SigRec: Automatic Recovery of Function Signatures in Smart Contracts

Chen, Ting; Li, Zihao; Luo, Xiapu; Wang, Ting; He, Zheyuan; Fang, Kezhao; Zhang, Yufei; Zhu, Hang; Li, Hongwei; Cheng, Yan; Zhang, Xiao Song

doi:10.1109/tse.2021.3078342

Cited by 14 publications

(4 citation statements)

References 41 publications

(98 reference statements)

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“…Once deployed, each contract is assigned a unique address. Users can interact with the contract by encoding the function signature and actual parameters [35,106] into a transaction according to the Application Binary Interface (ABI). The ABI is JSON data generated in compilation, which describes methods to be invoked to execute smart contracts.…”

Section: Background 21 Smart Contractmentioning

confidence: 99%

Are We There Yet? Unraveling the State-of-the-Art Smart Contract Fuzzers

Wu,

Li,

Yan

et al. 2024

Proceedings of the IEEE/ACM 46th International Conference on Software Engineering

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Given the growing importance of smart contracts in various applications, ensuring their security and reliability is critical. Fuzzing, an effective vulnerability detection technique, has recently been widely applied to smart contracts. Despite numerous studies, a systematic investigation of smart contract fuzzing techniques remains lacking. In this paper, we fill this gap by: 1) providing a comprehensive review of current research in contract fuzzing, and 2) conducting an in-depth empirical study to evaluate state-of-the-art contract fuzzers' usability. To guarantee a fair evaluation, we employ a carefully-labeled benchmark and introduce a set of pragmatic performance metrics, evaluating fuzzers from five complementary perspectives. Based on our findings, we provide direction for the future research and development of contract fuzzers.

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Section: Background 21 Smart Contractmentioning

confidence: 99%

Are We There Yet? Unraveling the State-of-the-Art Smart Contract Fuzzers

Wu,

Li,

Yan

et al. 2024

Proceedings of the IEEE/ACM 46th International Conference on Software Engineering

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show abstract

“…Type Inference. SIGREC [11] presents an automatic method for function signature recovery, which includes the identification of the function arguments' types via analyzing how the CALLDATA is processed by the contract's code. However, as the primary scope of SIGREC is to extract the function signatures of smart contracts with unknown ABIs, we consider it orthogonal to the work presented in this paper.…”

Section: Related Workmentioning

confidence: 99%

Not your Type! Detecting Storage Collision Vulnerabilities in Ethereum Smart Contracts

Ruaro,

Gritti,

McLaughlin

et al. 2024

Proceedings 2024 Network and Distributed System Security Symposium

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In recent years, the Ethereum blockchain has seen significant growth and adoption. One of the key factors of its success is the possibility to run immutable programs known as smart contracts. Smart contracts allow for the automatic manipulation of digital assets and play a central role in the new decentralized finance (DeFi) ecosystem. With the growth of DeFi, the interactions between smart contracts have become increasingly complex, enabling advanced financial protocols and applications. However, bugs in smart contract interactions are also a common cause of critical vulnerabilities that result in considerable financial losses.In this paper, we study and detect a type of cross-contract vulnerability known as a storage collision. A smart contract uses storage to persistently store its data on the blockchain. Typically, each contract has its own separate storage. However, it is also possible that two smart contracts share their storage (using a delegate call). Unfortunately, when these two contracts have different understandings of the types/semantics of their shared storage, a storage collision vulnerability can occur. This may lead to unexpected behavior such as denial of service (frozen funds), privilege escalation, and theft of financial assets.To detect and investigate the impact of storage collision vulnerabilities at scale, we propose CRUSH, a novel analysis system that discovers these flaws and synthesizes proof-of-concept exploits. We leverage CRUSH to perform a large-scale analysis of 14,237,696 smart contracts deployed on the Ethereum blockchain since its genesis. CRUSH identifies 14,891 potentially vulnerable contracts and automatically synthesizes an end-to-end exploit for 956 of them. Our system uncovers more than $6 million of novel, previously unreported potential financial damage caused by storage collision vulnerabilities.

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“…For example, tools including EthIR (Albert et al, 2018), EtherSolve (Contro et al, 2021) and Mythril (Mueller, 2018) with the aid of a symbolic execution engine to explore the traces of complied smart contracts. SigRec (Chen et al, 2022) is proposed by leveraging type-aware symbolic execution to detect known patterns used by Solidity compiler to recover function signatures automatically. Other works rely on program analysis.…”

Section: Decompilers For Smart Contractsmentioning

confidence: 99%

Neural-FEBI: Accurate Function Identification in Ethereum Virtual Machine Bytecode

He¹,

Li²,

Wang³

et al. 2023

Preprint

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Millions of smart contracts have been deployed onto the Ethereum platform, posing potential attack subjects. Therefore, analyzing contract binaries is vital since their sources are unavailable, involving identification comprising function entry identification and detecting its boundaries. Such boundaries are critical to many smart contract applications, e.g. reverse engineering and profiling. Unfortunately, it is challenging to identify functions from these stripped contract binaries due to the lack of internal function call statements and the compiler-inducing instruction reshuffling. Recently, several existing works excessively relied on a set of handcrafted heuristic rules which impose several faults. To address this issue, we propose a novel neural network-based framework for EVM bytecode Function Entries and Boundaries Identification (neural-FEBI) that does not rely on a fixed set of handcrafted rules. Instead, it used a two-level bi-Long Short-Term Memory network and a Conditional Random Field network to locate the function entries. The suggested framework also devises a control flow traversal algorithm to determine the code segments reachable from the function entry as its boundary. Several experiments on 38,996 publicly available smart contracts collected as binary demonstrate that neural-FEBI confirms the lowest and highest F1-scores for the function entries identification task across different datasets of 88.3 to 99.7, respectively. Its performance on the function boundary identification task is also increased from 79.4% to 97.1% compared with state-of-the-art. We further demonstrate that the identified function information can be used to construct more accurate intra-procedural CFGs and call graphs. The experimental results confirm that the proposed framework significantly outperforms state-of-the-art, often based on handcrafted heuristic rules.

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SigRec: Automatic Recovery of Function Signatures in Smart Contracts

Cited by 14 publications

References 41 publications

Are We There Yet? Unraveling the State-of-the-Art Smart Contract Fuzzers

Are We There Yet? Unraveling the State-of-the-Art Smart Contract Fuzzers

Not your Type! Detecting Storage Collision Vulnerabilities in Ethereum Smart Contracts

Neural-FEBI: Accurate Function Identification in Ethereum Virtual Machine Bytecode

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