EOSIOAnalyzer: An Effective Static Analysis Vulnerability Detection Framework for EOSIO Smart Contracts

Li, Wenyuan; He, Jiahao; Zhao, Gansen; Yang, Jinji; Li, Shuangyin; Lai, Ruilin; Li, Ping; Tang, Hua; Luo, Haoyu; Zhou, Ziheng

doi:10.1109/compsac54236.2022.00124

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…EOSIOAnalyzer. EOSIOAnalyzer [65] detects vulnerabilities in EOSIO smart contracts by analyzing the ICFG of the program. The ICFG is the combination of the CFG and the Call Graph (CG) of the program, allowing EOSIOAnalyzer to analyze the data propagation relationships between functions when they call each other.…”

Section: Static Analysismentioning

confidence: 99%

“…However, variations in evaluation strategies can still impact the validity of the results. For example, EVulHunter reported an F 1 -score of 93%, but other studies found its F 1 score to be 17% and 23% [18,65]. However, using the results from these complementary studies may further threaten the validity of the results as they may contain implementation bugs.…”

Section: Limitationsmentioning

confidence: 99%

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SoK: Analysis Techniques for WebAssembly

Harnes,

Morrison

2024

Future Internet

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WebAssembly is a low-level bytecode language that enables high-level languages like C, C++, and Rust to be executed in the browser at near-native performance. In recent years, WebAssembly has gained widespread adoption and is now natively supported by all modern browsers. Despite its benefits, WebAssembly has introduced significant security challenges, primarily due to vulnerabilities inherited from memory-unsafe source languages. Moreover, the use of WebAssembly extends beyond traditional web applications to smart contracts on blockchain platforms, where vulnerabilities have led to significant financial losses. WebAssembly has also been used for malicious purposes, like cryptojacking, where website visitors’ hardware resources are used for crypto mining without their consent. To address these issues, several analysis techniques for WebAssembly binaries have been proposed. This paper presents a systematic review of these analysis techniques, focusing on vulnerability analysis, cryptojacking detection, and smart contract security. The analysis techniques are categorized into static, dynamic, and hybrid methods, evaluating their strengths and weaknesses based on quantitative data. Our findings reveal that static techniques are efficient but may struggle with complex binaries, while dynamic techniques offer better detection at the cost of increased overhead. Hybrid approaches, which merge the strengths of static and dynamic methods, are not extensively used in the literature and emerge as a promising direction for future research. Lastly, this paper identifies potential future research directions based on the state of the current literature.

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Section: Static Analysismentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

SoK: Analysis Techniques for WebAssembly

Harnes,

Morrison

2024

Future Internet

View full text Add to dashboard Cite

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“…Moreover, in EOSIO smart contracts, not checking the transaction notification to ensure that the transaction's recipient is a correct contract address can lead to a forged transfer notification or fake receipt [45,94,97,121]. To detect these issues, address integrity asserts that i) hard-coded addresses are used if known before deployment to call external contracts, ii) the 'new' keyword is used for newly created contract instances to explicitly declare that these contracts are not yet live on the chain, and iii) the address of the transaction recipient is always checked when a transfer notification is received.…”

Section: Contractmentioning

confidence: 99%

“…If a contract does not do that, it can deposit invalid tokens, resulting in a fake deposit vulnerability [100]. During a transfer, if an EOSIO contract does not check that the EOS (or token) was generated using standard code (eosio.token), it results in the generation of fake EOS [45,94,97,121]. Validity of deposits addresses these issues by asserting that a contract implements tokens using technical standards only and does not deposit invalid tokens into the exchange.…”

Section: Validity Of Depositsmentioning

confidence: 99%

“…Unfortunately, since they are predictable, a miner holding a stake in a contract could gain an advantage by guessing a random number or by choosing a suitable timestamp for a block she is mining [23,102]. Similarly, in EOSIO smart contracts, block information can be obtained through tapos_block_prefix() or tapos_block_num() and used as the judgment condition of control flow [121]. Using such variables can cause dependence on predictable variables, bad-randomness, block number, and timestamp dependency [22, 45, 84, 104, 121, 131-133, 148, 212, 216, 239].…”

Section: Value Unpredictabilitymentioning

confidence: 99%

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Pre-deployment Analysis of Smart Contracts -- A Survey

Munir¹,

Taha²

2023

Preprint

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Smart contracts are programs that execute transactions involving independent parties and cryptocurrencies. As programs, smart contracts are susceptible to a wide range of errors and vulnerabilities. Such vulnerabilities can result in significant losses. Furthermore, by design, smart contract transactions are irreversible. This creates a need for methods to ensure the correctness and security of contracts pre-deployment. Recently there has been substantial research into such methods. The sheer volume of this research makes articulating state-of-the-art a substantial undertaking. To address this challenge, we present a systematic review of the literature. A key feature of our presentation is to factor out the relationship between vulnerabilities and methods through properties. Specifically, we enumerate and classify smart contract vulnerabilities and methods by the properties they address. The methods considered include static analysis as well as dynamic analysis methods and machine learning algorithms that analyze smart contracts before deployment.Several patterns about the strengths of different methods emerge through this classification process.CCS Concepts: • General and reference → Surveys and overviews; • Software and its engineering → Automated static analysis; Dynamic analysis; Software verification.

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OpenSCV: an open hierarchical taxonomy for smart contract vulnerabilities

Vidal,

Ivaki,

Laranjeiro

2024

Empir Software Eng

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Smart contracts are nowadays at the core of most blockchain systems. Like all computer programs, smart contracts are subject to the presence of residual faults, including severe security vulnerabilities. However, the key distinction lies in how these vulnerabilities are addressed. In smart contracts, when a vulnerability is identified, the affected contract must be terminated within the blockchain, as due to the immutable nature of blockchains, it is impossible to patch a contract once deployed. In this context, research efforts have been focused on proactively preventing the deployment of smart contracts containing vulnerabilities, mainly through the development of vulnerability detection tools. Along with these efforts, several heterogeneous vulnerability classification schemes appeared (e.g., most notably DASP and SWC). At the time of writing, these are mostly outdated initiatives, even though new smart contract vulnerabilities are consistently uncovered. In this paper, we propose OpenSCV, a new and Open hierarchical taxonomy for Smart Contract vulnerabilities, which is open to community contributions and matches the current state of the practice while being prepared to handle future modifications and evolution. The taxonomy was built based on the analysis of the existing research on vulnerability classification, community-maintained classification schemes, and research on smart contract vulnerability detection. We show how OpenSCV covers the announced detection ability of the current vulnerability detection tools and highlight its usefulness in smart contract vulnerability research. To validate OpenSCV, we performed an expert-based analysis wherein we invited multiple experts engaged in smart contract security research to participate in a questionnaire. The feedback from these experts indicated that the categories in OpenSCV are representative, clear, easily understandable, comprehensive, and highly useful. Regarding the vulnerabilities, the experts confirmed that they are easily understandable.

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EOSIOAnalyzer: An Effective Static Analysis Vulnerability Detection Framework for EOSIO Smart Contracts

Cited by 5 publications

References 13 publications

SoK: Analysis Techniques for WebAssembly

SoK: Analysis Techniques for WebAssembly

Pre-deployment Analysis of Smart Contracts -- A Survey

OpenSCV: an open hierarchical taxonomy for smart contract vulnerabilities

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