DeFi protocols are programs that manage high-value digital assets on blockchain. The price manipulation vulnerability is one of the common vulnerabilities in DeFi protocols, which allows attackers to gain excessive profits by manipulating token prices. In this paper, we propose DeFiTainter, an inter-contract taint analysis framework for detecting price manipulation vulnerabilities. DeFiTainter features two innovative mechanisms to ensure its effectiveness. The first mechanism is to construct a call graph for inter-contract taint analysis by restoring call information, not only from code constants but also from contract storage and function parameters. The second mechanism is a high-level semantic induction tailored for detecting price manipulation vulnerabilities, which accurately identifies taint sources and sinks and tracks taint data across contracts. Extensive evaluation of real-world incidents and high-value DeFi protocols shows that DeFiTainter outperforms existing approaches and achieves state-of-the-art performance with a precision of 96% and a recall of 91.3% in detecting price manipulation vulnerabilities. Furthermore, DeFiTainter uncovers three previously undisclosed price manipulation vulnerabilities.

DeFiTainter: Detecting Price Manipulation Vulnerabilities in DeFi Protocols

—Blockchain consensus protocols are responsible for coordinating the nodes to make agreements on the transaction results. Their implementation bugs, including memory-related and consensus logic vulnerabilities, may pose serious threats. Fuzzing is a promising technique for protocol vulnerability detection. However, existing fuzzers cannot deal with complex consensus states of distributed nodes, thus generating a large number of useless packets, inhibiting their effectiveness in reaching the deep logic of consensus protocols. In this work, we propose LOKI, a blockchain consensus protocol fuzzing framework that detects consensus memory-related and logic bugs. LOKI fetches consensus states in real- time by masquerading as a node. First, LOKI dynamically builds a state model that records the state transition of each node. After that, LOKI adaptively generates the input targets, types, and contents according to the state model. With a bug analyzer, LOKI detects the consensus protocol implementation bugs with well-defined oracles. We implemented and evaluated LOKI on four widely used commercial blockchain systems, including Go-Ethereum, Meta Diem, IBM Fabric, and WeBank FISCO-BCOS. LOKI has detected 20 serious previously unknown vulnerabilities with 9 CVEs assigned. 14 of them are memory-related bugs, and 6 are consensus logic bugs. Compared with state-of-the-art tools such as Peach, Fluffy, and Twins, LOKI improves the branch coverage by an average of 43.21%, 182.05%, and 291.58%.

/pdf/loki-state-aware-fuzzing-framework-for-the-implementation-of-1qlkc594.pdf

LOKI: State-Aware Fuzzing Framework for the Implementation of Blockchain Consensus Protocols

Smart contracts are programs deployed on a blockchain and are immutable once deployed. Reentrancy, one of the most important vulnerabilities in smart contracts, has caused millions of dollars in financial loss. Many reentrancy detection approaches have been proposed. It is necessary to investigate the performance of these approaches to provide useful guidelines for their application. In this work, we conduct a large-scale empirical study on the capability of five well-known or recent reentrancy detection tools such as Mythril and Sailfish. We collect 230,548 verified smart contracts from Etherscan and use detection tools to analyze 139,424 contracts after deduplication, which results in 21,212 contracts with reentrancy issues. Then, we manually examine the defective functions located by the tools in the contracts. From the examination results, we obtain 34 true positive contracts with reentrancy and 21,178 false positive contracts without reentrancy. We also analyze the causes of the true and false positives. Finally, we evaluate the tools based on the two kinds of contracts. The results show that more than 99.8% of the reentrant contracts detected by the tools are false positives with eight types of causes, and the tools can only detect the reentrancy issues caused by call.value(), 58.8% of which can be revealed by the Ethereum's official IDE, Remix. Furthermore, we collect real-world reentrancy attacks reported in the past two years and find that the tools fail to find any issues in the corresponding contracts. Based on the findings, existing works on reentrancy detection appear to have very limited capability, and researchers should turn the rudder to discover and detect new reentrancy patterns except those related to call.value(). 

pdf/turn-the-rudder-a-beacon-of-reentrancy-detection-for-smart-356hveixqz.pdf

Turn the Rudder: A Beacon of Reentrancy Detection for Smart Contracts on Ethereum

Decentralized applications (DApps) consist of multiple smart contracts running on Blockchain. With the increasing popularity of the DApp ecosystem, vulnerabilities in DApps could bring significant impacts such as financial losses. Identifying vulnerabilities in DApps is by no means trivial, as modern DApps consist of complex interactions across multiple contracts. Previous research suffers from either high false positives or false negatives, due to the lack of precise contextual information which is mandatory for confirming smart contract vulnerabilities when analyzing smart contracts. In this paper, we present IcyChecker, a new fuzzing-based framework to effectively identify State inconsistency (SI) Bugs – a specific type of bugs that can cause vulnerabilities such as re-entrancy, front-running with complex patterns. Different from prior works, IcyChecker utilizes a set of accurate contextual information for contract fuzzing by replaying the on-chain historical transactions. Besides, instead of designing specific testing oracles which are required by other fuzzing approaches, IcyChecker implements novel mechanisms to mutate a set of fuzzing transaction sequences, and further identify SI bugs by observing their state differences. Evaluation of IcyChecker over the top 100 popular DApps showed it effectively identifies a total number of 277 SI bugs, with a precision of 87%. By comparing IcyChecker with other state-of-the-art tools (i.e., Smartian, Confuzzius, and Sailfish), we show IcyChecker not only identifies more SI bugs but also with much lower false positives, thanks to its integration of accurate on-chain data and unique fuzzing strategies. Our research sheds light on new ways of detecting smart contract vulnerabilities in DApps.

Detecting State Inconsistency Bugs in DApps via On-Chain Transaction Replay and Fuzzing

Smart contracts are immutable programs executed in the context of a globally distributed system known as a blockchain . They enable the decentralized implementation of many interesting applications, such as financial protocols, voting systems, and supply-chain management. In many cases, multiple smart contracts need to work together and communicate with one another to implement complex business logic. However, these smart contracts must take special care to guard against malicious interactions that might lead to the violation of a contract’s security properties and possibly result in substantial financial losses. In this paper, we introduce a class of inter-program communication flaws that we call confused contract vulnerabilities . This type of bug is an instance of the confused deputy vulnerability, set in the new context of smart contract inter-communication. When exploiting a confused contract bug, an attacker is able to divert a remote (inter-contract) call in a confused (victim) contract to a target contract and function of the attacker’s choosing. The call performs sensitive operations on behalf of the confused contract, which can result in financial loss or malicious modifications of the persistent storage of the involved contracts. To identify opportunities for confused contract attacks at scale, we implemented J ACKAL , a system that is able to automatically identify and exploit confused contracts and candidate target contracts on the Ethereum mainnet. We lever-aged J ACKAL to analyze a total of 2,335,193 smart contracts deployed in the past two years, and we identified 529 potential confused contracts for which we were able to generate 31 working exploits. When investigating the impact of our exploits, we discovered past and present opportunities for confused contract attacks that could have compromised digital assets worth more than one million US dollars.

Confusum Contractum: Confused Deputy Vulnerabilities in Ethereum Smart Contracts

Pluto: Exposing Vulnerabilities in Inter-Contract Scenarios

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Pluto: Exposing Vulnerabilities in Inter-Contract Scenarios