at-Risk (VaR) models are combined to quantify risk factors and construct a risk matrix covering technology, market, governance, and contract risks. An automated detection tool based on an extended Slither framework is developed using taint tracking and pattern matching. Finally, a multi-layer defense strategy is proposed, integrating trusted execution environments at the data source layer, TWAP smoothing and adaptive circuit breakers at the smart contract layer, and optimized kernel network configurations at the operating system layer. Experiments show that the proposed approach reduces attack-induced price deviation from 55.56% to below 5%. The detection tool achieves 94.38% accuracy and 92.31% recall, outperforming existing open-source tools.