Zhibo Wang, Liu Guoming, Hongzhen Xu, Shengyu You · 6 authors
Smart contracts play an essential role in the handling and management of digital assets, where vulnerabilities can lead to severe security issues and financial losses. Current detection techniques are largely limited to identifying single vulnerabilities and lack comprehensive identification capabilities for multiple vulnerabilities that may coexist in smart contracts. To address this challenge, we propose a novel multi-label vulnerability detection model that integrates extractive summarization methods with deep learning, referred to as Ext-ttg. The model begins by preprocessing the data using an extractive summarization approach, followed by the deployment of a custom-built deep learning model to detect vulnerabilities in smart contracts. Experimental results demonstrate that our method achieves commendable performance across various metrics, establishing the effectiveness of the proposed approach in the multi-vulnerability detection tasks within smart contracts.
Francisco Javier Aguilar Feijóo, Diego Fernando Andaluz Espinosa
This research aims to determine the incidence of computer attacks on servers with the Linux operating system of local government entities. The study is limited to the decentralized autonomous government (GAD) of the Ecuadorian Amazon. Initially, the most common computer attacks that have affected organizations in recent years were determined using statistical reports from important computer security companies positioned as leaders in Gartner’s magic quadrant. Phishing and distributed denial of service (DDoS) attacks are established as computer attacks under study. Computer attacks are carried out before and after mitigation measures are established. With the help of the information systems risk analysis and management methodology (MAGERIT), the vulnerability, level of impact, and risk computer attacks cause on servers with the Linux operating system are determined. This research aims to serve as a guide to the information technology departments of local governments in implementing mechanisms that safeguard the most important asset of an organization, such as information. Keywords: computer attack, phishing, DDoS, MAGERIT, Linux. Resumen La presente investigación tiene como finalidad determinar la incidencia de los ataques informáticos en los servidores con sistema operativo Linux de entidades de gobierno local. El estudio está delimitado a un gobierno autónomo descentralizado (GAD) de la Amazonía ecuatoriana. Inicialmente se determina los ataques informáticos más comunes que han afectado a las organizaciones en los últimos años haciendo uso de reportes estadísticos de importantes empresas de seguridad informática posicionadas como líderes en el cuadrante mágico de Gartner. Se establece como ataques informáticos objeto de estudio los ataques de phishing y de denegación de servicio distribuido (DDoS). Se realizan ataques informáticos antes y después de establecer las medidas de mitigación y con la ayuda de la metodología de análisis y gestión de riesgos de los sistemas de información (MAGERIT) se determina la vulnerabilidad, el nivel de impacto y el riesgo que los ataques informáticos provocaban en los servidores con sistema operativo Linux. El presente trabajo de investigación pretende ser de gran utilidad y servir de guía a los departamentos de tecnologías de la información de gobiernos locales en la implementación de mecanismos que salvaguarden el activo más importante de una organización como lo es la información. Palabras Clave: ataque informático, phishing, ddos, magerit, linux.
Fatma S. Alrayes, Mohammed Aljebreen, MOHAMMED ALGHAMDI, Faheed A. F. Alrslani · 8 authors
Consumer electronics (CE) and the Internet of Things (IoTs) are transforming daily routines by integrating smart technology into household gadgets. IoT allows devices to link and communicate from the Internet with better functions, remote control, and automation of various complex systems simulation platforms. The quick progress in IoT technology has continuously driven the progress of further connected and intelligent CEs, shaping more smart cities and homes. Blockchain (BC) technology is emerging as a promising technology offering immutable distributed ledgers that improve the security and integrity of data. However, even with BC resilience, the IoT ecosystem remains vulnerable to Distributed Denial of Service (DDoS) attacks. In contrast, the malicious actor overwhelms the network with traffic, disrupting services and compromising device functionality. Incorporating BC with IoT infrastructure presents groundbreaking techniques to alleviate these threats. IoT networks can better detect and respond to DDoS attacks in real time by leveraging BC cryptographic techniques and decentralized consensus mechanisms, which safeguard against disruptions and enhance resilience. There must be a reliable mechanism of recognition based on adequate techniques to detect and identify whether these attacks have happened or not in the system. Artificial intelligence (A) is the most common technique that uses machine learning (ML) and deep learning (DL) to recognize cyber threats. This research presents a new Blockchain with Ensemble Deep Learning-based Distributed DoS Attack Detection (BCEDL-DDoSD) approach in the IoT platform. The primary intention of the BCEDL-DDoSD approach is to leverage BC with a DL-based attack recognition process in the IoT platform. BC technology is utilized to enable a secure data transmission process. In the BCEDL-DDoSD approach, Z-score normalization is initially employed to measure the input data. Besides, the selection of features takes place using the Fractal Wombat optimization algorithm (WOA). For attack recognition, the BCDL-DDoSD technique applies an ensemble of three models, namely denoising autoencoder (DAE), gated recurrent unit (GRU), and long short-term memory (LSTM). Lastly, an orca predator algorithm (OPA)-based hyperparameter tuning procedure has been implemented to select the parameter value of DL models. A sequence of simulations is made on the benchmark database to authorize the performance of the BCDL-DDoSD approach. The simulation results showed that the BCDL-DDoSD approach performs better than other DL techniques.
Abstract The adoption of digital assets and distributed ledger technology in finance is rapidly increasing. This adoption introduces new types of risks, currently not adequately covered in conventional risk management frameworks. This paper identifies, reviews, and categorises these risks. It draws on a systematic review of literature and classifies the vulnerabilities by layer—network layer, consensus layer, protocol layer, and enablement layer.
Current authentication schemes based on zero-knowledge proof (ZKP) still face issues such as high computation costs, low efficiency, and security assurance difficulty. Therefore, we propose a secure and efficient authentication scheme (SEAS) for large-scale IoT devices based on ZKP. In the initialization phase, the trusted authority creates prerequisites for device traceability and system security. Then, we propose a new registration method to ensure device anonymity. In the identity tracing and revocation phase, we revoke the real identity of abnormal devices by decrypting and updating group public keys, avoiding their access and reducing revocation costs. In the authentication phase, we check the arithmetic relationship between blind certificates, proofs, and other random data. We propose a new anonymous batch authentication method to effectively reduce computation costs, enhance authentication efficiency, and guarantee device authentication security. Security analysis and experimental results show that an SEAS can ensure security and effectively reduce verification time and energy costs. Its security and performance exceed existing schemes.
Open access
User Authentication and Security Systems
Advanced Steganography and Watermarking Techniques
Decompilation of binary code has arisen as a highly-important application in the space of Ethereum VM (EVM) smart contracts. Major new decompilers appear nearly every year and attain popularity, for a multitude of reverse-engineering or tool-building purposes. Technically, the problem is fundamental: it consists of recovering high-level control flow from a highly-optimized continuation-passing-style (CPS) representation. Architecturally, decompilers can be built using either static analysis or symbolic execution techniques. We present Shrnkr, a static-analysis-based decompiler succeeding the state-of-the-art Elipmoc decompiler. Shrnkr manages to achieve drastic improvements relative to the state of the art, in all significant dimensions: scalability, completeness, precision. Chief among the techniques employed is a new variant of static analysis context: shrinking context sensitivity. Shrinking context sensitivity performs deep cuts in the static analysis context, eagerly “forgetting” control-flow history, in order to leave room for further precise reasoning. We compare Shrnkr to state-of-the-art decompilers, both static-analysis- and symbolic-execution-based. In a standard benchmark set, Shrnkr scales to over 99.5% of contracts (compared to ∼95% for Elipmoc), covers (i.e., reaches and manages to decompile) 67% more code than Heimdall-rs, and reduces key imprecision metrics by over 65%, compared again to Elipmoc.
Konrad Weiss, Christof Ferreira Torres, Florian Wendland
Ethereum smart contracts are executable programs deployed on a blockchain. Once deployed, they cannot be updated due to their inherent immutability. Moreover, they often manage valuable assets that are worth millions of dollars, making them attractive targets for attackers. The introduction of vulnerabilities in programs due to the reuse of vulnerable code posted on Q&A websites such as Stack Overflow is not a new issue. However, little effort has been made to analyze the extent of this issue on deployed smart contracts. In this paper, we conduct a study on the impact of vulnerable code reuse from Q&A websites during the development of smart contracts and provide tools uniquely fit to detect vulnerable code patterns in complete and incomplete Smart Contract code. This paper proposes a pattern-based vulnerability detection tool that is able to analyze code snippets (i.e., incomplete code) as well as full smart contracts based on the concept of code property graphs. We also propose a methodology that leverages fuzzy hashing to quickly detect code clones of vulnerable snippets among deployed smart contracts. Our results show that our vulnerability search, as well as our code clone detection, are comparable to state-of-the-art while being applicable to code snippets. Our large-scale study on 18,660 code snippets reveals that 4,596 of them are vulnerable, out of which 616 can be found in 7,852 deployed smart contracts. These results highlight that the reuse of vulnerable code snippets is indeed an issue in currently deployed smart contracts.
Although Decentralized Finance (DeFi) applications facilitate tamper-proof transactions among multiple anonymous users, since attackers can access the smart contract bytecode directly, vulnerabilities in the transaction mechanism, contract code, or third-party components can be easily exploited to manipulate token prices, leading to financial losses. Since price manipulation often relies on specific states and complex trading sequences, existing detection tools have limitations in addressing this problem. In addition, to swiftly identify the root cause of an attack and implement targeted defense and remediation measures, auditors typically prioritize understanding the methodology behind the attack, emphasizing 'how' it occurred rather than simply confirming its existence. To address these problems, this paper presents a novel automatic price manipulation detection and analysis framework, named DeFort, which contains a price manipulation behavior model to guide on-chain detection, multiple price monitoring strategies to detect pools with abnormal token prices, and various profit calculation mechanisms to confirm attacks. Based on behavioral models, DeFort can automatically locate transactions and functions that cause abnormal price fluctuations and identify attackers and victims. Experimental results demonstrate that DeFort can outperform state-of-the-art price manipulation detection methods. Furthermore, after monitoring 441 real-world projects for two months, DeFort successfully detected five price manipulation attacks.
Open access
Blockchain Technology Applications and Security
Advanced Malware Detection Techniques
Advanced Steganography and Watermarking Techniques
Charlotte McCabe, Althaff Irfan Cader Mohideen, Raman Singh
Passwords are the first line of defence against preventing unauthorised access to systems and potential leakage of sensitive data. However, the traditional reliance on username and password combinations is not enough protection and has prompted the implementation of technologies such as two-factor authentication (2FA). While 2FA enhances security by adding a layer of verification, these techniques are not impervious to threats. Even with the implementation of 2FA, the relentless efforts of cybercriminals present formidable obstacles in securing digital spaces. The objective of this work is to implement blockchain technology as a form of 2FA. The findings of this work suggest that blockchain-based 2FA methods could strengthen digital security compared to conventional 2FA methods.
Smart contracts are programs that reside and execute on a blockchain, like any transaction. They are automatically executed when preprogrammed terms and conditions are met. Although the smart contract (SC) must be presented in the blockchain for the integrity of data and transactions stored within it, it is highly exposed to several vulnerabilities attackers exploit to access the data. In this paper, classification and detection of vulnerabilities targeting smart contracts are performed using deep learning algorithms over two datasets containing 12,253 smart contracts. These contracts are converted into RGB and Grayscale images and then inserted into Residual Network (ResNet50), Visual Geometry Group-19 (VGG19), Dense Convolutional Network (DenseNet201), k-nearest Neighbors (KNN), and Random Forest (RF) algorithms for binary and multi-label classification. A comprehensive analysis is conducted to detect and classify vulnerabilities using different performance metrics. The performance of these algorithms was outstanding, accurately classifying vulnerabilities with high F1 scores and accuracy rates. For binary classification, RF emerged in RGB images as the best algorithm based on the highest F1 score of 86.66% and accuracy of 86.66%. Moving on to multi-label classification, VGG19 stood out in RGB images as the standout algorithm, achieving an impressive accuracy of 89.14% and an F1 score of 85.87%. To the best of our knowledge, and according to the available literature, this study is the first to investigate binary classification of vulnerabilities targeting Ethereum smart contracts, and the experimental results of the proposed methodology for multi-label vulnerability classification outperform existing literature.
In recent years, a more advanced form of phishing has arisen on Ethereum, surpassing early-stage, simple transaction phishing.This new form, which we refer to as payload-based transaction phishing (PTXPHISH), manipulates smart contract interactions through the execution of malicious payloads to deceive users.PTXPHISH has rapidly emerged as a significant threat, leading to incidents that caused losses exceeding $70 million in 2023 reports.Despite its substantial impact, no previous studies have systematically explored PTXPHISH.In this paper, we present the first comprehensive study of the PTXPHISH on Ethereum.Firstly, we conduct a long-term data collection and put considerable effort into establishing the first ground-truth PTXPHISH dataset, consisting of 5,000 phishing transactions.Based on the dataset, we dissect PTXPHISH, categorizing phishing tactics into four primary categories and eleven sub-categories.Secondly, we propose a rule-based multidimensional detection approach to identify PTXPHISH, achieving an F1-score of over 99% and processing each block in an average of 390 ms.Finally, we conduct a large-scale detection spanning 300 days and discover a total of 130,637 phishing transactions on Ethereum, resulting in losses exceeding $341.9 million.Our in-depth analysis of these phishing transactions yielded valuable and insightful findings.Scammers consume approximately 13.4 ETH daily, which accounts for 12.5% of the total Ethereum gas, to propagate address poisoning scams.Additionally, our analysis reveals patterns in the cash-out process employed by phishing scammers, and we find that the top five phishing organizations are responsible for 40.7% of all losses.Furthermore, our work has made significant contributions to mitigating real-world threats.We have reported 1,726 phishing addresses to the community, accounting for 42.7% of total community contributions during the same period.Additionally, we have sent 2,539 on-chain alert messages, assisting 1,980 victims.This research serves as a valuable reference in combating the emerging PTXPHISH and safeguarding users' assets.
Kai Wang, Michael Wen Tong, Jun Pang, Jitao Wang · 5 authors
Recently, there is a surge in ransomware activities that encrypt users’ sensitive data and demand bitcoins for ransom payments to conceal the criminal’s identity. It is crucial for regulatory agencies to identify as many ransomware addresses as possible to accurately estimate the impact of these ransomware activities. However, existing methods for detecting ransomware addresses rely primarily on time-consuming data collection and clustering heuristics, and they face two major issues: (1) The features of an address itself are insufficient to accurately represent its activity characteristics, and (2) the number of disclosed ransomware addresses is extremely less than the number of unlabeled addresses. These issues lead to a significant number of ransomware addresses being undetected, resulting in a substantial underestimation of the impact of ransomware activities. To solve the above two issues, we propose an optimized ransomware address detection method based on Bitcoin transaction relationships, named XRAD , to detect more ransomware addresses with high performance. To address the first one, we present a cascade feature extraction method for Bitcoin transactions to aggregate features of related addresses after exploring transaction relationships. To address the second one, we build a classification model based on Positive-unlabeled learning to detect ransomware addresses with high performance. Extensive experiments demonstrate that XRAD significantly improves average accuracy, recall, and F1 score by 15.07%, 19.71%, and 34.83%, respectively, compared to state-of-the-art methods. In total, XRAD detects 120,335 ransomware activities from 2009 to 2023, revealing a development trend and average ransom payment per year that aligns with three reports by FinCEN, Chainalysis, and Coveware.
Smart contracts are a major tool in Ethereum transactions. Therefore hackers can exploit them by adding code vulnerabilities to their sources and using these vulnerabilities for performing malicious transactions. This paper presents two successful approaches for detecting malicious contracts: one uses opcode and relies on GPT2 and the other uses the Solidity source and a LORA fine-tuned CodeLlama. Finally, we present an XGBOOST model that combines gas properties and Hexa-decimal signatures for detecting malicious transactions. This approach relies on early assumptions that maliciousness is manifested by the uncommon usage of the contracts' functions and the effort to pursue the transaction.
The metaverse, known as the next-generation 3D Internet, represents virtual environments that mirror the physical world. It is supported by innovative technologies such as digital twins and extended reality (XR), which elevate user experiences across various fields. However, the metaverse also introduces significant cybersecurity and privacy challenges that remain underexplored. Due to its complex multi-tech infrastructure, the metaverse requires sophisticated, automated, and intelligent cybersecurity measures to mitigate emerging threats effectively. Therefore, this paper is the first to explore Artificial Intelligence (AI)-driven cybersecurity techniques for the metaverse, examining academic and industrial perspectives. First, we provide an overview of the metaverse, presenting a detailed system model, diverse use cases, and insights into its current industrial status. We then present attack models and cybersecurity threats derived from the unique characteristics and technologies of the metaverse. Next, we review AI-driven cybersecurity solutions based on three critical aspects: User authentication, intrusion detection systems (IDS), and the security of digital assets, specifically for Blockchain and Non-fungible Tokens (NFTs). Finally, we highlight challenges and suggest future research opportunities to enhance metaverse security, privacy, and digital asset transactions.
Aria Seo, Young-Tak Kim, Ji Seok Yang, YangSun Lee · 5 authors
Smart contracts on blockchain platforms are susceptible to security issues that can lead to significant financial losses. This study converts the Solidity code into abstract syntax trees and generates control flow graphs and data flow graphs. These graphs train a graph convolutional network model to detect security weaknesses. The proposed system outperforms traditional tools, achieving higher accuracy, recall, precision, and F1 scores when detecting weaknesses such as integer overflow/underflow, reentrancy, delegate call to the untrusted callee, and time-based issues. This study demonstrates that leveraging control and data flow analysis with graph neural networks significantly enhances smart contract security and provides a robust and reliable solution.
We present VirtSecIO, a hypervisor-based platform for executing secure modules. VirtSecIO provides the modules with secure paths to peripheral devices, which can be shared between the modules and the operating system. Moreover, VirtSecIO is a thin hypervisor with a negligible performance overhead and a minimal attack surface. We demonstrate VirtSecIO’s abilities by developing HyperWallet, a secure module that acts as a hardware crypto-wallet, without requiring any dedicated hardware.
Open access
Security and Verification in Computing
Advanced Malware Detection Techniques
Physical Unclonable Functions (PUFs) and Hardware Security
Blockchain fuelled the innovation of numerous application fields. In particular, Web3 applications benefit the most because blockchain can be used to implement a rewarding system for users that contribute the most, thus increasing the overall social good provided by these platforms. One of the sectors that has benefited most from blockchain technology is the gaming sector through the so-called Play-to-Earn (P2E) model. The P2E Blockchain Video Games allow players to earn rewards in the form of tokens or NFTs, by having an impact on the social good. Unfortunately, bot accounts could exploit these platforms, which defeats the purpose of having a reward system because they invalidate the social good introduced by the rewards. In this paper, we provide an analysis geared towards detecting suspicious behaviour in P2E blockchain-based games by exploiting Gods Unchained as a case study. Using the game’s official APIs, we download 12 months’ worth of players’ activity. Analysing the data, we detect two groups of players with abnormal activity. Additionally, analysing the players’ graph, we find communities made of the best players with similar activity. Lastly, we observe that users with suspicious behaviour belong to these communities.
Algimantas Venčkauskas, Vacius Jusas, Dominykas Barisas, Boriss Mišņevs
Sharing cyber threat intelligence (CTI) can significantly improve the security of information technology (IT) in organizations. However, stakeholders and practitioners are not keen on sharing CTI data due to the risk of exposing their private data and possibly losing value as an organization on the market. We present a model for CTI data sharing that maintains trust and confidentiality and incentivizes the sharing process. The novelty of the proposed model is that it combines two incentive mechanisms: money and reputation. The reputation incentive is important for ensuring trust in the shared CTI data. The monetary incentive is important for motivating the sharing and consumption of CTI data. The incentives are based on a subscription fee and a reward score for activities performed by a user. User activities are considered in the following three fields: producing CTI data, consuming CTI data, and reviewing CTI data. Each instance of user activity is rewarded with a score, and this score generates some value for reputation. An algorithm is proposed for assigning reward scores and for recording the accumulated reputation of the user. This model is implemented on the Hyperledger Fabric blockchain and the Interplanetary File System for storing data off-chain. The implemented prototype demonstrates the feasibility of the proposed model. The provided simulation shows that the selected values and the proposed algorithm used to calculate the reward scores are in accordance with economic laws.
This paper explores the vulnerability of machine learning models, specifically Random Forest, Decision Tree, and K-Nearest Neighbors, to very simple single-feature adversarial attacks in the context of Ethereum fraudulent transaction detection. Through comprehensive experimentation, we investigate the impact of various adversarial attack strategies on model performance metrics, such as accuracy, precision, recall, and F1-score. Our findings, highlighting how prone those techniques are to simple attacks, are alarming, and the inconsistency in the attacks' effect on different algorithms promises ways for attack mitigation. We examine the effectiveness of different mitigation strategies, including adversarial training and enhanced feature selection, in enhancing model robustness.
The emergence of the Internet of Things (IoT), Industry 5.0 applications and associated services have caused a powerful transition in the cyber threat landscape. As a result, organisations require new ways to proactively manage the risks associated with their infrastructure. In response, a significant amount of research has focused on developing efficient Cyber Threat Intelligence (CTI) sharing. However, in many cases, CTI contains sensitive information that has the potential to leak valuable information or cause reputational damage to the sharing organisation. While a number of existing CTI sharing approaches have utilised blockchain to facilitate privacy, it can be highlighted that a comprehensive approach that enables dynamic trust-based decision-making, facilitates decentralised trust evaluation and provides CTI producers with highly granular sharing of CTI is lacking. Subsequently, in this paper, we propose a blockchain-based CTI sharing framework, called Priv-Share, as a promising solution towards this challenge. In particular, we highlight that the integration of differential sharing, trustless delegation, democratic group managers and incentives as part of Priv-Share ensures that it can satisfy these criteria. The results of an analytical evaluation of the proposed framework using both queuing and game theory demonstrate its ability to provide scalable CTI sharing in a trustless manner. Moreover, a quantitative evaluation of an Ethereum proof-of-concept prototype demonstrates that applying the proposed framework within real-world contexts is feasible.
Abstract Suppose that a sequence of $${\varvec{n}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> cards, numbered 1 to $${\varvec{n}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> , is placed face up in random order. Let $${\varvec{k}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> </mml:math> be the number on the first card in the sequence. Then take the first $${\varvec{k}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> </mml:math> cards from the sequence, rearrange that subsequence of $${\varvec{k}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> </mml:math> cards in reverse order, and return them to the original sequence. Repeat this prefix reversal until the number on the first card in the sequence becomes 1. This is a one-player card game called Topswops. The computational complexity of Topswops has not been thoroughly investigated. For example, letting $${\varvec{f}}({\varvec{n}})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mi>f</mml:mi> </mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> denote the maximum number of prefix reversals for Topswops with $${\varvec{n}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> cards, values of $${\varvec{f}}({\varvec{n}})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mi>f</mml:mi> </mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> for $${\varvec{n}}\ge 20$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> <mml:mo>≥</mml:mo> <mml:mn>20</mml:mn> </mml:mrow> </mml:math> remain unknown. In general, there is no known efficient algorithm for finding an initial sequence of $${\varvec{n}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> cards that requires exactly $$\ell $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ℓ</mml:mi> </mml:math> prefix reversals for any integers $${\varvec{n}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> and $${\varvec{\ell }}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>ℓ</mml:mi> </mml:mrow> </mml:math> . In this paper, using a deck of cards, we propose a physical zero-knowledge proof protocol that allows a prover to convince a verifier that the prover knows an initial sequence of $${\varvec{n}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> cards that requires $${\varvec{\ell }}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>ℓ</mml:mi> </mml:mrow> </mml:math> prefix reversals without leaking knowledge of that sequence. We also deal with Botdrops, a variant of Topswops.
Luca Aceto, Daniele Gorla, Stian Lybech, Mohammad Hamdaqa
We continue the development of TinySol, a minimal object-oriented language based on Solidity, the standard smart-contract language used for the Ethereum platform. We first extend TinySol with exceptions and a gas mechanism, and equip it with a small-step operational semantics. Introducing the gas mechanism is fundamental for modelling real-life smart contracts in TinySol, since this is the way in which termination of Ethereum smart contracts is usually ensured. We then devise a type system for smart contracts guaranteeing that such programs never run out of gas at runtime. This is a desirable property for smart contracts, since a transaction that runs out of gas is aborted, but the price paid to run the code is not returned to the invoker.
The rapid growth of Decentralized Finance (DeFi) has been accompanied by substantial financial losses due to smart contract vulnerabilities, underscoring the critical need for effective security auditing. With attacks becoming more frequent, the necessity and demand for auditing services has escalated. This especially creates a financial burden for independent developers and small businesses, who often have limited available funding for these services. Our study builds upon existing frameworks by integrating Retrieval-Augmented Generation (RAG) with large language models (LLMs), specifically employing GPT-4-1106 for its 128k token context window. We construct a vector store of 830 known vulnerable contracts, leveraging Pinecone for vector storage, OpenAI's text-embedding-ada-002 for embeddings, and LangChain to construct the RAG-LLM pipeline. Prompts were designed to provide a binary answer for vulnerability detection. We first test 52 smart contracts 40 times each against a provided vulnerability type, verifying the replicability and consistency of the RAG-LLM. Encouraging results were observed, with a 62.7% success rate in guided detection of vulnerabilities. Second, we challenge the model under a "blind" audit setup, without the vulnerability type provided in the prompt, wherein 219 contracts undergo 40 tests each. This setup evaluates the general vulnerability detection capabilities without hinted context assistance. Under these conditions, a 60.71% success rate was observed. While the results are promising, we still emphasize the need for human auditing at this time. We provide this study as a proof of concept for a cost-effective smart contract auditing process, moving towards democratic access to security.
Smart contract developers frequently seek solutions to developmental challenges on Q&A platforms such as Stack Overflow (SO). Although community responses often provide viable solutions, the embedded code snippets can also contain hidden vulnerabilities. Integrating such code directly into smart contracts may make them susceptible to malicious attacks. We conducted an online survey and received 74 responses from smart contract developers. The results of this survey indicate that the majority (86.4%) of participants do not sufficiently consider security when reusing SO code snippets. Despite the existence of various tools designed to detect vulnerabilities in smart contracts, these tools are typically developed for analyzing fully-completed smart contracts and thus are ineffective for analyzing typical code snippets as found on SO. We introduce SOChecker, the first tool designed to identify potential vulnerabilities in incomplete SO smart contract code snippets. SOChecker first leverages a fine-tuned Llama2 model for code completion, followed by the application of symbolic execution methods for vulnerability detection. Our experimental results, derived from a dataset comprising 897 code snippets collected from smart contract-related SO posts, demonstrate that SOChecker achieves an F1 score of 68.2%, greatly surpassing GPT-3.5 and GPT-4 (20.9% and 33.2% F1 Scores respectively). Our findings underscore the need to improve the security of code snippets from Q&A websites.