Is Auditing Smart Contracts Using AI More Cost-Effective and Secure?

Smart contract security stands as an unequivocal priority. These self-executing contracts frequently facilitate the transfer of digital assets, sensitive data, and, at times, entire decentralized applications (dApps). Any vulnerability or oversight within the code of a smart contract can result in catastrophic consequences—ranging from financial losses to data breaches, undermining trust in blockchain systems.

Integrating artificial intelligence (AI) has emerged as a promising solution to address these concerns and fortify the security of smart contracts. AI, equipped with the ability to scrutinize extensive code repositories, identify intricate patterns, and pinpoint potential vulnerabilities, is poised to revolutionize smart contracts’ audit process.

Challenges in Smart Contract Auditing

Ensuring the security of smart contracts is an ever-evolving endeavor, and while AI offers promising solutions, it also confronts several significant challenges:

Constraints of the Context Window in AI Models

In smart contract auditing, AI models, particularly large language models (LLMs), face a unique challenge—the constraints of the context window. This window functions like memory, dictating how much code an AI model can analyze at any given time. While this limitation might not be a significant concern for simpler contracts and tokens, it becomes increasingly restrictive when assessing complex blockchain projects.

These intricate projects often comprise many smart contracts that intricately interact with each other. The result? An interwoven tapestry of code that can’t be feasibly analyzed in isolation. This constraint underscores the need for AI models to grapple with an intricate web of dependencies and interactions among smart contracts, all while staying within the confines of their context window.

The Need for Continuous AI Model Updates Due to Evolving Threats

Another formidable challenge arises from the dynamic nature of the blockchain and the continuously evolving threat landscape. AI models rely on historical data and known vulnerabilities to assess like their human counterparts. However, new vulnerabilities emerge with alarming frequency in the fast-paced blockchain world.

AI models must be continuously updated to effectively detect these new vulnerabilities—an endeavor that presents complexities. Most known vulnerabilities are scrutinized, with comprehensive data and insights readily available. Conversely, emerging vulnerabilities often lack the requisite data for extensive training of an LLM; this places the onus on rapid model adaptation to avoid emerging threats.

Current Limitations of AI Models in Detecting Complex Vulnerabilities

While AI has made significant strides in various domains, including natural language processing and image recognition, its effectiveness in detecting complex vulnerabilities within smart contracts is a work in progress. Tests conducted by experts reveal that even advanced AI models, such as ChatGPT4, Bard, and Claud 2, are primarily proficient at identifying straightforward bugs within smart contracts.

Understanding the intricacies of a smart contract and assessing whether it is susceptible to rug pulls, intricate exploits, or novel vulnerabilities remains a formidable challenge for current AI models. For instance, when queried to provide a specific section of code where an issue was detected, an AI model may offer a similar, albeit incorrect, code snippet; this presents an inherent limitation: understanding how the AI model arrived at its conclusion and subsequently, debugging becomes a non-trivial task.

Lack of Transparency in AI Decision-Making

Transparency in AI decision-making is a cornerstone of trust, particularly in the context of smart contract audits. However, current AI models often lack transparency, leaving auditors and developers in the dark about the reasoning behind their assessments. It becomes unclear whether the code provided to the model fits within the context window entirely or only partially.

This opacity presents a fundamental challenge for smart contract auditors. Without clarity on the information the AI model used to reach its conclusions, it becomes challenging to validate its recommendations and make informed decisions regarding code changes or security improvements.

In the face of these challenges, the question arises: Can AI truly serve as an effective tool for auditing smart contracts? While these hurdles are formidable, we cannot dismiss the potential of AI to enhance smart contract security.

AI as a Tool for Smart Contract Audits

AI gives auditors a remarkable ability: a swift and comprehensive understanding of smart contracts. 

AI equips auditors and developers with essential insights by rapidly summarizing a contract’s functionality and purpose. This foundational understanding streamlines the audit process, enabling professionals to grasp a contract’s primary objectives efficiently. AI is a guiding compass through the intricate world of smart contracts.

Identifying Potential Vulnerabilities with AI’s Vigilant Assistance

AI’s prowess in identifying potential vulnerabilities is a game-changer. AI is an adept sentinel that can scan code repositories and recognize patterns indicative of vulnerabilities. It meticulously scans code, flagging areas that warrant closer examination and highlighting potential weaknesses.

While AI often excels in identifying more straightforward vulnerabilities, its capability to rapidly and comprehensively analyze code makes it an invaluable tool. It acts as the first line of defense, helping auditors pinpoint areas that require heightened scrutiny. In doing so, it significantly fortifies the security assessment process.

Elevating Audits Through Synergy with Human Auditors

AI’s role transcends mere automation, acting as a force multiplier alongside human auditors. Seamless collaboration with human experts results in the reduction of human error.

Despite their expertise, human auditors may occasionally overlook subtleties or miss nuanced vulnerabilities. In contrast, AI shines in pattern recognition and thorough code analysis. Consequently, it can detect issues that might elude even the most diligent human auditors. This harmonious collaboration, combining human proficiency with AI’s computational insight, creates a formidable auditing partnership.

More explicit Issue Descriptions with AI Assistance

In smart contract auditing, precise and clear communication holds paramount importance. AI emerges as a skilled tool for articulating identified issues and translating technical findings into easily understandable language. This capability proves particularly valuable for non-native speakers, bridging language gaps and fostering effective communication between auditors and developers.

AI generates concise yet detailed explanations of detected vulnerabilities, facilitating an understanding of an issue’s severity and potential impact. This clarity in communication expedites issue resolution and ensures that security concerns receive the attention they deserve.

Case Study: AI Identifying a Problem in a Smart Contract

To exemplify the practical application of AI in smart contract auditing, we present a case study that highlights a scenario where AI successfully identified a problem within a smart contract. This case underscores the real-world relevance of AI in bolstering the security of blockchain-based agreements.

Description of the Smart Contract and Its Purpose

The smart contract under scrutiny in this case study is a decentralized escrow service designed to facilitate secure transactions between buyers and sellers within a blockchain-based marketplace. Its purpose is to hold funds in escrow until predefined conditions are met, ensuring trust and fairness in digital transactions.

Below is an excerpt of the Solidity code representing the key functionality of the smart contract:

For this audit, we employed the expertise of a state-of-the-art AI model, GPT-3.5. GPT-3.5, powered by OpenAI, is renowned for its natural language processing capabilities and aptitude for comprehending and analyzing complex code structures. Its pattern recognition and code scrutiny prowess make it an ideal tool for smart contract audits.

Step-by-Step Analysis of How AI Identified a Problem in the Contract

The AI audit of the smart contract proceeded through a systematic process of code analysis, pattern recognition, and issue identification. Here is a breakdown of the steps:

Code Scrutiny: The AI model commenced by analyzing the entire codebase of the smart contract, reviewing its functions, variables, and logical structures.

Pattern Recognition: GPT-3.5 employed its pattern recognition capabilities to identify potential vulnerabilities or irregularities in the code. It compared the contract’s structure to established best practices and known security patterns.

Identification of a Critical Flaw: During this analysis, the AI model identified a critical flaw within the contract. It recognized that the contract lacked a secure mechanism to prevent funds from being withdrawn prematurely, potentially leading to disputes and financial losses.

Explanation of How the AI’s Assistance Improved the Auditing Process

The AI’s contribution to the auditing process proved invaluable. It expedited identifying a significant issue that might have been overlooked in a manual review. By pinpointing the flaw, the AI model enabled auditors to focus their efforts on addressing this critical vulnerability promptly.

The AI’s assistance also offered clarity in the issue description. It concisely summarized the problem and its potential consequences, facilitating effective communication between auditors and developers.

The Future of Smart Contract Audits with AI

As we peer into the horizon of smart contract security, it becomes evident that AI holds the key to a more robust and efficient auditing process. The future promises exciting developments in this coordination between technology and security.

Specialized AI Models for Specific Vulnerabilities

One of the most promising advancements lies in creating specialized AI models tailored to address specific vulnerabilities. While current AI models exhibit proficiency in identifying common issues, the future heralds models finely tuned to detect nuanced vulnerabilities. For instance, models could specialize in detecting reentrancy attacks, front-running vulnerabilities, or flash loan exploits.

These specialized AI models will draw from extensive, high-quality vulnerability datasets, allowing them to recognize and categorize vulnerabilities accurately. As a result, auditors can expect a more precise and targeted approach to security assessments.

The Role of Automated Tests in Enhancing Security

Automated tests will play an increasingly pivotal role in enhancing the security of smart contracts. These tests, driven by AI, will go beyond identifying vulnerabilities and actively assessing a contract’s security posture in real-time. They will continuously monitor blockchain transactions, identifying suspicious patterns and proactively alerting auditors and developers to potential threats.

By automating security checks and deploying AI-driven monitoring, the blockchain community can bolster the proactive defense of smart contracts, reducing the window of vulnerability and mitigating potential risks more swiftly.

Collaboration Between AI and Human Auditors

The most effective approach to smart contract security lies in harmonious collaboration between AI and human auditors. While AI offers unparalleled computational capabilities, human auditors provide critical subject expertise and nuanced judgment. Together, they form a dynamic duo capable of addressing familiar and novel security challenges.

Human auditors with AI-generated insights can make more informed decisions and conduct thorough audits efficiently. The coordination between human intuition and AI’s computational prowess will set the stage for a new era of smart contract security.

The Evolving Landscape of AI in Blockchain Security

The landscape of AI in blockchain security is continually evolving. AI models, much like the blockchain itself, are in a state of constant improvement. They are learning from new data, adapting to emerging threats, and refining their ability to identify vulnerabilities.

Furthermore, the integration of AI in other facets of blockchain security, such as anomaly detection, network security, and consensus algorithm analysis, is on the horizon. This broader application of AI will create a comprehensive security ecosystem that safeguards the entire blockchain infrastructure.


As the blockchain arena continues to advance, the imperative of securing smart contracts remains paramount. Within this dynamic landscape, the convergence of technology and security, specifically AI, emerges as a powerful ally in fortifying the integrity of smart contract audits. This article has illustrated the tangible benefits of AI through a compelling case study, shedding light on its role in expediting vulnerability detection, enhancing communication, and streamlining the auditing process. Despite its inherent limitations, AI harmonizes seamlessly with human expertise, accelerating risk identification and mitigation. The future of smart contract audits appears promising as specialized AI models, automated tests, and collaborative efforts between human auditors and AI converge to bolster blockchain security. As the blockchain community embraces these advancements, it inches closer to realizing a trustless and secure digital ecosystem where smart contracts execute with unwavering dependability.

In this dynamic coordination between technology and security, the horizon brims with innovation and enhancement possibilities. The pursuit of resilient and impervious smart contracts forges ahead, guided by the collective wisdom of human intellect and the capabilities of artificial intelligence. As the future unfolds, the blockchain community stands resolute, reinforcing its foundations to ensure the enduring fulfillment of decentralized, secure, and transparent transactions.


How does AI improve the efficiency of smart contract audits?

AI accelerates auditing by providing quick contract overviews, identifying potential vulnerabilities, and aiding in issue description. It automates tasks that would be time-consuming for human auditors, enabling them to focus on critical areas.

Can AI models detect complex vulnerabilities in smart contracts?

While AI models excel at identifying common vulnerabilities, detecting complex and unique issues can be challenging. Specialized AI models trained on high-quality vulnerability datasets show promise in addressing this limitation.

How can AI and human auditors collaborate effectively?

The collaboration between AI and human auditors leverages the strengths of both. AI provides computational power and pattern recognition, while human auditors offer subject expertise and nuanced judgment. Together, they form a robust auditing team.

What role do specialized AI models play in smart contract security?

Specialized AI models can detect specific vulnerabilities, such as reentrancy attacks or front-running vulnerabilities. They draw from extensive vulnerability datasets, enabling precise and targeted security assessments.

How do automated tests enhance smart contract security?

Automated tests, driven by AI, continuously monitor blockchain transactions and proactively alert auditors to potential threats. They play a vital role in proactive defense, reducing the window of vulnerability.

What can we expect in the future of smart contract audits with AI?

The future holds promise regarding AI advancements, with specialized models, automated tests, and broader AI applications in blockchain security. This coordination between AI and human auditors will fortify smart contract security and pave the way for a more secure digital realm.

Disclaimer. The information provided is not trading advice. Cryptopolitan.com holds no liability for any investments made based on the information provided on this page. We strongly recommend independent research and/or consultation with a qualified professional before making any investment decisions.

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