Smart Contract Vulnerabilities Research Articles

A Comparative Evaluation of Deep Learning Techniques for Smart Contract Vulnerability Classification

A Comparative Evaluation of Deep Learning Techniques for Smart Contract Vulnerability Classification

When ChatGPT Meets Smart Contract Vulnerability Detection: How Far Are We?

With the development of blockchain technology, smart contracts have become an important component of blockchain applications. Despite their crucial role, the development of smart contracts may introduce vulnerabilities and potentially lead to severe consequences, such as financial losses. Meanwhile, large language models, represented by ChatGPT, have gained great attentions, showcasing great capabilities in code analysis tasks. In this paper, we presented an empirical study to investigate the performance of ChatGPT in identifying smart contract vulnerabilities. Initially, we evaluated ChatGPT's effectiveness using a publicly available smart contract dataset. Our findings discover that while ChatGPT achieves a high recall rate, its precision in pinpointing smart contract vulnerabilities is limited. Furthermore, ChatGPT's performance varies when detecting different vulnerability types. We delved into the root causes for the false positives generated by ChatGPT, and categorized them into four groups. Second, by comparing ChatGPT with other state-of-the-art smart contract vulnerability detection tools, we found that ChatGPT's F-score is lower than others for 3 out of the 7 vulnerabilities. In the case of the remaining 4 vulnerabilities, ChatGPT exhibits a slight advantage over these tools. Finally, we analyzed the limitation of ChatGPT in smart contract vulnerability detection, revealing that the robustness of ChatGPT in this field needs to be improved from two aspects: its uncertainty in answering questions; and the limited length of the detected code. In general, our research provides insights into the strengths and weaknesses of employing large language models, specifically ChatGPT, for the detection of smart contract vulnerabilities.

IDENTIFICATION AND LOCALIZATION OF VULNERABILITIES IN SMART CONTRACTS USING ATTENTION VECTORS ANALYSIS IN A BERT-BASED MODEL

Context. With the development of blockchain technology and the increasing use of smart contracts, which are automatically executed in blockchain networks, the significance of securing these contracts has become extremely relevant. Traditional code auditing methods often prove ineffective in identifying complex vulnerabilities, which can lead to significant financial losses. For example, the reentrancy vulnerability that led to the DAO attack in 2016 resulted in the loss of 3.6 million ethers and the split of the Ethereum blockchain network. This underscores the necessity for early detection of vulnerabilities. Objective. The objective of this work is to develop and test an innovative approach for identifying and localizing vulnerabilities in smart contracts based on the analysis of attention vectors in a model using BERT architecture. Method. The methodology described includes data preparation and training a transformer-based model for analyzing smart contract code. The proposed attention vector analysis method allows for the precise identification of vulnerable code segments. The use of the CodeBERT model significantly improves the accuracy of vulnerability identification compared to traditional methods. Specifically, three types of vulnerabilities are considered: reentrancy, timestamp dependence, and tx.origin vulnerability. The data is preprocessed, which includes the standardization of variables and the simplification of functions. Results. The developed model demonstrated a high F-score of 95.51%, which significantly exceeds the results of contemporary approaches, such as the BGRU-ATT model with an F-score of 91.41%. The accuracy of the method in the task of localizing reentrancy vulnerabilities was 82%. Conclusions. The experiments conducted confirmed the effectiveness of the proposed solution. Prospects for further research include the integration of more advanced deep learning models, such as GPT-4 or T5, to improve the accuracy and reliability of vulnerability detection, as well as expanding the dataset to cover other smart contract languages, such as Vyper or LLL, to enhance the applicability and efficiency of the model across various blockchain platforms. Thus, the developed CodeBERT-based model demonstrates high results in detecting and localizing vulnerabilities in smart contracts, which opens new opportunities for research in the field of blockchain platform security.

SmartLLMSentry: A Comprehensive LLM Based Smart Contract Vulnerability Detection Framework

Smart contracts are essential for managing digital assets in blockchain networks, highlighting the need for effective security measures. This paper introduces SmartLLMSentry, a novel framework that leverages large language models (LLMs), specifically ChatGPT with in-context training, to advance smart contract vulnerability detection. Traditional rule-based frameworks have limitations in integrating new detection rules efficiently. In contrast, SmartLLMSentry utilizes LLMs to streamline this process. We created a specialized dataset of five randomly selected vulnerabilities for model training and evaluation. Our results show an exact match accuracy of 91.1% with sufficient data, although GPT-4 demonstrated reduced performance compared to GPT-3 in rule generation. This study illustrates that SmartLLMSentry significantly enhances the speed and accuracy of vulnerability detection through LLM-driven rule integration, offering a new approach to improving Blockchain security and addressing previously underexplored vulnerabilities in smart contracts.

FlawCheck: Detecting Smart Contract Vulnerabilities Based on Symbolic Execution

ABSTRACTSmart contracts are turing‐complete computer programs running on blockchains. Like traditional programs, smart contracts are also vulnerable. However, unlike traditional programs, it is very difficult to modify smart contracts once they are deployed on the blockchain. Therefore, reducing potential vulnerabilities in contracts before deployment to the blockchain is very important. The existing smart contract detection tools mostly fail to fully consider the complex control flow relationships within smart contracts, leading to false positives and false negatives. To address these problems, we propose FlawCheck, a vulnerability detection tool based on symbolic execution. First, it compiles smart contract source code into bytecode. Then, it disassembles bytecode into an opcode sequence, building the dependencies of contract control flow. Next, it performs preliminary analysis on the information generated during the simulated execution on the Ethereum virtual machine to identify suspicious vulnerability paths. Finally, it detects these suspicious paths by using symbolic execution. We verified that FlawCheck can detect five types of smart contract vulnerabilities. Experimental results on a dataset of 13016 real contracts shows that FlawCheck has higher accuracy than other tools.

Contractsentry: a static analysis tool for smart contract vulnerability detection

Contractsentry: a static analysis tool for smart contract vulnerability detection

Advanced Security Auditing Methods for Solidity-Based Smart Contracts

The development of smart contracts remains in its early stages, with significant differences in underlying programming languages and application platforms resulting in a lack of standardization. This lack of standardization increases the susceptibility to vulnerabilities and associated financial losses. To address security vulnerabilities in smart contracts on the Ethereum blockchain platform, this paper proposes a security audit method based on formal verification. The method integrates an input module, static analysis module, formal verification module, analog execution module, and report and recommendation module, which can accurately discover the security vulnerabilities and logical flaws of smart contracts through formal verification and other analysis techniques, thus realizing correctness detection. During the experiment, the method detects 8 types of common vulnerabilities in 148 smart contracts and marks 21 smart contracts with vulnerabilities. After manual review and analysis, it is found that 17 of these 21 marked smart contracts do have security vulnerabilities. The experimental results show that the proposed method can accurately detect security vulnerabilities and logic flaws in smart contracts through formal verification and other analysis techniques before smart contracts are deployed, thus significantly improving the security of smart contracts and reducing the economic losses that may be caused by code defects.

Isabelle/Solidity: A deep embedding of Solidity in Isabelle/HOL

Smart contracts are computer programs designed to automate legal agreements. They are usually developed in a high-level programming language, the most popular of which is Solidity. Every day, hundreds of thousands of new contracts are deployed managing millions of dollars’ worth of transactions. As for every computer program, smart contracts may contain bugs which can be exploited. However, since smart contracts are often used to automate financial transactions, such exploits may result in huge economic losses. In general, it is estimated that since 2019, more than $5B was stolen due to vulnerabilities in smart contracts. This paper addresses the issue of smart contract vulnerabilities by introducing an executable denotational semantics for Solidity within the Isabelle/HOL interactive theorem prover. This formal semantics serves as the basis for an interactive program verification environment for Solidity smart contracts. To evaluate our semantics, we integrate grammar-based fuzzing with symbolic execution to automatically test it against the Solidity reference implementation. The paper concludes by showcasing the formal verification of Solidity programs, exemplified through the verification of a basic Solidity token.

MVD-HG: multigranularity smart contract vulnerability detection method based on heterogeneous graphs

Smart contracts have significant losses due to various types of vulnerabilities. However, traditional vulnerability detection methods rely extensively on expert rules, resulting in low detection accuracy and poor adaptability to novel attacks. To address these problems, in this paper, deep learning methods are combined with smart contract vulnerability code detection approaches. syntax trees (ASTs), which are special isomorphic graph structures, are an important bridge between source code and graph neural networks. By learning the AST, the model can understand the semantics of the source code. Moreover, graph neural networks have an increasing ability to address complex heterogeneous graphs. Therefore, control flow graphs are fused with data flow graphs on the basis of the ASTs to build heterogeneous graphs with richer code semantics. Furthermore, multigranularity analysis of the vulnerability detection results is performed, including coarse-grained contract-level vulnerability detection and fine-grained line-level vulnerability detection. Through this multigranularity detection approach, vulnerabilities in contracts can be identified and analysed more comprehensively, providing a richer perspective and more solutions for vulnerability detection. The experimental results show that the proposed multigranularity vulnerability detection method based on heterogeneous graphs (MVD-HG) improves both the accuracy and range of the detected vulnerability types in contract-level vulnerability detection tasks; moreover, in the line-level vulnerability detection task, the MVD-HG model achieves significant results and addresses the shortcomings of existing methods. In addition, based on code generation methods used in related fields, a data enhancement method based on the source code is developed, which effectively expands the experimental dataset to address the reduced credibility of the results due to insufficient amounts of data.

Automated Repair of Smart Contract Vulnerabilities: A Systematic Literature Review

The substantial value held by smart contracts (SCs) makes them an enticing target for malicious attacks. The process of fixing vulnerabilities in SCs is intricate, primarily due to the immutability of blockchain technology. This research paper introduces a systematic literature review (SLR) that evaluates rectification systems designed to patch vulnerabilities in SCs. Following the guidelines set forth by the PRISMA statement, this SLR meticulously reviews a total of 31 papers. In this context, we classify recently published SC automated repair frameworks based on their methodologies for automatic program repair (APR), rewriting strategies, and tools for vulnerability detection. We argue that automated patching enhances the reliability and adoption of SCs, thereby allowing developers to promptly address identified vulnerabilities. Furthermore, existing automated repair tools are capable of addressing only a restricted range of vulnerabilities, and in some cases, patches may not be effective in preventing the targeted vulnerabilities. Another key point that should be taken into account is the simplicity of the patch and the gas consumption of the modified program. Alternatively, large language models (LLMs) have opened new avenues for automatic patch generation, and their performance can be improved by innovative methodologies.

FunFuzz: A Function-Oriented Fuzzer for Smart Contract Vulnerability Detection with High Effectiveness and Efficiency

FunFuzz: A Function-Oriented Fuzzer for Smart Contract Vulnerability Detection with High Effectiveness and Efficiency

Integration of NLP and machine learning methods for smart contract security: a comparison with traditional approaches

In modern blockchain systems, smart contracts are one of the most critical components for ensuring the automated execution of agreements without the need for intermediaries. However, smart contracts written in languages like Solidity may contain vulnerabilities that can be exploited by malicious actors to steal funds or manipulate assets. Given the increasing number of attacks on smart contracts, the development of effective methods for detecting such vulnerabilities is crucial. Traditional approaches to detecting vulnerabilities in smart contracts include symbolic execution, fuzzing, formal verification, and pattern matching. These methods have their advantages but face several challenges, such as high resource consumption, limitations in detecting new types of vulnerabilities, and difficulties in scaling to large contracts. As a result, there is a need to introduce new approaches, such as natural language processing (NLP) and machine learning, which can address these challenges more effectively. In this study, an NLP-based method was explored, using Word2Vec to convert smart contract code into vector representations, allowing for better analysis of the semantic relationships between elements of the code. These vector representations are then fed into a bidirectional recurrent neural network with GRU blocks and an attention mechanism. This approach allows the model to focus on the most important parts of the code and improve the accuracy of vulnerability detection. The comparative analysis showed that NLP-based methods significantly outperform traditional approaches in all key metrics. In particular, the GRU model with an attention mechanism demonstrated high results in accuracy, recall, and F-measure, making it effective for detecting complex vulnerabilities such as reentrancy. Furthermore, the NLP-based approach is capable of adapting to new types of attacks thanks to training on large datasets. Thus, the integration of NLP and machine learning represents a promising direction for enhancing the security of smart contracts. Future research can focus on improving these approaches, particularly through the implementation of advanced models such as transformers.

Survey on Quality Assurance of Smart Contracts

As blockchain technology continues to advance, the secure deployment of smart contracts has become increasingly prevalent, underscoring the critical need for robust security measures. This surge in usage has led to a rise in security breaches, often resulting in substantial financial losses for users. This paper presents a comprehensive survey of smart contract quality assurance, from understanding vulnerabilities to evaluating the effectiveness of detection tools. Our work is notable for its innovative classification of forty smart contract vulnerabilities, mapping them to established attack patterns. We further examine nine defense mechanisms, assessing their efficacy in mitigating smart contract attacks. Furthermore, we develop a labeled dataset as a benchmark encompassing ten common vulnerability types, which serves as a critical resource for future research. We also conduct comprehensive experiments to evaluate fourteen vulnerability detection tools, providing a comparative analysis that highlights their strengths and limitations. In summary, this survey synthesizes state-of-the-art knowledge in smart contract security, offering practical recommendations to guide future research and foster the development of robust security practices in the field.

Smart Contract Vulnerability Detection Based on Automated Feature Extraction and Feature Interaction

Smart Contract Vulnerability Detection Based on Automated Feature Extraction and Feature Interaction

Unraveling the Potential of Decentralized Finance: A Comprehensive Analysis of Opportunities, Risks, and Future Trends

The financial sector is going through a major transformation and evolution with the emergence of Decentralized Finance (DeFi). It is a modern concept that is reshaping the traditional financial landscape and thus changing traditional financial structures. This research delves into the term “Decentralized Finance” by exploring its various advantages, associated challenges, and future patterns and trends. Leveraging blockchain technology, DeFi offers new financial services without intermediaries that in turn enhance financial inclusion, autonomy, and economic empowerment of individuals, though this also entails enormous risks and challenges, including vulnerability to smart contract vulnerabilities, regulatory uncertainty, and market volatility. This research also attempts, through a systematic literature review, to identify the most significant opportunities for DeFi, such as democratized access to financial services, lower transaction fees, and increased liquidity. Additionally, this research highlights the key challenges facing the development of DeFi, such as limitations on its ability to handle large transactions, concerns about user data protection, and the need to adhere to regulatory standards. The paper concludes with a view and insights into the future of DeFi, emphasizing the importance of regulatory clarity, technological innovation, and community governance in shaping the trajectory of DeFi. This analysis contributes to gaining a deeper understanding of the potential and capabilities of DeFi, and provides stakeholders with indispensable insights to navigate the dynamics of DeFi.

DFier: A directed vulnerability verifier for Ethereum smart contracts

Smart contracts are self-executing digital agreements that automatically enforce the terms between parties, playing a crucial role in blockchain systems. However, due to the potential losses of digital assets caused by vulnerabilities, the security issues of Ethereum smart contracts have garnered widespread attention. To address this, researchers have developed various techniques to detect vulnerabilities in smart contracts, with fuzzing techniques achieving promising results. Nonetheless, current fuzzers are unable to effectively exercise suspicious targets because they overlook two key factors: comprehensively exploring all paths to the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), which elaborates effective transaction sequences with directed inputs for the fuzzer. This focuses on exploring target paths and automatically validating whether the specified locations are vulnerable. Specifically, DFier employs static analysis to help locate target paths, facilitating their comprehensive exploration. Additionally, we devise three heuristic strategies to enable our fuzzing technique to generate directed inputs that effectively validate the targets. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius), while the performance losses are in an acceptable range.

Vulnerability detection techniques for smart contracts: A systematic literature review

The number of applications supported by blockchain smart contracts has been greatly increasing in recent years, with smart contracts now being used across several domains, such as the music industry, finance, and retail, to name a few. Despite being used in business-critical contexts, the number of security vulnerabilities in smart contracts has also been increasing, with many of them being exploited and resulting in huge financial and reputation losses. This is despite the enormous effort that is being placed into the research and development of vulnerability detection tools and techniques, which have also greatly increased in number and type in the last few years. Motivated by the recent increase in both vulnerabilities and vulnerability detection techniques, this paper reviews the latest research in smart contract vulnerability detection, emphasizing the techniques being used, the vulnerabilities targeted, and the characteristics of the dataset used for evaluating the technique. We mapped the vulnerabilities against two common vulnerability classification schemes (DASP and SWC) and performed a consolidated analysis. We identified the current research trends and gaps in each technique and highlighted future research opportunities in the field.

Efficiently Detecting Reentrancy Vulnerabilities in Complex Smart Contracts

Reentrancy vulnerability as one of the most notorious vulnerabilities, has been a prominent topic in smart contract security research. Research shows that existing vulnerability detection presents a range of challenges, especially as smart contracts continue to increase in complexity. Existing tools perform poorly in terms of efficiency and successful detection rates for vulnerabilities in complex contracts. To effectively detect reentrancy vulnerabilities in contracts with complex logic, we propose a tool named SliSE. SliSE’s detection process consists of two stages: Warning Search and Symbolic Execution Verification . In Stage 1, SliSE utilizes program slicing to analyze the Inter-contract Program Dependency Graph (I-PDG) of the contract, and collects suspicious vulnerability information as warnings. In Stage 2, symbolic execution is employed to verify the reachability of these warnings, thereby enhancing vulnerability detection accuracy. SliSE obtained the best performance compared with eight state-of-the-art detection tools. It achieved an F1 score of 78.65%, surpassing the highest score recorded by an existing tool of 9.26%. Additionally, it attained a recall rate exceeding 90% for detection of contracts on Ethereum. Overall, SliSE provides a robust and efficient method for detection of Reentrancy vulnerabilities for complex contracts.

Smart contract vulnerability detection using wide and deep neural network

Smart contracts, integral to blockchain technology, automate agreements without intermediaries, ensuring transparency and security across various sectors. However, the immutable nature of blockchain exposes deployed contracts to potential risks if they contain vulnerabilities. Current approaches, including symbolic execution and graph-based machine learning, aim to ensure smart contract security. However, these methods suffer from limitations such as high false positive rates, heavy reliance on trained data, and over-generalization.The goal of this paper is to investigate the application of Wide and Deep Neural Networks in identifying vulnerabilities within smart contracts. We introduce WIDENNET, a method based on deep neural networks, designed to detect reentrancy and timestamp dependence vulnerabilities in smart contracts. Our approach involves extracting bytecodes from the contracts and converting them into Operational Codes (OPCODES), which are then transformed into distinct vector representations. These vectors are subsequently fed into the neural network to extract both complex and simple patterns for vulnerability detection. Testing on real-world datasets yielded an average accuracy of 83.07% and a precision of 83.13%. Our method offers a potential solution to mitigate vulnerabilities in blockchain applications.

Smart contract vulnerabilities detection with bidirectional encoder representations from transformers and control flow graph

Smart contract vulnerabilities detection with bidirectional encoder representations from transformers and control flow graph