1. Introduction
Blockchain technology can potentially revolutionize various industries, including finance, healthcare, and supply chain management [1,2]. Its decentralized nature offers several advantages over traditional centralized systems, such as increased transparency, immutability, and security. These features have fueled significant adoption of and investment in blockchain technologies. According to Deloitte’s 2021 Global Blockchain Survey, 76% of respondents reported that their organizations had adopted blockchain as a digital asset, while 83% believed digital assets would replace fiat currency within the next decade [3]. The growth trajectory of blockchain technology is also evident from projections by Grand View Research Inc., which estimated that the global blockchain technology market would reach USD 1431.54 billion by 2030, with a compound annual growth rate (CAGR) of 87.7% between 2023 and 2030 [4]. Similarly, another report predicted that the blockchain distributed ledger market would grow from USD 2.89 billion in 2020 to USD 137.29 billion by 2027, with a CAGR of 62.7% [5].
While the financial services sector currently dominates the blockchain market, the technology’s applications have rapidly expanded into various domains. Beyond cryptocurrencies, blockchain enables cross-border money transfers [6], supports Ethereum Virtual Machine (EVM)-based smart contracts [7], integrates into the Internet of Things (IoT) ecosystem [8,9], enhances data and identity security [10,11], and facilitates electronic healthcare records (EHRs) [12,13,14]. Additionally, blockchain has been applied to automated logistics systems [15] and the burgeoning market of non-fungible tokens (NFTs) [16].
Despite its transformative potential, blockchain technology faces significant challenges, particularly in security. The decentralized nature of blockchain networks does not render them immune to attacks; on the contrary, it introduces novel vulnerabilities. Blockchain system hacks have increased, and NIST reported a notable rise in vulnerabilities in their published report in 2023 [17], while SonicWall highlighted a sharp rise in cybercrimes in its 2024 Cyber Threat Report [18]. Real-world incidents illustrate the severity of these threats. For example, Ethereum Classic suffered multiple 51% and double-spending attacks in 2019 and 2020, resulting in a loss of over USD 6.7 million [19,20]. On 10 August 2021, the Poly Network was exploited via a smart contract vulnerability, leading to the theft of USD 611 million—the largest crypto-related hack to date [21]. Similarly, Binance, the world’s largest cryptocurrency exchange, faced a major breach in October 2022, with hackers stealing 2 million BNB tokens worth USD 570 million [22,23]. These incidents underscore the urgency of addressing security challenges within blockchain ecosystems.
As blockchain adoption accelerates, understanding its vulnerabilities and implementing robust countermeasures is critical. The current study aims to contribute to this effort by conducting a comprehensive analysis of blockchain attacks, vulnerabilities, and mitigation strategies. A thorough examination of existing security challenges and their impacts can help practitioners and researchers design more resilient blockchain systems. To guide this research, we formulated the following research questions: RQ1: What are the most common types of attacks on blockchain technology, and how do they affect the security and integrity of the system? RQ2: What security measures and technologies have been employed to detect and mitigate malicious blockchain attacks? RQ3: How can context-specific mitigation strategies be designed to address the unique requirements and constraints of different blockchain applications?
To address these questions, this study adopts a qualitative and quantitative approach to reviewing the state-of-the-art research on blockchain security and privacy. By presenting a holistic outline of vulnerabilities, attack vectors, and countermeasures, this research serves as a valuable resource for academics, industry professionals, and policymakers seeking to safeguard decentralized ecosystems from current and future threats.
2. Methodology
We employed a systematic literature search strategy, where we used three major online digital libraries: IEEE Xplore, ACM digital library, and Springer Nature Link digital library. An analytical process was undertaken to synthesize the information for the research questions. Out of the initial 1143 studies, 73 studies were from IEEE Xplore, 578 studies were from ACM digital library, and 492 studies were from SpringerLink library.
2.1. Scoping Criteria
To address the research questions concerning the common types of attacks on blockchain technology, their impact on security and integrity, and the security measures employed to detect and mitigate such attacks, we established comprehensive scoping criteria. These criteria focus on identifying, categorizing, and evaluating blockchain vulnerabilities, attack mechanisms, and mitigation strategies. In order to limit our scope, we focused on the top three cryptocurrencies (Bitcoin, Ethereum, and Tether) based on their market capital according to Forbes [24]. In order to guarantee that this systematic evaluation covers the most recent blockchain security risks, new attack vectors, and cutting-edge mitigation techniques, publications from 2020 to 2024 were chosen. With developments in decentralized finance (DeFi), smart contracts, Layer 2 scaling solutions, and cross-chain protocols, the blockchain environment has swiftly changed, bringing with it new vulnerabilities including bridge attacks, reentrancy problems, and flash loan exploits [25]. Furthermore, modern cryptographic countermeasures and security frameworks are required due to recent advancements in quantum computing and AI-driven cyberthreats [26]. Additionally, security compliance and governance models have been impacted by global regulatory changes, such as the EU’s Markets in Crypto-Assets (MiCA) framework and the U.S. SEC’s heightened scrutiny [27].
Our analysis explores key attack vectors, including 51% attacks, bribing attacks, double-spend attacks, smart contract vulnerability attacks, selfish mining attacks, and Sybil attacks [28,29]. For example, a 51% attack allows malicious actors to gain control of the majority of a network’s computational power, enabling them to manipulate transactions and even reverse previously confirmed transactions. A notable instance occurred in the Ethereum Classic blockchain in 2019, resulting in significant financial losses. Similarly, the study examines vulnerabilities within smart contracts, such as the infamous Poly Network attack in 2021, where a critical exploit led to the theft of USD 611 million.
2.2. Structured Literature Search Procedure
2.2.1. Eligibility Criteria
Inclusion: Peer-reviewed studies, conference papers, journal papers, and research articles published between 1 January 2020 and 31 December 2024.
Exclusion: Studies lacking empirical data, opinion pieces, keynotes, short papers, magazines, books, non-English articles, retracted papers, and studies that do not discuss mitigation, detection, and prevention strategies.
2.2.2. Search Strategy
We conducted our search with specific keywords based on the given queries (last search date: 10 March 2025): IEEE Xplore library: (((“All Metadata”: blockchain) AND (“All Metadata”: security) AND (“All Metadata”: privacy) AND (“All Metadata”: attack) AND (“All Metadata”: vulnerability*)) AND ((“All Metadata”: bitcoin) OR (“All Metadata”: btc) OR (“All Metadata”: tether) OR (“All Metadata”: usdt) OR (“All Metadata”: ethereum) OR (“All Metadata”: eth)) AND ((“All Metadata”: smart contract) OR (“All Metadata”: Cryptograph*) OR (“All Metadata”: Cryptocurrenc*))); ACM digital library: [All: blockchain] AND [All: security] AND [All: privacy] AND [All: attack] AND [All: vulnerabilit*] AND [[All: bitcoin] OR [All: btc] OR [All: tether] OR [All: usdt] OR [All: ethereum] OR [All: eth]] AND [[All: “smart contract”] OR [All: cryptograph*] OR [All: cryptocurrenc*]] AND [E-Publication Date: 1 January 2020 TO 31 December 2024)]; Nature SpringerLink library: (blockchain AND security AND privacy AND attack AND vulnerability*) AND (bitcoin OR btc OR tether OR usdt OR ethereum OR eth) AND (smart contract OR cryptograph* OR cryptocurrenc*).
2.2.3. Selection Process
We recruited six independent reviewers for the article screening process, and it was carried out using a manual quantitative analysis process achieved through open coding. All of the reviewers agreed to each of the codes that we used to answer our research questions. We also added a PRISMA flowchart to depict the study selection stages, which is shown in Figure 1.
2.2.4. Data Extraction Process
The data extraction process was structured to ensure the consistency, accuracy, and relevance of the extracted information. Initially, we found 1143 studies from three distinct databases. With the screening process that is shown in Figure 1, we ended up adding 252 studies to our research. Each selected study was reviewed to extract the key details, including attack type, impact assessment, mitigation strategies, study methodology, and blockchain application domain. The data were extracted using standardized forms and reviewed independently by six researchers to minimize bias, with any disagreements resolved through discussion or consultation with a seventh reviewer. The extracted information was cross-verified with original studies to maintain consistency, and any missing data were either supplemented using additional sources or excluded from the study. Finally, the extracted data were structured for comparison and the key findings were summarized to facilitate qualitative and quantitative analysis. This structured approach ensured that all relevant aspects of blockchain security threats and mitigation strategies were systematically captured and analyzed.
2.2.5. Study Quality Assessment
The study quality was assessed using the Critical Appraisal Skills Programme (CASP) checklist [30]. The CASP checklist ensures methodological rigor and helps to identify the potential biases in selected studies. Each study was evaluated based on relevance, validity, methodological soundness, bias and confounding factors, reproducibility, transparency, and applicability to blockchain security. In order to rank the studies based on their quality, a scoring system was applied based on their quality, and low-quality studies were excluded and studies without empirical evaluation were excluded during the final synthesis process.
2.3. Data Analysis Process
We started our investigation with 1143 studies from three databases, but we eliminated 8 of them since they were duplicates. Six pieces of research were also disqualified since they were retracted papers. We eliminated a total of 853 papers from our title–abstract screening procedure because they were either not research publications (n = 1) or did not address mitigation, detection, or prevention-related issues (n = 852). Lastly, we removed a total of 24 papers from our full-text analysis since 2 did not fall within the timeframe (2020–2024) that we were interested in, and 22 articles were not available even with institutional access.
The data analysis process involved categorizing attack types based on their mechanisms and assessing their impact on blockchain security and integrity. We gave specific attention to the evolution of attack techniques and motivations. Early attacks predominantly exploited technical flaws, while contemporary threats have evolved into more sophisticated strategies with financial gain as the primary objective. For instance, Sybil attacks leverage multiple fake identities to compromise network integrity, while advanced exploits target vulnerabilities in consensus algorithms such as Proof of Work (PoW) and Proof of Stake (PoS).
To evaluate countermeasures, we analyzed the effectiveness of network security protocols, including firewalls and intrusion detection systems, and cryptographic techniques such as digital signatures and hash functions. Security audits conducted by external experts and consensus mechanisms are critically assessed to determine their resilience against evolving attack methods. The analysis also examined how these measures have adapted over time in response to the growing sophistication of blockchain threats.
The research further explored the anticipated trajectory of blockchain attacks and corresponding security measures. As blockchain technology evolves, new attack vectors are expected to emerge, necessitating continuous advancements in defense mechanisms. Current challenges such as scalability, interoperability, and the trade-offs between decentralization and security are highlighted as critical areas for future investigation.
3. Attacks in Blockchain
3.1. Classification of Blockchain Attacks
Through our systematic literature search, we identified a diverse range of cyberattacks targeting blockchain-decentralized ecosystems, which we classified into distinct categories based on their mechanisms and impact, illustrated in Figure 2. These include quantum computing threats, which exploit advancements in quantum technology to undermine cryptographic security, and smart contract attacks, such as reentrancy vulnerabilities and gas limit manipulations, that exploit flaws in the execution of automated agreements. Additionally, network-based attacks including routing and eclipse attacks aim to disrupt the flow of information and isolate nodes, while transaction-based attacks, including double-spending and race attacks, compromise the integrity of financial operations.
Other identified categories include data and key management attacks, including private key theft and man-in-the-middle exploits, wallet and exchange attacks, including phishing and hot wallet compromises, and consensus-based attacks, such as the 51% attack, which undermines blockchain consensus protocols. Furthermore, blockchain fork and interoperability attacks exploit chain splits or cross-chain bridges, while governance and economic attacks manipulate decision-making or token economies.
3.1.1. The 51% Attack
Also known as majority attacks, 51% attacks remain one of the most critical threats to blockchain networks [31]. They occur when an attacker or a group of miners gains control of more than 50% of a network’s computational power, thereby compromising the consensus mechanism. This control enables malicious actors to manipulate blockchain data and execute attacks such as the following: (a). Reversing transactions, enabling double spending (spending the same coins multiple times); (b). Altering the order of transactions; (c). Disrupting the activities of other miners; (d). Preventing confirmation of legitimate transactions.
For instance, Ethereum Classic suffered a series of 51% attacks in 2019, causing financial losses and undermining the network’s integrity. Indicators of a 51% attack often include abrupt changes in network behavior or transaction anomalies.
3.1.2. Smart Contract Vulnerabilities
Smart contracts, which are self-executing programs running on blockchain networks, play a crucial role in automating and enforcing agreements without intermediaries. Despite their advantages, smart contracts are vulnerable to coding errors and design flaws that can result in unauthorized access or loss of funds [32,33,34]. These vulnerabilities are particularly critical due to the immutability of blockchain, which makes it challenging to correct errors once deployed [35]. Notable cases, such as the Poly Network hack in 2021, illustrate how attackers exploit smart contract vulnerabilities to siphon funds, demonstrating the need for rigorous security audits and robust coding practices.
3.1.3. Double-Spending Attack
A double-spending attack occurs when an attacker spends the same digital currency or asset more than once, violating the principle of data consistency. This attack is facilitated by exploiting weaknesses in the consensus algorithm or the delay in transaction confirmation. For example, in January 2021, a double-spending attack on Monero’s blockchain resulted in the theft of USD 3.3 million [36,37]. Factors contributing to this vulnerability include the following: Insufficiently secure or slow consensus algorithms; Delayed block confirmation times; Acceptance of unverified transactions; Direct connections of incoming nodes to the main chain [38,39].
These factors provide adversaries with an opportunity to manipulate transaction records and execute double-spending schemes.
3.1.4. Man-in-the-Middle (MITM) Attack
In a blockchain network, a man-in-the-middle (MITM) attack occurs when an attacker intercepts and manipulates communication between two nodes. This allows the attacker to alter transmitted data, steal private keys, or tamper with transaction records, resulting in significant security breaches, data loss, and financial damage [40,41,42]. MITM attacks undermine the trust and integrity of blockchain systems, emphasizing the need for secure communication protocols and encryption mechanisms.
3.1.5. Routing Attack
Blockchain networks rely on routing protocols for node communication, transaction propagation, and consensus building. In a routing attack, adversaries manipulate the routing protocol to redirect traffic or compromise nodes [43]. By injecting false routing information, attackers can force nodes to choose malicious paths for transactions and block propagation. This facilitates further attacks, such as the following: (a). Double-spending attacks; (b). Denial-of-Service (DoS) attacks; (c). 51% attacks.
Routing attacks disrupt network functionality and compromise data security, highlighting the necessity of robust routing protocols and real-time network monitoring.
3.1.6. Sybil Attack
A Sybil attack targets peer-to-peer (P2P) networks by allowing a malicious node to create multiple fake identities, disrupting network functionality. This attack is particularly effective in consensus mechanisms like Proof of Work (PoW), where a node’s computational power determines its ability to validate transactions and earn rewards. For example, in May 2022, the Ethereum-based DeFi protocol, Saddle Finance, suffered a Sybil attack that exploited vulnerabilities in its on-chain governance system, leading to a loss of over USD 10 million [44]. Similarly, in August 2021, a Sybil attack compromised the Liquid Global cryptocurrency exchange, resulting in losses exceeding USD 90 million [45]. Another study highlights that the node authentication process during the blockchain network’s joining phase is particularly vulnerable to Sybil attacks, as attackers can insert malicious nodes that disrupt the network and cause delays in block propagation. This vulnerability underscores the need for robust node authentication mechanisms to prevent the insertion of fake identities.
3.1.7. Race Attack
A race attack, a prevalent form of double-spending attack, occurs when a malicious actor broadcasts two conflicting transactions simultaneously to the network [46,47]. In this scenario, the attacker sends one transaction to the victim, who accepts the payment and delivers the product without waiting for confirmation. Simultaneously, the attacker broadcasts a contradictory transaction to the network, retracting the same amount of cryptocurrency, thereby invalidating the initial transaction [48]. If recipients fail to authenticate the initial transaction properly, the attacker can effectively acquire products or services without making a legitimate payment. This exploit highlights the importance of requiring sufficient confirmations before finalizing transactions to prevent race attacks [49].
3.1.8. Eclipse Attack
An eclipse attack occurs when a malicious actor gains control over all peers connected to a targeted node, isolating it from the rest of the blockchain network. This isolation allows the attacker to manipulate the targeted node, compromise its data, and block legitimate network communication, restricting the node’s interactions solely to malicious peers [43,50]. For example, attackers may send a falsified transaction to the isolated node, presenting it as proof of payment, while broadcasting a conflicting transaction to the broader network to double spend the same tokens [50]. By monopolizing the connections of a node, eclipse attacks disrupt network consensus and can have severe implications for blockchain integrity and reliability. Addressing this vulnerability requires implementing diverse connection strategies, random peer selection, and robust routing protocols.
3.1.9. Replay Attack
A replay attack exploits the reuse of valid transaction data to disrupt blockchain networks or steal funds. This attack typically occurs when blockchain ledgers experience hard forks or chain splits, creating separate chains that share a transaction history. In such cases, transactions broadcast on one chain may be replayed on another due to shared transaction data and the absence of replay protection mechanisms [51]. Vulnerabilities arise from incompatible transaction formats, shared historical data, and weak replay protection, particularly in cryptocurrency exchanges, decentralized finance (DeFi) platforms, and blockchain-based supply chain systems [52]. Replay attacks can have significant financial and operational consequences, emphasizing the need for robust replay protection mechanisms, such as unique chain identifiers and transaction tagging, to prevent unauthorized transaction duplication.
3.2. Key Insights and Implications
These attacks highlight the evolving threat landscape within blockchain ecosystems. While blockchain technology offers significant advantages, its adoption across diverse applications necessitates a continuous focus on identifying and mitigating security vulnerabilities. Understanding the root causes and mechanisms of these attacks provides critical insights for developing resilient security measures. As blockchain systems continue to evolve, addressing these challenges remains a vital area for research and innovation. The causes and affected sectors of certain typical blockchain security attack types are summarized in Table 1.
3.3. Detecting and Mitigating Malicious Blockchain Attacks
As blockchain technology adoption accelerates, the potential for malicious attacks grows, necessitating robust detection and mitigation strategies. A variety of methods and technologies has been developed to enhance the security and resilience of blockchain networks. These include machine-learning-based activity detection, consensus algorithms to prevent attacks such as double-spending attacks, and cryptographic tools for data integrity. Each subsection focuses on specific types of malicious blockchain attacks, providing a comprehensive understanding of their detection and mitigation techniques.
3.3.1. The 51% Attack
A 51% attack occurs when a single entity or group gains control of more than 50% of a network’s mining hash rate or staking power, enabling them to manipulate the blockchain. This attack is particularly relevant to Proof-of-Work (PoW) blockchains like Bitcoin and Litecoin, as well as smaller chains with lower hash rates. The attacker can double spend coins, prevent transactions from being confirmed, and disrupt the network’s integrity.
Detection Techniques
Detection techniques for 51% attacks focus on monitoring network hash rate distribution and identifying unusual patterns in block creation. Key methods include the following: Hash rate monitoring: Continuous tracking of hash rate distribution among mining pools to detect centralization risks [68]. Block propagation analysis: Observing block propagation times and orphan rates to identify potential manipulation [69]. Network consensus monitoring: Analyzing deviations from normal consensus behavior, such as sudden changes in block confirmation times [70].
Mitigation Strategies
To mitigate the risk of a 51% attack, the following strategies are essential: Decentralization of mining power: Encouraging a diverse and distributed mining pool ecosystem to reduce the risk of hash rate concentration [69]. Network size and security: Increasing the overall hash rate and network size to make it economically infeasible for an attacker to gain majority control [68]. Consensus algorithm enhancements: Transitioning to more secure consensus mechanisms, such as Proof of Stake (PoS), which are less susceptible to hash-rate-based attacks [70]. Real-time alerts and response systems: Implementing systems to detect and respond to unusual network activity, such as sudden hash rate spikes or block reorganizations [69].
By adopting these measures, blockchain networks can reduce the likelihood of a 51% attack and maintain the integrity and trustworthiness of their systems.
3.3.2. Smart Contract Vulnerabilities
Smart contracts, being self-executing and immutable, are susceptible to vulnerabilities that can lead to significant security breaches. Detection techniques for these vulnerabilities are broadly categorized into static and dynamic analysis methods.
Detection Techniques
Static analysis methods include the following: Symbolic execution: Code is analyzed with symbolic values, enabling the generation of algebraic terms and propositional formulas to uncover logical errors [71,72]. Control flow graph (CFG) construction: Represents execution flow as a directed graph, aiding understanding of the program’s structure [71,72]. Pattern recognition and rule-based analysis: Matches code against predefined secure patterns or known vulnerabilities [57,72]. Decompilation analysis: Translates lower-level bytecode into higher-level representations for easier parsing and vulnerability identification [71]. Formal verification: Uses mathematical proofs to ensure the program adheres to its specified properties [73].
Dynamic analysis methods include the following: Runtime Execution Trace: Captures the sequence of instructions executed during runtime for anomaly detection [71]. Fuzz Testing: Provides structured data as inputs to trigger unexpected behavior, such as crashes or abnormal execution paths [72].
Mitigation Strategies
To enhance smart contract security, the following measures are crucial: Adopting established design patterns and best practices to minimize coding errors. Implementing robust access control mechanisms to prevent unauthorized interactions. Conducting thorough security audits and regular code reviews to identify potential vulnerabilities. Utilizing comprehensive testing methods, including fuzz testing and formal verification [73]. Deploying upgradeable contracts to allow future improvements without disrupting the system. Monitoring deployed contracts and addressing detected vulnerabilities proactively [71,72].
3.3.3. Man-in-the-Middle (MITM) Attack
Man-in-the-middle (MITM) attacks involve intercepting and manipulating communication between nodes in a blockchain network. These attacks can compromise data integrity, steal sensitive information, and undermine trust within the network.
Detection Techniques
Network Monitoring: Detects unusual activity, such as rogue nodes or unexpected data transmissions [74].
Consensus Checks: Identifies discrepancies in transaction data and halts suspicious processes [75].
Digital Signatures: Ensures data integrity and authenticates transaction senders, preventing tampering [76].
Reputation Systems: Tracks node behavior to identify and flag potentially malicious nodes [77].
Mitigation Strategies
Encryption: Ensures that data transmitted between nodes remain secure and incomprehensible to attackers [78].
Multi-Factor Authentication (MFA): Protects private keys using techniques such as biometric authentication and one-time passwords [79].
Consensus Mechanisms: Requires multiple nodes to verify and approve transactions, making it difficult for attackers to manipulate data [80].
Identity Management: Authenticates and authorizes nodes before allowing them to join the network using tools like a public key infrastructure and digital certificates [81].
Real-Time Monitoring: Combines network and transaction monitoring to detect and mitigate potential MITM attacks [82].
Effective detection and mitigation of MITM attacks safeguard blockchain networks by ensuring data confidentiality, integrity, and authenticity. Table 2 summarizes the findings.
3.3.4. Routing Attack
Detecting routing attacks in blockchain networks is challenging due to the difficulty of distinguishing between benign and malicious network behavior. However, several techniques have been proposed to identify such attacks effectively. Network monitoring is a key technique, allowing the tracking of transaction routing paths and the detection of anomalies in network traffic [88]. For instance, a transaction propagating through an unusual path may indicate a routing attack [89]. Data analysis can uncover patterns and trends in network traffic that suggest malicious activities. For example, a sudden increase in the number of transactions routed through a specific node may signal a routing attack [90,91]. Reputation systems are also critical, assigning scores to nodes based on their behavior. Nodes with low reputation scores can be flagged as potential sources of routing attacks [92].
To mitigate routing attacks, several strategies have been proposed. Secure routing protocols, such as BGPsec and RPKI, use digital certificates to authenticate routing information and prevent spoofing attacks [93]. Distributed consensus mechanisms, including Proof of Work (PoW) and Proof of Stake (PoS), increase the difficulty for attackers to control the majority of the network’s computational power or stake, thereby preventing routing attacks [94]. Peer-to-peer networking enhances network resilience by allowing nodes to communicate directly, reducing reliance on centralized routing authorities [95]. Furthermore, node reputation systems can identify malicious or compromised nodes, preventing them from propagating transactions and blocks in the network [96]. Nodes with low reputation scores can be excluded or subjected to additional scrutiny, thereby limiting the risk of routing attacks.
3.3.5. Race Attack
Race attacks have been extensively studied, with several detection techniques proposed. A key detection method is the Listening Period, during which vendors associate each transaction with a listening time frame. During this period, transactional records are scrutinized for any signs of a race attack. If no malicious activity is detected within this time frame, the vendor proceeds with the transaction [97]. Another effective technique is the Insertion of Observers, where vendors deploy monitoring nodes within the network. These observers transmit all transactions to the vendor, enabling swift detection of race or double-spending attacks [97].
To prevent race attacks, several mitigation strategies can be employed. Timestamping all transactions ensures that the processing time is recorded, allowing the first recorded transaction to be considered valid. Cryptographic signatures authenticate transactions, verifying that they were submitted by the intended user. Multi-party signatures enhance security by requiring multiple parties to approve a transaction, reducing the risk of unauthorized activities. Increasing the block processing time also mitigates race attacks, as attackers face greater difficulty in executing multiple fraudulent transactions within a restricted timeframe [64].
3.3.6. Eclipse Attack
Eclipse attacks have been addressed in various studies, with several detection approaches proposed. One method involves analyzing the timestamps of questionable blocks. If the time interval between newly created blocks is abnormally high, it may indicate an eclipse attack, with detection typically requiring 2–3 h [66]. Another technique is the ubiquitous gossip protocol, which allows users to connect with protocol-dominated servers through gossip messages without requiring changes to the Bitcoin protocol or the peer-to-peer network. This approach minimizes dependency on web servers for connecting with Bitcoin block headers [98]. Additionally, redundancy can be introduced by replicating keys across multiple nodes and conducting routing failure tests to identify compromised nodes.
To mitigate eclipse attacks, several countermeasures are available. Elimination of Far Successors is a technique where nodes autonomously calculate the distances between their successors, removing any malicious entries immediately [99]. Bitcoin clients also use a classification system to segregate new peers and previously established connections, preventing attackers from exploiting these distinctions [43]. Expanding the network size further complicates an attacker’s ability to control a significant portion of the network. Peer-discovery mechanisms allow nodes to connect with a diverse set of peers, thereby reducing the likelihood of isolation and increasing resilience against eclipse attacks.
3.3.7. Double-Spending Attack
Double-spending attacks pose a significant threat to blockchain systems by allowing malicious actors to spend the same digital asset multiple times, undermining transaction integrity. Several detection and prevention mechanisms have been developed to mitigate this risk effectively.
Key detection techniques include transaction and block verification processes, where the transaction time and block confirmation are monitored to identify inconsistencies or unusual behavior [36]. Updating the existing block rather than removing it and implementing timestamping during chain-building processes further enhance detection accuracy [86]. Approaches such as the Longest Chain Rule in Proof-of-Stake (PoS) protocols, key-evolving cryptography, and increasing the number of confirmed blocks have also been shown to effectively identify double-spending attempts [100]. Continuous monitoring of node connections, disabling unconfirmed connections, and employing transaction forwarding mechanisms ensure additional layers of security [37,59].
Mitigation measures for double-spending attacks include adopting recipient-oriented transactions and increasing block confirmation thresholds, which make fraudulent transactions more challenging to execute. Cryptographic methods such as key-evolving cryptography provide secure mechanisms to verify transaction authenticity. Additionally, ensuring a consistent ledger and disabling unconfirmed connections prevent the propagation of invalid transactions [39]. These strategies collectively enhance blockchain reliability and maintain transactional trustworthiness.
3.3.8. Sybil Attack
Sybil attacks exploit vulnerabilities in peer-to-peer (P2P) networks by introducing multiple malicious nodes that mimic legitimate nodes, thereby disrupting network operations. Addressing these attacks requires a combination of detection and mitigation strategies.
TrustChain, a blockchain solution incorporating the NetFlow algorithm, calculates node reputation to detect and mitigate Sybil attacks by identifying malicious nodes [61]. Decentralized registration mechanisms prevent Sybil node injection into routing tables, addressing vulnerabilities such as Routing Table Insertion (RTI) attacks [62]. Identity verification during the initial node authentication phase, as proposed by Tayebeh et al., can constrain nodes exhibiting abnormally high computational power, mitigating risks to the network [63]. Furthermore, imposing non-refundable deposits, applying time locks to fund usage, and leveraging coin-age mechanisms can reduce the likelihood of Sybil nodes affecting block propagation and consensus [101].
Effective countermeasures also include implementing rigorous node authentication processes, which may involve network joining fees, source validation of node connections, and monitoring nodes’ forwarding behavior over time [102]. By integrating these techniques, blockchain networks can significantly reduce the impact of Sybil attacks and ensure network stability.
3.3.9. Replay Attack
Replay attacks exploit vulnerabilities in blockchain systems, particularly during hard forks or chain splits, by replaying valid transactions from one chain onto another. Detection techniques include monitoring for duplicate transactions across chains and analyzing transaction histories to identify repeated or suspicious activities [52]. Incorporating cryptographic measures, such as one-time private–public key pairs and elliptic-curve-based encryption, further strengthens detection capabilities [87].
Mitigation strategies for replay attacks include implementing strong replay protection, which ensures transactions made on a new blockchain post-fork cannot be replayed on the original chain. Splitting coins into distinct transactions on each chain prevents cross-chain replay vulnerabilities [51]. Opt-in replay protection allows users to mark transactions manually, granting additional control over transaction security [52]. These measures collectively enhance the resilience of blockchain systems against replay attacks.
4. Our Findings
In our study, we identified several critical findings that highlight the significance of addressing blockchain security vulnerabilities. Common attacks, including double-spending attacks, 51% attacks, and smart contract vulnerabilities, pose substantial threats to blockchain networks by undermining their security and integrity, leading to financial losses and eroding public trust.
4.1. Probability of Blockchain Attacks
We attempted to identify the probability of various blockchain attack types, which varies significantly based on the underlying vulnerabilities and the operational design of blockchain systems. Smart contract vulnerabilities exhibit the highest probability compared to other types of vulnerabilities in almost every case, and there are different key factors, including code vulnerabilities, questionable audit quality, and lack of formal verifications. These vulnerabilities often lead to attacks like reentrancy exploits, which are particularly devastating in decentralized finance (DeFi) platforms.
Table 3 illustrates the analysis of the likelihood of various blockchain attacks, and reveals significant variations in attack probabilities. Man-in-the-middle (MITM) attacks and double-spending attacks also demonstrate relatively high probabilities, at around 35–45%. The prevalence of MITM attacks arises from weaknesses in communication protocols and insufficient encryption mechanisms in blockchain networks. Double-spending attacks, meanwhile, are facilitated by delayed transaction confirmations or inadequate consensus mechanisms in Proof-of-Work (PoW) or hybrid systems.
Other attack types, such as Sybil attacks, leverage weaknesses in node authentication processes, allowing attackers to disproportionately influence network consensus or disrupt data propagation. Similarly, routing attacks and eclipse attacks exhibit moderate likelihoods, targeting network-layer vulnerabilities to isolate or delay nodes. Lower-probability attacks, like replay attacks, occur in specific contexts such as double spending or hard forks but remain critical threats in those scenarios.
These probabilities highlight the higher risks associated with certain attack types, such as double-spending attacks and smart contract vulnerabilities. Prioritizing security measures for these high-risk areas can significantly enhance blockchain resilience. These findings provide critical insights for researchers and practitioners, offering a roadmap for addressing blockchain security challenges effectively.
4.2. Detection and Mitigation Strategies for Blockchain Attacks
Our study comprehensively analyzed the diverse range of technologies and security measures available for detecting and mitigating blockchain attacks. These strategies encompass advanced consensus algorithms, such as Byzantine Fault Tolerance (BFT), Proof of Stake (PoS), and Delegated Proof of Stake (DPoS), which enhance the network’s resilience against consensus-based attacks like 51% attacks and Sybil attacks. Cryptographic techniques, including zero-knowledge proofs (ZKPs), multi-signature schemes, and lattice-based post-quantum cryptography, serve as robust safeguards against key management vulnerabilities, ensuring the integrity of transactions and the confidentiality of user identities.
Additionally, smart contract auditing tools and methodologies, such as static and dynamic analysis, formal verification, and fuzz testing, play a pivotal role in identifying vulnerabilities like reentrancy attacks or logic flaws within smart contracts. Secure development practices, such as adhering to well-defined design patterns, conducting regular code reviews, and implementing access control mechanisms, further mitigate the risks associated with poorly designed systems. Industry-specific measures, such as secure oracles for blockchain-based supply chains or privacy-preserving encryption techniques for healthcare applications, highlight the necessity of tailoring security solutions to specific use cases.
Given the dynamic nature of blockchain security, continuous research and cross-disciplinary collaboration are imperative. Emerging threats, such as quantum computing risks and cross-chain interoperability vulnerabilities, demand proactive development of quantum-resistant cryptography and secure protocols for decentralized applications. By staying informed about evolving attack vectors and integrating state-of-the-art technologies, the blockchain community can build robust security frameworks that ensure the resilience and trustworthiness of blockchain systems across diverse applications. This approach not only safeguards existing implementations but also fosters trust in and broad adoption of blockchain technology.
4.3. Significant Keyword Frequencies
In our analysis, we found that the keyword frequencies provide significant insights into the research trends and priorities within the field. The term “Blockchain” emerged as the most frequently mentioned keyword, appearing 16,400 times, which underscores its central role in the studies. This was closely followed by “Security” (11,518) and “Smart” (13,760), indicating a strong emphasis on security aspects and smart technologies, particularly in the context of blockchain systems. We also observed that keywords such as “Vulnerability/Vulnerabilities” (8422), “Attack” (5450), and “Detection” (7043) were highly prevalent, reflecting a substantial focus on identifying and addressing security threats and weaknesses within blockchain and related technologies. Additionally, terms like “Privacy” (4454) and “Encryption” (1633) highlight the importance of data protection and cryptographic methods in ensuring secure transactions and network integrity. The prominence of “Ethereum” (4672) and “Bitcoin” (1971) further emphasize the dominance of these platforms in blockchain research. Overall, our findings illustrate a research landscape deeply engaged with blockchain technology, its security challenges, and the development of strategies to mitigate vulnerabilities and enhance system robustness. Table 4 illustrates the significant keywords from our included studies.
4.4. Publication Frequencies by Year
As shown in Figure 3, our results revealed a significant trend in the included studies’ yearly publication frequency. According to the data, just 22 pieces of research were published in 2020, the year with the fewest publications, but, in the years that followed, there was a noticeable rise, with 39 publications in 2021 and 37 in 2022. After reaching 58 publications in 2023, the rising trend continued, culminating in a noteworthy peak of 118 publications in 2024. This dramatic increase in publications, especially in 2024, suggests that interest in the topic is expanding and that research is moving more quickly. The rise might be explained by the growing importance of blockchain technology, its uses, and the security issues that surround it, which have drawn more attention from both researchers and industry professionals. This pattern emphasizes how dynamic and quickly changing the area is, which reflects how crucial it is to solve new problems and advance our understanding of blockchain and related fields.
4.5. Distribution of Attack Categories
In our study, we determined the distribution of attack types and their frequencies across the included studies. Also, we categorized the attacks into 42 distinct categories based on their characteristics and impact areas. The category “Smart Contract Vulnerability” received the greatest attention, with 48 mentions, emphasizing its importance as a significant area of concern in blockchain and related technologies. This was followed by “Financial Fraud” (18) and “Privacy Violation” (19), both of which received significant attention, emphasizing the need for tackling financial and privacy threats in digital ecosystems. Other important categories are “Network Attack” (10), “Cryptographic Attack” (9), and “Denial of Service” (8), emphasizing the importance of network security, cryptographic vulnerabilities, and service disruption threats. Additionally, the term “IoT Vulnerability” occurred 22 times, showing a rising level of worry about the security of Internet of Things (IoT) systems. Categories such as “Authentication/Authorization” (17) and “Data Security” (4) highlight the significance of safe access control and data protection techniques. The inclusion of less common but significant categories, such as “Supply Chain Attack” (2), “Threat Intelligence” (1), and “Trusted Execution Environment” (1), implies that a wide variety of security concerns is being investigated. Overall, these findings point to a research environment that emphasizes smart contract vulnerabilities, financial fraud, and privacy concerns while also addressing a wide range of additional security threats and weaknesses in blockchain and associated technologies. Figure 4 demonstrates the distribution of attack categories.
4.6. Analysis of Core Reasons for Vulnerabilities
In our research, we discovered the primary causes of vulnerabilities throughout the included studies, as shown in Figure 5. The most common explanation was “Software Vulnerability”, which had 35 instances, showing the ubiquity of faults in software systems as a key source of security hazards. This was followed by “Access Control Weakness” (23) and “AI Model Bias” (21), which emphasizes the difficulties in protecting access mechanisms and resolving biases in artificial intelligence models. Furthermore, “IoT Security Weakness” occurred 15 times, indicating rising worries about the security of Internet of Things (IoT) devices. With 10 instances apiece, “Blockchain Security Issue” and “Cryptographic Flaw” are two more noteworthy explanations that highlight how critical it is to fix flaws in blockchain systems and cryptographic implementations. Although they are less common, problems like “Human Manipulation” (3), “Third-Party Risk” (3), and “Data Privacy Issues” (8) also add to the overall vulnerability illustration.
The category “Other” had the highest count (117), which represents a broad range of less frequently cited but equally critical reasons for vulnerabilities. These include emerging threats, such as novel attack techniques not yet widely studied; regulatory and compliance gaps, where security weaknesses arise from a lack of standardized regulations, particularly in emerging technologies like blockchain and AI; economic and incentive misalignment, where cost-cutting or misaligned incentives in decentralized finance (DeFi) lead to security flaws; legacy system weaknesses, where outdated or deprecated technologies remain susceptible to known exploits; interoperability and integration flaws, caused by poor integration between platforms or systems; lack of security culture and awareness, where organizational or user behavior leads to vulnerabilities, such as developers neglecting best practices or users falling for scams; and Protocol Design Flaws, where inherent weaknesses in system architectures, such as blockchain consensus mechanisms or smart contract frameworks, create exploitable vulnerabilities. These findings emphasize the diverse and multifaceted nature of security weaknesses, ranging from technical flaws to human, organizational, and systemic factors, and highlight the need for comprehensive strategies to mitigate these risks.
4.7. Attack Categories and Their Impact Results Across the Included Studies
In order to systematically analyze the effect outcomes of assaults throughout the available research, we divided them into 42 different categories. This classification made it possible for us to recognize the wide variety of vulnerabilities and the effects they have on different fields. For example, studies have emphasized the security concerns of AI/ML, including financial exploitation threats and poor vulnerability identification [103,104,105,106]. Likewise, Web3 apps’ application vulnerability resulted in execution errors that went unnoticed and possible security compromises [107]. Studies [69,70,108,109,110,111,112,113,114,115,116] that examined the authentication/authorization category found a variety of effects, such as private key breaches, illegal access, and legal liabilities. Performance deterioration and election result manipulation were two major blockchain security issues [117,118,119,120]. This thorough taxonomy highlights the complexity of security issues and their wide-ranging effects while offering an organized summary of the threat landscape. We want to enable focused mitigation efforts and more investigation into certain areas of susceptibility by classifying these findings into discrete categories. Table 5 summarizes our findings in different attack categories and their impact results across the included studies.
5. Discussion
Blockchain technology has revolutionized various industries by offering a decentralized and secure platform for transactions and data management. However, its potential is often undermined by persistent security challenges that threaten the integrity, confidentiality, and availability of blockchain networks. This research explored the security vulnerabilities in blockchain systems, emphasizing the detection and mitigation strategies for various attacks. The findings address three core research questions (RQs) and highlight their practical and theoretical implications, paving the way for more secure and resilient blockchain networks.
5.1. RQ1: Common Types of Attacks on Blockchain Technology and Their Impact
Blockchain technology faces several common attack types that compromise security and integrity: Smart contract vulnerabilities: Reentrancy attacks, integer overflow, and weak access control mechanisms lead to financial fraud and unauthorized transactions. Denial-of-service (DoS) attacks: DDoS blockchain state storage attacks cause network congestion and transaction delays, impacting system reliability. Consensus attacks: 51% attacks, selfish mining, and long-range attacks exploit mining power to reverse transactions and double spend assets. Oracle manipulation: Flash loan exploits and price manipulation enable attackers to control asset prices and execute fraudulent trades in DeFi applications. Cryptographic attacks: Quantum cryptographic threats, identity theft, and weak key management lead to unauthorized access and data breaches. Privacy violations: Data leaks in EHRs and IoT privacy breaches due to weak encryption mechanisms expose user-sensitive data. IoT security weaknesses: IoT device hijacking, unauthorized access, and industrial IoT intrusions result in compromised network security.
These attacks cause financial losses, data breaches, network disruption, and erosion of trust in blockchain systems.
5.2. RQ2: Security Measures and Technologies for Detecting and Mitigating Blockchain Attacks
To counter malicious blockchain attacks, various security measures are employed: Detection Techniques: −. AI and ML-based anomaly detection for fraud and transaction manipulation. −. Static and dynamic smart contract analysis for vulnerability detection. −. Cryptographic verification ensuring transaction integrity. −. Consensus monitoring for anomaly detection in mining behavior. Mitigation Measures: −. Secure smart contract coding practices and formal verification. −. Blockchain-based authentication for secure access control. −. Privacy-preserving mechanisms such as zero-knowledge proofs (ZKP) and homomorphic encryption. −. Hybrid blockchain models integrating public and private blockchains for enhanced security. Prevention Techniques: −. Decentralized identity management with multi-factor authentication. −. Intrusion prevention systems (IPS) for blocking unauthorized transactions. −. Tokenization and encrypted storage for securing sensitive data.
These security mechanisms help ensure blockchain resilience against various threats.
5.3. RQ3: Context-Specific Mitigation Strategies for Different Blockchain Applications
Different blockchain applications require tailored security approaches: Smart Contracts (DeFi and Financial Transactions): −. Pre-deployment formal verification to identify coding errors. −. Multi-signature authentication to prevent unauthorized withdrawals. −. Real-time transaction monitoring for Ponzi scheme detection. Enterprise and Government Blockchains: −. Hybrid blockchain integration balancing transparency and confidentiality. −. Regulatory compliance mechanisms such as AML and KYC frameworks. −. Decentralized governance models using DAOs for decision-making. IoT and Industrial Blockchain Applications: −. Lightweight blockchain security protocols for resource-constrained devices. −. Edge and fog computing integration to secure IoT data. −. Zero-knowledge proofs for securing IoT-generated data. Privacy-Critical Applications (Healthcare, Identity Management): −. Decentralized identity management to prevent unauthorized access. −. Confidential transactions with advanced encryption mechanisms. Blockchain-Based Voting and Governance: −. Cryptographic vote verification for secure elections. −. Resilient consensus mechanisms to prevent rollback attacks.
By implementing application-specific security strategies, blockchain networks can achieve better security, compliance, and efficiency.
5.4. Broader Implications
This research highlights the evolving nature of blockchain security, where the increasing sophistication of attack vectors necessitates continual advancements in detection and mitigation strategies. The integration of machine learning and artificial intelligence into anomaly detection, as well as the exploration of post-quantum cryptographic techniques, represent promising directions for future research.
Additionally, the environmental impact of resource-intensive consensus mechanisms, such as Proof of Work (PoW), underscores the importance of exploring energy-efficient alternatives like Proof-of-Stake (PoS) and hybrid models. The scalability and adoption of these solutions will play a critical role in ensuring blockchain technology’s long-term sustainability.
5.5. Practical and Theoretical Contributions
From a theoretical perspective, this study provides a structured taxonomy of blockchain attacks, detailing their mechanisms, causes, and countermeasures. Practically, it offers actionable recommendations for blockchain practitioners and industry stakeholders, emphasizing the importance of tailoring security measures to meet application-specific needs. These contributions bridge the gap between academic research and practical implementation, facilitating the development of secure blockchain ecosystems.
6. Conclusions
This research provides a comprehensive analysis of mitigating blockchain attacks and enhancing security measures, aligning with the transformative potential of blockchain technology across industries. Blockchain’s decentralized nature, combined with its benefits in transparency, immutability, and security, makes it a revolutionary tool for applications ranging from finance to supply chains and healthcare. However, our analysis has also highlighted significant challenges, particularly concerning security vulnerabilities and attacks that threaten the integrity and trustworthiness of blockchain networks.
The severity and recurrence of these security threats are evident in real-world examples, such as 51% attacks, double-spending attacks, and smart contract vulnerabilities. These attacks have led to substantial financial losses and have eroded public confidence in blockchain systems. The increasing prevalence of reported vulnerabilities and the rise in cybercrime further emphasize the urgency of addressing these challenges through robust and adaptive security measures.
Through our research questions, we identified and categorized common blockchain attacks, evaluated their impact on security and system integrity, and assessed the efficacy of existing detection and mitigation strategies. Our findings revealed that smart contract vulnerabilities and double-spending attacks represent the most significant threats due to their prevalence and the potential for large-scale financial damage. Moreover, the need for tailored solutions is evident as blockchain applications across industries exhibit varying security requirements and threat landscapes.
This research underscores the importance of employing customized security measures tailored to the unique operational needs of blockchain applications. For instance, financial systems require enhanced consensus mechanisms and cryptographic techniques to prevent double spending, while supply chain networks benefit from robust node reputation systems and secure routing protocols to maintain data integrity. By carefully analyzing the unique characteristics and threats faced by blockchain systems, developers and practitioners can implement effective, context-specific security measures.
Our study contributes to the body of knowledge on blockchain security by offering a detailed examination of attack mitigation techniques and their practical applications. It equips researchers and practitioners with actionable insights to help them make informed decisions and adopt robust security strategies. Furthermore, it highlights the limitations of current security mechanisms, encouraging the exploration of cutting-edge technologies to address emerging threats. A notable limitation of this study is the lack of an in-depth discussion about the mitigation or prevention techniques for the attacks, although some studies have in-depth discussions on mitigating or preventing those attacks, which we want to address in future research.
Looking ahead, we recognize that the dynamic nature of blockchain technology necessitates ongoing research and collaboration to address new vulnerabilities and attack vectors. Emerging technologies, such as quantum-resistant cryptography and artificial-intelligence-based anomaly detection, hold promise for further enhancing the security and resilience of blockchain systems. By prioritizing security and adopting proactive measures, we can ensure the trust in and integrity of blockchain technology, paving the way for its widespread adoption and transformative impact.
M.K.S. designed the systematic search strategy, performed keyword analysis, conducted the literature review, contributed to the results and discussion, created the visualizations, and participated in manuscript refinement; B.S. conceived the initial idea for the paper and contributed to the development of the introduction and methodology; M.M.H. assisted in the preparation of the results and contributed to the discussion; M.J.H.F. coordinated the overall research effort and contributed to the abstract, title, introduction, and conclusion; N.A., S.T. and A.S. participated in the analysis of the included literature; H.S. provided academic supervision, critical feedback, and guidance, led scholarly discussions, and finalized the manuscript. All authors have read and agreed to the published version of the manuscript.
The authors declare no conflicts of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Figure 1 PRISMA flow diagram for study selection in the systematic review.
Figure 2 A comprehensive classification of attacks in blockchain technology.
Figure 3 Publication frequency based on included studies.
Figure 4 Distribution of attack categories and their frequencies in the included studies.
Figure 5 Distribution of core reasons for vulnerabilities across the included studies.
A comprehensive overview of blockchain security attacks.
| Attack Name | Cause of Vulnerability | Impact on Sectors | References |
|---|---|---|---|
| 51% Attack | Low network hash rate; Centralized mining power; Renting computational resources; Lack of honest economic incentives; Low attack execution cost. | Cryptocurrency exchanges; DeFi platforms; Blockchain-based supply chains. | [ |
| Smart Contract Vulnerabilities | Programming errors; Complex business logic; Inadequate testing and auditing; Lack of standards and best practices. | DeFi platforms; Cryptocurrencies; DAOs; Supply chain systems. | [ |
| Man-in-the-Middle Attack | Intercepted communication between nodes; Stolen private keys; Tampered transaction data. | Blockchain networks; Cryptocurrency exchanges; Healthcare. | [ |
| Replay Attack | Shared transaction history; Incompatible message formats; Lack of replay protection. | Cryptocurrency exchanges; DeFi platforms; Blockchain-based supply chains. | [ |
| Double-Spending Attack | Delayed transaction confirmation; Network latency and forks; Acceptance of unverified transactions. | Cryptocurrencies; Crypto-payment systems; PoW-based solutions. | [ |
| Routing Attack | Manipulated routing protocols; False routing information. | P2P networks; PoW/PoS-based systems; Healthcare. | [ |
| Sybil Attack | Fake node insertion in routing tables; Ignoring node authentication; Centralized computational authority. | Cryptocurrencies; Blockchain-based supply chains; P2P networks. | [ |
| Race Attack | Simultaneous contradictory transactions; Lack of transaction validation. | P2P networks; Cryptocurrency exchanges; Crypto-payment systems. | [ |
| Eclipse Attack | Isolation of nodes by malicious peers; Exploitation of untrusted peers; Weak peer-to-peer protocols. | PoW-based solutions; Healthcare; Blockchain networks. | [ |
A summarized overview of the detection and mitigation techniques for blockchain attacks.
| Attack | Detection Techniques | Security Measures (Mitigation) | References |
|---|---|---|---|
| 51% Attack | Monitor unusual network behavior; Analyze mined blocks and block reorganization; Detect double spending and suspicious transactions; Observe mining pool activities; Implement alerting mechanisms. | Use robust consensus mechanisms; Increase network size and decentralization; Apply checkpoints and diverse mining communities. | [ |
| Smart Contract Vulnerabilities | Static analysis (e.g., symbolic execution, control flow graphs); Dynamic analysis (e.g., fuzz testing, runtime monitoring); Formal verification and code auditing. | Adopt best design patterns and access control mechanisms; Conduct regular security audits; Use upgradeable contracts and comprehensive testing. | [ |
| Double-Spending Attack | Monitor transaction verification times; Analyze block confirmation and ledger consistency; Detect suspicious fork creation. | Increase confirmation times and block consistency; Use key-evolving cryptography; Disable unconfirmed connections. | [ |
| Race Attack | Monitor transaction broadcasts; Insert observer nodes for suspicious activity. | Periodic transaction monitoring; Deploy observer nodes for continuous surveillance. | [ |
| Replay Attack | Detect duplicate transactions; Analyze transaction history in forked chains. | Implement strong replay protection; Use coin-splitting techniques; Opt-in replay protection. | [ |
| Sybil Attack | Monitor node reputation and activity; Identify suspicious node behavior during PoW initiation. | Restrict high-computing-power nodes; Implement non-refundable deposits (e.g., currency burning); Use fidelity bonds and coin-age mechanisms. | [ |
| Eclipse Attack | Use anomaly detection tools; Run seeding and eliminate exploitation windows. | Implement timestamp-based protocols; Use threat database models for detection; Enhance network load monitoring. | [ |
| Man-in-the-Middle Attack | Detect unusual network activity; Check transaction discrepancies; Monitor potentially malicious nodes. | Encrypt communication between nodes; Use multi-factor authentication for private key access; Employ public key infrastructure and biometric authentication. | [ |
| Routing Attack | Monitor network traffic patterns; Use reputation systems to identify malicious nodes. | Secure routing protocols using digital certificates; Apply distributed consensus mechanisms. | [ |
Blockchain attack targets and key factors influencing likelihood for different types of attacks.
| Attack Type | Target | Key Factors Influencing Likelihood |
|---|---|---|
| 51% Attack | PoW (Bitcoin, Litecoin, Small Chains) | Hash rate concentration, mining cost, network difficulty |
| Sybil Attack | PoS, Permissionless Chains | Node diversity, validator stake, network entry barriers |
| Eclipse Attack | P2P Nodes | Network size, node connectivity, topology |
| Smart Contract Vulnerabilities | Ethereum, BSC, DeFi Contracts | Code vulnerabilities, audit quality, formal verification |
| Double-Spending Attack | PoW/PoS Networks | Transaction finality, block confirmation time |
| Routing Attack | All Blockchain Networks | ISP dependency, network propagation speed |
| Replay Attack | Transaction Authentication | Weak authentication, transaction replay capability |
| Man-in-the-Middle Attack | Blockchain Communication Layer | Unencrypted communication, weak key exchange |
| Race Attack | PoW/PoS Networks (Fast Transaction Confirmations) | Transaction propagation speed, network latency, low block confirmation requirements |
Keyword frequencies of the included studies.
| Keyword | Count |
|---|---|
| Technology/Technologies | 4378 |
| Security | 11,518 |
| Systems | 3697 |
| Blockchain | 16,400 |
| Distributed | 2112 |
| Privacy | 4454 |
| Information | 5438 |
| Control | 2881 |
| Encryption | 1633 |
| Detection | 7043 |
| Prevention | 381 |
| Attack | 5450 |
| Vulnerability/Vulnerabilities | 8422 |
| Protocol | 1901 |
| Transaction | 5249 |
| Network | 6848 |
| Bitcoin | 1971 |
| Ethereum | 4672 |
| Smart | 13,760 |
| Internet | 2375 |
| Cryptocurrency/Cryptocurrencies | 1221 |
| Strategies | 554 |
| Strategy | 590 |
| Mitigation | 385 |
| Cryptography | 642 |
| IoT | 633 |
| Tether | 20 |
Cybersecurity attacks and their impacts based on their attack categories.
| Reference(s) | Attack Category | Impact Result |
|---|---|---|
| [ | AI/ML Security | Unreliable vulnerability detection, financial exploitation risks, missed security flaws in contract audits, and unpatched vulnerabilities in deployed contracts. |
| [ | Application Vulnerability | Undetected execution faults in Web3 applications, leading to potential security breaches. |
| [ | Authentication or Authorization | User errors, private key leaks, unauthorized access attempts, patient data exposure, healthcare service disruptions, legal liabilities, data breaches, loss of intellectual property, regulatory penalties, compromised data integrity, financial loss, reputation damage, operational disruption, unauthorized vehicle use, financial fraud, identity theft, data theft, and unauthorized smart contract execution. |
| [ | Blockchain Security | Performance degradation, increased attack surface, data tampering risks, election result manipulation, transaction bottlenecks, and reduced performance. |
| [ | Centralization Vulnerability | Anonymity loss and privileged operations due to centralized control. |
| [ | Cloud Security | State continuity violations in cloud-based systems. |
| [ | Code Injection | Database manipulation through malicious code injection. |
| [ | Code Reuse Vulnerability | Propagation of known security flaws and widespread deployment of vulnerable contracts. |
| [ | Critical Infrastructure | Grid data manipulation in critical infrastructure systems. |
| [ | Cross-Chain Vulnerability | Unintended contract behavior exploitation, cross-chain transaction fraud, and privacy leaks. |
| [ | Cryptocurrency Vulnerability | Bitcoin loss due to script flaws in cryptocurrency systems. |
| [ | Cryptographic Attack | Unauthorized data access in smart grids, data breaches, loss of user trust, legal liabilities, data theft, espionage, compromised encryption, and privacy leakage. |
| [ | Data Integrity | Loss of critical digital evidence, unauthorized modifications, and compromised tenant data. |
| [ | Data Security | Data loss, unauthorized access, medical data breaches, identity theft, and loss of control over information. |
| [ | Denial of Service | Service disruption, network security compromise, financial losses, blockchain slowdown, transaction delays, and smart contract failure. |
| [ | Digital Asset Theft | Player data compromises in digital asset systems. |
| [ | Domain Security | Phishing attacks and lost domain access due to security vulnerabilities. |
| [ | Educational | Knowledge gaps in blockchain security education and training. |
| [ | Electoral Fraud | Vote tampering and lack of transparency in electoral systems. |
| [ | Financial Fraud | Financial fraud, illicit contract use, fund misuse, investor losses, scam tokens, unfair governance token distributions, and cyber threats to financial stability. |
| [ | Fraud/Identity Theft | Fake degrees, credential fraud, compromised health data, and fraudulent registrations. |
| [ | Identity Management | Higher threat exposure in self-sovereign identity systems. |
| [ | Incident Response | Delayed security responses and uncoordinated mitigation efforts. |
| [ | Insider Threat | Compromised smart grid infrastructure due to insider threats. |
| [ | Intellectual Property Theft | Revenue loss for copyright holders, unauthorized data usage, and copyright infringement. |
| [ | IoT Vulnerability | Data interception, unauthorized access, widespread IoT network vulnerabilities, data leaks, privacy issues, sensitive data exposure, financial loss, and compromised industrial control systems. |
| [ | Malware | Data loss and ransom demands due to malware attacks. |
| [ | Media Manipulation | Public misinformation, identity theft, political and financial misinformation risks, fake news, and lack of content authenticity. |
| [ | Memory Vulnerability | Memory corruption and code execution vulnerabilities. |
| [ | Network Attack | Operational disruptions, compromised data integrity, financial losses, service disruption, data tampering, cybercriminal activities, and denial-of-service (DoS) attacks. |
| [ | Oracle Manipulation | Flash-loan-based financial exploits, price manipulations, inaccurate data inputs, and market manipulation. |
| [ | Phishing/Social Engineering | User credential theft, financial fraud, and unauthorized fund theft. |
| [ | Privacy Violation | Privacy violations in AI models, user identity theft, misinformation, data exposure, identity leaks, compromised vehicle safety, legal and financial risks, and unauthorized access to stored blockchain data. |
| [ | Scalability Security | High fees and slow transactions due to scalability issues. |
| [ | Smart Contract Vulnerability | Financial exploits, blockchain instability, unauthorized fund transfers, contract hijacking, financial fraud, money laundering, and irreversible financial losses. |
| [ | Software Testing | Security vulnerabilities overlooked, undetected software vulnerabilities, and unreliable security testing results. |
| [ | Supply Chain Attack | Malfunctioning electronic components and compromised software due to supply chain attacks. |
| [ | Threat Intelligence | Slow response to cyber threats due to inadequate threat intelligence. |
| [ | Transaction Manipulation | Financial loss, disruption of services, unauthorized transactions, and financial manipulation. |
| [ | Trust Exploitation | False service discovery and compromised interactions due to trust exploitation. |
| [ | Trusted Execution Environment | Vulnerabilities in trusted execution environments. |
| [ | Various (studies that discuss attacks from multiple categories) | Online fraud, identity theft, unauthorized access to ECUs, denial of service (DoS), financial losses, privacy violations, data breaches, network collapse, theft of NFTs, and unauthorized code execution. |
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; Saha Bilash 2 ; Hasan Md Mehedi 2
; Hossain Faruk Md Jobair 1
; Anjum Nafisa 2
; Sharaban, Tahora 2 ; Aiasha, Siddika 2 ; Hossain, Shahriar 3 1 Department of Computer Science, New York Institute of Technology, New York, NY 10023, USA; [email protected]
2 Department of Information Technology, Kennesaw State University, Kennesaw, GA 30144, USA
3 Center for Cybersecurity, University of West Florida, Pensacola, FL 32514, USA