Blockchain is a decentralized distributed system on which a large number of financial applications have been deployed. The consensus process in it plays an important role, which guarantees that legal transactions on the chain can be executed and recorded fairly and consistently. However, because of Consensus Failure Bugs (CFBs), many blockchain systems do not provide even this basic guarantee. The validity and consistency of blockchain systems rely on the soundness of complex consensus logic implementation. Any bugs which cause the blockchain consensus failure can be crucial.In this work, we introduce Tyr, an open-source tool for detecting CFBs in blockchain systems with a large number of abnormal divergent consensus behaviors. First, we design four oracle detectors to monitor the behaviors of nodes and analyze the violation of consensus properties. To trigger these oracles effectively, Tyr harnesses a behavior divergent model to constantly generate consensus messages and make nodes behave as differently as possible. We implemented and evaluated Tyr on six widely used commercial blockchain consensus systems, including IBM Fabric, WeBank FISCO-BCOS, ConsenSys Quorum, Facebook Diem, Go-Ethereum, and EOS. Compared with the state-of-the-art tools Peach, Fluffy, and Twins, Tyr covers 27.3%, 228.2%, and 297.1% more branches, respectively. Furthermore, Tyr has detected 20 serious previously unknown vulnerabilities, all of which have been repaired by the corresponding maintainers.

Tyr: Finding Consensus Failure Bugs in Blockchain System with Behaviour Divergent Model

—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%.

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

The promise of smart contracts (computer programs running on a decentralized virtual computer) lies in the ability to execute agreements without the risk of interference by powerful intermediaries. However, in practice, many smart contracts reintroduce privileged parties on the application layer. They are programmed to enforce that certain functions can only be executed by the owners of defined accounts. We propose and validate a method to detect such privileged parties from binary smart contract code on the Ethereum platform. Our open-source implementation, Ethpector, can be used to verify claims about “zero-trust,” reveal ownership structures, forensically analyze networks of virtual shell organizations, and may support auditors when testifying ownership of intangible assets on Ethereum held by conventional legal entities.

Detecting Privileged Parties on Ethereum

With the development of decentralized networks, smart contracts, especially those for ERC tokens, are attracting more and more Dapp users to implement their applications. There are some functions in ERC token contracts that only a specific group of accounts could invoke. Among those functions, some even can influence other accounts or the whole system without prior notice or permission. These functions are referred to as contract backdoors. Once exploited by an attacker, they can cause property losses and harm users’ privacy. In this work, we propose Pied-Piper, a hybrid analysis method that integrates datalog analysis and directed fuzzing to detect backdoor threats in Ethereum ERC token contracts. First, datalog analysis is applied to abstract the data structures and identification rules related to the threats for preliminary static detection. Then, directed fuzzing is applied to eliminate false positives caused by the static analysis. We first evaluated Pied-Piper on 200 smart contracts, which are injected with different types of backdoors. It reported all problems without false positives, and none of the injected problems was missed. Then, we applied Pied-Piper on 13,484 real token contracts deployed on Ethereum. Pied-Piper reported 189 confirmed problems, four of which have been assigned unique CVE ids while others are still in the review process. Each contract takes 8.03 seconds for datalog analysis on average, and the fuzzing engine can eliminate the false positives within one minute.

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Xiao Dai

Papers

Pied-Piper: Revealing the Backdoor Threats in Ethereum ERC Token Contracts