A quantitative notion of economic security for smart contract compositions
Emily Priyadarshini, Massimo Bartoletti
TL;DR
This paper introduces MEV interference, a quantitative metric that captures how attacking a single component in a DeFi-smart-contract composition can amplify economic losses through dependencies in the broader context. By formalizing a deterministic contract model, a threat model based on local MEV, and a precise definition $\mathcal{I} = 1 - \frac{\mathrm{MEV}_{\dagger{\Delta}}}{\mathrm{MEV}}$, the authors study fundamental properties (e.g., monotonicity, wallet-independence, and independency-based preservation) and apply the approach to use-cases like token-price bets, price-oracle-based lending, and AMM interactions. The results offer a principled way to quantify economic security and guide design choices in DeFi micro-architectures, while acknowledging limitations such as fixed prices and abstract mempool considerations that point to fruitful future work. Overall, the work provides a framework for static, quantitative assessment of inter-contract security risks and their potential systemic impact in smart contract ecosystems.
Abstract
Decentralized applications are often composed of multiple interconnected smart contracts. This is especially evident in DeFi, where protocols are heavily intertwined and rely on a variety of basic building blocks such as tokens, decentralized exchanges and lending protocols. A crucial security challenge in this setting arises when adversaries target individual components to cause systemic economic losses. Existing security notions focus on determining the existence of these attacks, but fail to quantify the effect of manipulating individual components on the overall economic security of the system. In this paper, we introduce a quantitative security notion that measures how an attack on a single component can amplify economic losses of the overall system. We study the fundamental properties of this notion and apply it to assess the security of key compositions. In particular, we analyse under-collateralized loan attacks in systems made of lending protocols and decentralized exchanges.