Ethereum co-founder Vitalik Buterin recently gave his views on AI-powered code audits, why we should explore this option, and what benefits the blockchain world can reap from this concept.
The revolutionary blockchain world has brought so many innovations to the financial world that its importance cannot be suppressed. While the use cases it serves have made life easier for many, some lingering concerns still need to be addressed because the security and safety of users’ funds are at stake.
In general, cryptocurrency and blockchain have been riddled with incidents where people have experienced losing funds due to scams and hacking attempts. In 2023 alone, crypto users lost an estimated $2 billion due to hacks and scams, which is alarming.
The blockchain ecosystem Ethereum is at the top of the list because it has the most extensive ecosystem and most of the top-notch blockchain-related projects, which makes it the most favorable target for hackers and scammers because of the possibility of exploiting most funds from the ecosystems that are linked with Ethereum.
To counteract these growing strings of attacks and ensure the integrity and security of the systems, Vitalik Buterin, the famous co-founder of Ethereum, has come up with a solution that he believes can prove instrumental in addressing these growing hacking attempts.
He believes that the blockchain world needs to leverage the power of Artificial Intelligence (AI) to conduct code audits that can cover the loopholes that hackers exploit.
Buterin pointed out the necessity for doing code audits using AI because hackers are trying to exploit vulnerabilities in the smart contracts that power the decentralized applications (dApps). Even minor vulnerabilities in these self-executing contracts can have catastrophic consequences, as the crypto community has witnessed such events.
We will look more closely at what sort of loopholes hackers try to exploit and why traditional code audits have failed to address these problems till now. The article delves into Buterin’s proposal, exploring its potential benefits, limitations, and broader implications for enhancing blockchain security.
Why Traditional Code Audits Fall Short
At one time, traditional code audits would have sufficed to address the problems where hackers exploit loopholes in the system to siphon off funds from unsuspecting victims. However, since they rely primarily on human experts doing manual reviews of the system, they might need to catch something that catches the eyes of the attackers.
We will look at how traditional code audits may fall short of addressing these problems and how hackers can exploit these shortcomings:
Scalability
Increasing Complexity: Smart contracts are self-executing digital contracts that automatically enforce and execute predefined terms and conditions, facilitating trustless and transparent transactions on blockchain networks.
The problem is that these codes have become increasingly complex as they involve intricate logic, interactions with multiple parties, and integration with various protocols and standards.
The problem is that these complex codes and logic take several lines of code to implement, and the probability arises that human auditors might miss something when going over these lines of code to look for any flaws. When the scenario occurs that hackers manage to find these flaws, then they take full advantage of exploiting these flaws.
Volume: The number of smart contracts developed and deployed in the blockchain world is rising. With each contract representing a potential failure or vulnerability, the growing volume increases the challenges for human auditors to review them thoroughly and ensure that each line code is working correctly and that no point of failure is present in the overall system.
Bottlenecks and Delays: Traditional audit methods face bottlenecks due to the complexity and volume of large datasets. Human auditors can only review a limited number of contracts within a given timeframe, leading to delays in the auditing process. Sometimes, these delays are why hackers find a backdoor into the systems and try to exploit their vulnerabilities.
Cost
Manual Effort: Traditional audits rely heavily on manual effort from highly skilled human auditors. The drawback of using these highly skilled task forces to conduct audit duties is that they charge a lot to complete this task.
Since the safety and security of the system are dependent on this audit, not to mention the reputation of the project, the developers of the project have to pay the high cost to make sure that their product doesn’t contain any loopholes.
Prohibitive for Smaller Projects: Paying high costs to external auditors who specialize in finding weaknesses in your designed system is only some people’s cup of tea.
Major blockchain projects that have millions of dollars in investments and a dedicated fund for such tasks can afford to hire auditors to complete these tasks. Still, smaller projects or individual developers with limited financial resources cannot bear these costs, which creates a barrier to entry for ensuring the security of their smart contracts.
Subjectivity
Human Biases and Judgement: The next cause of concern in the problems related to traditional audit methods is that relying on human judgment can yield inconsistent results. Various human biases can influence manual audits.
The auditors may need to consider any existing flaws or prioritize some other section of the code based on their personal preferences, past experiences, or the predetermined notion that what they are doing is the best course of action.
These biases and inconsistencies by auditors due to their personal choices can lead to critical issues being overlooked, even if it was not their intention.
The resulting outcome would mean that the system has been deployed with flaws still present, which poses significant risks to the security and reliability of the audited system.
Therefore, it’s crucial for auditors to remain vigilant and employ systematic approaches to minimize the impact of biases and ensure thorough and impartial evaluations of the codebase.
Limited Scope
Incomplete Evaluation: Traditional audits, while valuable in targeting known vulnerabilities and compliance requirements, may fail to provide a comprehensive assessment of smart contracts due to their selective focus and inadequate evaluation of complex interactions with external systems, protocols, and data sources.
Such limitations leave potential vulnerabilities undetected, posing risks to the overall security posture of the contract.
Overall, these limitations underscore the need for alternative approaches to code auditing, and this is where automated tools that use AI technology and techniques may prove helpful to fix the existing issues and address the evolving challenges of securing smart contracts effectively.
Examples of incidents where Hackers exploited Smart Contract Vulnerabilities
To get an understanding of why Buterin suggested that we need to leverage the power of AI technology to address smart contract vulnerabilities, we can take a look at these incidents where hackers managed to exploit the network for millions of dollars and caused significant doubts toward the security feature of the blockchain networks.
The DAO Hack (2016)
The hack done in 2016 targeted the Decentralized Autonomous Organization (DAO), a pioneering project on the Ethereum blockchain.
An attacker exploited a reentrancy vulnerability in the DAO’s smart contract, allowing them to siphon off over $150 million worth of ETH.
Wormhole Bridge Exploit (2022)
The Wormhole Bridge, a famous cross-chain bridge facilitating token transfers between Ethereum and Solana, fell victim to a sophisticated attack.
Hackers exploited a flaw in the bridge’s smart contract, resulting in the theft of over $320 million worth of cryptocurrency.
Multichain Bridges Exploit (2023)
On July 6, 2023, a series of exploits targeted Multichain, a cross-chain bridge protocol, resulting in the loss of nearly $130 million worth of cryptocurrency.
Attackers siphoned funds from the protocol’s bridges on the Fantom, Moonriver, and Dogechain networks, stealing tokens like wBTC, USDC, USDT, and several altcoins.
Multichain developers identified the unusual activity and advised users to revoke smart contract approvals related to the protocol. It highlighted the vulnerabilities inherent in such systems and the need for enhanced security measures.
These incidents exposed the critical need for thorough code audits and robust security practices in the blockchain ecosystem.
How do AI-powered code Audits address these problems?
AI-powered code audits represent a significant advancement in the realm of blockchain security, offering a range of benefits that address the limitations of traditional audit methods:
Increased Efficiency and Wider Scope
The AI algorithms have the potential to excel at processing vast amounts of data sets quickly and comprehensively compared to traditional audit tools. The AI tools can leverage the capabilities of machine learning algorithms and natural language processing techniques that can power the AI audit tools to analyze large codebases with greater efficiency than human auditors.
So, when these AI-powered audit tools make full use of these modern methodologies, they not only process data faster with more incredible speed but also enhance their scope of analysis and set their targets for detecting subtle vulnerabilities that can hide from human eyes and analyze complex patterns that may go unnoticed in manual audits.
Furthermore, these AI tools can be designed to continuously learn and improve their detection capabilities, adapting to evolving threats and coding practices within the blockchain ecosystem.
Reduced Cost and Objectivity
Automated processes in AI-powered audits significantly reduce the time and resources required for thorough code analysis, resulting in lower audit costs compared to traditional manual audits.
The cost-effectiveness factor makes security assessments accessible to a broader range of blockchain projects, including smaller startups and individual developers with limited budgets.
Moreover, AI algorithms are inherently objective and devoid of human biases that can sometimes affect the judgment of human auditors. The impartiality leads to more consistent and reliable results, which proves instrumental in instilling greater confidence in the security assessments provided by AI-powered tools.
Enhanced Security Across the Ecosystem
AI-powered audits can be applied proactively across various aspects of blockchain security, including smart contract analysis, protocol security assessment, and continuous monitoring of deployed contracts.
By identifying vulnerabilities early in the development lifecycle, AI tools help prevent potential exploits and security breaches, bolstering the overall resilience of the blockchain ecosystem.
Furthermore, the proactive nature of AI-powered audits can foster trust and confidence among users, investors, and stakeholders, contributing to the long-term sustainability and growth of decentralized applications (dApps) and decentralized finance (DeFi) protocols.
Additionally, the improved security posture facilitated by AI audits may lead to lower insurance costs for dApps and DeFi protocols as insurers gain greater confidence in the risk management practices implemented within the ecosystem.
Challenges and Risks
While Buterin’s statements regarding leveraging the power of AI technology to conduct audits in smart contracts can prove very beneficial in the short and long run, some risks are still associated with completely giving control to such mechanisms.
The risks associated with it cannot be ignored, and you should also understand what drawbacks such systems can face, helping you make an informed judgment on how much you should trust the outcome of such tools.
Data Availability
Training AI models for code audits necessitates providing abundant labeled data, particularly code samples containing known vulnerabilities.
However, obtaining such data can be challenging, as it often requires access to diverse repositories of vulnerable code or historical records of security incidents.
Additionally, ensuring the quality and relevance of the labeled data is significant to the effectiveness of AI models.
Addressing this challenge involves building comprehensive datasets through collaboration among industry stakeholders, open-source contributions, and partnerships with cybersecurity research organizations.
Interpretability
While AI algorithms can efficiently identify vulnerabilities, the need for interpretability in their decision-making processes presents a significant challenge.
Developers must clearly explain why specific code segments are flagged as vulnerable to remediate issues effectively.
Enhancing the interpretability of AI models involves developing techniques to visualize and explain the features and patterns driving vulnerability detection.
If such techniques are developed, then it enables developers to gain insights into the underlying reasons for vulnerabilities and facilitates targeted improvements in code quality and security practices.
False Positives
AI-powered code audits may generate false positives, where the model erroneously identifies code segments as vulnerable when they are not.
Mitigating false positives is crucial to avoid inundating developers with irrelevant findings, leading to inefficiencies and distracting from genuine security threats.
AI algorithms must be refined through iterative training and validation processes, leveraging feedback from human auditors to fine-tune detection thresholds and reduce false positives.
Additionally, implementing human review and validation mechanisms helps ensure the accuracy and reliability of AI-generated findings.
Evolving Threats
The cybersecurity landscape constantly evolves, with attackers continuously developing new tactics and exploiting emerging vulnerabilities.
AI models must adapt to these evolving threats to maintain their effectiveness in identifying and mitigating risks.
It requires ongoing monitoring of emerging threat intelligence, regular updates to AI algorithms, and collaboration with cybersecurity experts to stay one step ahead of emerging attack vectors and defense strategies.
Additionally, implementing feedback loops that incorporate real-world security incidents and conflicting techniques into AI training processes helps ensure the resilience and relevance of AI-powered code audits in the face of evolving threats.
Final Thoughts
Integrating AI-powered code audits represents a promising frontier in bolstering blockchain security. While addressing shortcomings of traditional methods, such as scalability and subjectivity, challenges like data availability and interpretability remain. Yet, by mitigating risks through collaboration and refinement, AI holds significant potential in fortifying the integrity and resilience of decentralized systems against evolving threats.
