The rapid growth of Decentralized Finance (DeFi) boosts the blockchain ecosystem. At the same time, attacks on DeFi applications (apps) are increasing. However, to the best of our knowledge, existing smart contract vulnerability detection tools cannot directly detect DeFi attacks. That's because they lack the capability to recover and understand high-level DeFi semantics, e.g., a user trades a token pairXandYin a Decentralized EXchange (DEX). In this work, we focus on the detection of two new types of price manipulation attacks. To this end, we propose a platform-independent method to identify high-level DeFi semantics. Specifically, we first construct the Cash Flow Tree (CFT) from a raw transaction and then lifting the low-level semantics to high-level ones, including five advanced DeFi actions. Finally, we use patterns expressed with the recovered DeFi semantics to detect price manipulation attacks. We implemented a prototype namedDeFiRangerthat detected 14zero-daysecurity incidents. These findings were reported to affected parties or/and the community for the first time. Furthermore, the backtest experiment discovered 15 unknown historical security incidents. We further performed an attack analysis to shed light on the root causes of vulnerabilities incurring price manipulation attacks.

The prosperity of Decentralized Finance (DeFi) unveils underlying risks, with reported losses surpassing 3.2 billion USD between 2018 and 2022 due to vulnerabilities in Decentralized Applications (DApps). One significant threat is the Price Manipulation Attack (PMA) that alters asset prices during transaction execution. As a result, PMA accounts for over 50 million USD in losses. To address the urgent need for efficient PMA detection, this article introduces a novel detection service,DeFiGuard, using Graph Neural Networks (GNNs). In this article, we propose cash flow graphs with four distinct features, which capture the trading behaviors from transactions. Moreover,DeFiGuardintegrates transaction parsing, graph construction, model training, and PMA detection. Evaluations on the collected transactions demonstrate thatDeFiGuardwith GNN models outperforms the baseline MLP model and classical classification models in Accuracy, TPR, FPR, and AUC-ROC. The results of ablation studies suggest that the combination of the four proposed node features enhancesDeFiGuard’s efficacy. Moreover,DeFiGuardclassifies transactions within 0.892 to 5.317 seconds, which provides sufficient time for the victims (DApps and users) to take action to rescue their vulnerable funds. In conclusion, this research offers a significant step towards safeguarding the DeFi landscape from PMAs using GNNs.

The prosperity of decentralized finance motivates many investors to profit via trading their crypto assets on decentralized applications (DApps for short) of the Ethereum ecosystem. Apart from Ether (the native cryptocurrency of Ethereum), many ERC20 (a widely used token standard on Ethereum) tokens obtain vast market value in the ecosystem. Specifically, the approval mechanism is used to delegate the privilege of spending users’ tokens to DApps. By doing so, the DApps can transfer these tokens to arbitrary receivers on behalf of the users. To increase the usability, unlimited approval is commonly adopted by DApps to reduce the required interaction between them and their users. However, as shown in existing security incidents, this mechanism can be abused to steal users’ tokens.