How to Use LLMs to Automate Solidity Code Audits

With the rise of DeFi, NFTs, and enterprise blockchain platforms, the complexity of smart contracts has grown tremendously. Unfortunately, so has the risk of vulnerabilities, which can lead to serious security issues and loss of funds. Older auditing methods, like manual code reviews and rule-based checks, are no longer equipped to handle the scale and speed of these innovations. 

This is where advanced AI models, capable of reading and generating code, offer a game-changing solution by automating Solidity audits. These models enhance both the efficiency and accuracy of security assessments, ensuring faster detection of issues.

Our LLM-based auditing tools analyze contract structures, detect vulnerabilities, and even suggest fixes for issues like improper tokenomics or lack of proper authorization. IdeaUsher has worked closely with numerous Web3 startups and enterprise blockchain providers to implement these solutions, helping them scale their security efforts while ensuring contract integrity. We’re here to spread our knowledge through this blog, so you can begin automating your Solidity code audits with LLMs.

Key Market Takeaways of LLMs for Solidity Code Audits

According to GrandViewResearch, the global market for large language models is set to grow rapidly, with projections estimating an increase from USD 5.6 billion in 2024 to over USD 35 billion by 2030. This growth is partly driven by the rise of blockchain technology, which has created a strong demand for automated solutions like LLMs to audit Solidity code for smart contracts. As decentralized applications continue to gain traction, the need for scalable and accurate auditing tools powered by LLMs has become essential.

Source: GrandViewResearch

LLMs are increasingly being used in Solidity code audits due to their ability to efficiently analyze complex smart contracts for vulnerabilities, security risks, and inefficiencies. Advanced techniques, such as zero-shot and chain-of-thought prompting, allow these models to detect critical vulnerabilities with high accuracy, aligning with industry standards like OWASP’s top risks. 

This makes them a powerful tool for preventing exploits in Ethereum and other blockchain ecosystems.

What sets Solidity-specific LLMs apart is their fine-tuning on domain-specific data, which allows them to outperform general-purpose models in smart contract audits. By streamlining the auditing process, LLMs enhance developer productivity, reduce the risk of costly exploits, and increase trust among stakeholders, ensuring the creation of secure, optimized smart contracts for decentralized applications.

Why a Solidity Code Audit Is Critical for Smart Contracts?

A smart contract audit is a detailed security review of blockchain-based code, typically written in Solidity for Ethereum and EVM-compatible chains. The goal is to identify vulnerabilities, inefficiencies, and unintended behaviors before the contract goes live. Unlike traditional software audits, smart contract audits are especially critical because:

• Immutable Code: Once deployed, bugs cannot be easily fixed without expensive migrations.
• High-Value Targets: DeFi platforms, NFT marketplaces, and enterprise blockchains often handle millions in assets.
• Trustless Environment: With no intermediaries, any exploit is irreversible.

Smart contract audits combine automated scanning (static and dynamic analysis) and manual review by experts who analyze the contract’s logic, architecture, and economic incentives.

Key Security Risks

Audits are crucial for catching vulnerabilities like:

• Reentrancy Attacks: For example, the DAO hack, where $60 million was stolen due to recursive withdrawals. Audits check for unsafe external calls before state updates.
• Integer Over/Underflows: A bug where a poorly checked balance variable could wrap around to an unexpectedly large or small number. Using SafeMath or Solidity 0.8+ checks can prevent this.
• Front-Running: Miners or attackers can exploit pending transactions to prioritize their own trades. Audits can recommend commit-reveal schemes to mitigate such risks.
• Access Control Flaws: Missing modifiers like onlyOwner can lead to unauthorized upgrades, which audits can flag.
• Oracle Manipulation: Fake price feeds can be used to drain lending pools. Audits can detect reliance on untrusted sources and suggest safeguards.

How Do LLMs “Understand” Solidity Code?

Traditional tools, like Slither, use rule-based pattern matching, focusing on syntax issues. LLMs, on the other hand, go beyond simple pattern recognition. They interpret intent, trace data flow across functions, and understand business logic, allowing them to spot logic flaws that go unnoticed by traditional methods.

For example, LLMs can identify risks like a vested contract where a malicious actor might manipulate timestamps to game the system or a voting system with delegation loops that lead to infinite vote weight. LLMs can also flag misalignment in tokenomics, such as when a whitepaper promises certain returns that the code doesn’t actually support.

LLM Auditing vs. Traditional Tools

While traditional auditing tools excel at identifying known issues through static analysis and symbolic execution, LLMs take a different approach. They offer semantic reasoning, allowing them to understand why certain code exists and whether it aligns with the intended business logic. 

For example, LLMs help reduce false positives by incorporating context-aware detection, taking into account code comments or project-specific patterns. This leads to more accurate findings and fewer unnecessary alerts.

Why Businesses Use LLMs to Automate Solidity Audits?

Businesses use LLMs to automate Solidity audits because they can quickly scan and analyze multiple contracts, saving time and reducing costs. This allows for faster, more accurate security checks and helps prevent vulnerabilities before deployment.

1. Speed and Scalability

With LLMs, audits can scale significantly, allowing businesses to quickly review multiple contracts per month, which would be impossible with manual auditing. This is especially valuable for NFT marketplaces or DeFi protocols launching multiple collections or tokens simultaneously.

2. Cost Efficiency

Traditional audits can cost anywhere from $10K to $50K per contract. By incorporating LLMs for initial screening, businesses can reduce costs by up to 80%, making it easier for startups and smaller projects to access high-quality security assessments.

3. Proactive Security

LLMs provide real-time feedback as developers work, alerting them to potential vulnerabilities in their IDEs. This proactive approach helps prevent security issues before they even arise in deployed contracts.

4. Investor and Regulatory Confidence

Automated audits powered by LLMs offer transparency, generating detailed audit trails and reports that satisfy regulatory requirements, such as those from the SEC or GDPR, providing additional confidence for investors and stakeholders.

Benefits of Using LLMs in Solidity Code Audits for Enterprises

Using LLMs for Solidity code audits helps enterprises speed up the review process while ensuring better accuracy and security. It also reduces costs, boosts trust, and allows for scalable audits across multiple contracts, helping businesses stay competitive in the fast-paced blockchain space.

Technical Benefits

• Real-time auditing during development (IDE integration): LLMs can integrate directly into a developer’s IDE, offering real-time feedback and vulnerability alerts as code is written, making it easier to catch issues early in the development process.
• Intelligent remediation suggestions: LLMs not only identify issues but also provide context-specific recommendations for how to fix them, helping developers implement secure solutions quickly and efficiently.
• Faster code review cycles: With automated audits powered by LLMs, code review cycles are significantly shortened, allowing developers to identify and address potential vulnerabilities much faster.
• Integration with CI/CD pipelines: LLMs can seamlessly integrate into continuous integration and delivery (CI/CD) workflows, ensuring smart contracts are automatically tested and audited as part of the regular development process.

Business Advantages

• Lower cost per audit: Automating the audit process with LLMs reduces the need for manual audits, cutting down on costs and making high-quality audits more accessible for businesses of all sizes.
• Faster time to market for secure dApps: With quicker and more efficient auditing, businesses can launch secure dApps faster, gaining a competitive edge in the market.
• Scalable auditing for multiple contracts across platforms: LLMs enable businesses to scale their auditing processes, allowing them to review multiple contracts across different blockchain platforms simultaneously without compromising quality.
• Increased investor and user trust: Automated, accurate auditing enhances transparency and security, building trust among investors and users who rely on the integrity of the smart contracts powering their investments and interactions.

How to Automate Solidity Code Audits with LLMs?

We know how important it is to ensure the security of your smart contracts. That’s why we’ve developed an automated approach to Solidity code audits using advanced LLMs (Large Language Models). This process allows us to quickly and efficiently detect vulnerabilities, saving you time while ensuring your contracts are secure and reliable. Here’s how we handle it for our clients:

1. Define Security Requirements

We start by understanding your smart contract’s business logic and security needs. By getting a clear picture of the desired functionality and any compliance requirements, we can ensure the audit focuses on the right areas and aligns with your goals.

2. Choose or Fine-Tune an LLM

Next, we select the most suitable LLM, whether it’s a pre-trained model or one we fine-tune specifically for your contract. This fine-tuning helps the model better detect the types of vulnerabilities that are unique to Solidity code, ensuring a more precise and targeted audit.

3. Prepare and Preprocess Code

Before the audit begins, we clean up your codebase by removing any unnecessary dependencies that could complicate the process. We also annotate complex sections to ensure the LLM can parse and understand the code more effectively, which leads to more accurate results.

4. Design Effective Prompts

We use security-focused prompts or leverage LLM-powered frameworks that allow for deeper analysis. This enables multi-layered auditing, catching everything from basic syntax issues to more complex logic vulnerabilities, all while making sure we cover the entire contract’s structure.

5. Generate Audit Reports

Once the audit is complete, we automatically generate detailed reports that categorize vulnerabilities by their severity. These reports come with developer-friendly summaries and clear suggestions on how to fix each issue, making it easy for your team to act quickly.

6. Review and Validate

Even with LLMs doing most of the heavy lifting, we always have a senior auditor review and validate the results. During the code patching process, the LLM provides additional context and suggestions, ensuring that any fixes are aligned with best practices while maintaining oversight to avoid any errors.

Common Challenges in Using LLMs for Solidity Code Audits

Having worked with many clients, we’ve learned that integrating LLMs for Solidity code audits can come with some challenges. Below are the common issues we’ve encountered and how we’ve successfully tackled them to ensure a seamless, accurate, and secure audit process.

1. LLM Hallucinations and Inaccuracies

LLMs sometimes generate plausible but incorrect vulnerability reports due to overgeneralization from training data or a lack of Solidity-specific context in general-purpose models.

Solutions

• Curated Prompts & Model Constraints: We use domain-specific prompts to direct the model’s focus (e.g., “Analyze this function for reentrancy, considering Solidity 0.8+ safeguards”) and set confidence thresholds to ensure only high-certainty issues are flagged.
• Hybrid Verification with Traditional Tools: We run LLM findings through traditional tools like Slither or Mythril to cross-validate results, ensuring that potential issues, such as overflows, are accurately flagged and verified.

2. Data Privacy & Proprietary Code Concerns

Enterprises are often hesitant to expose sensitive smart contract logic to third-party LLM APIs, risking the leak of proprietary code.

Solutions

• On-Premise LLM Deployment: We recommend self-hosting models such as Llama 3 or Solidity-Code-LLM within your infrastructure, so sensitive code never leaves your servers. For example, a bank auditing private blockchain contracts can do so securely without external data leaks.
• Federated Learning & Secure APIs: We also employ federated learning, where models are trained on decentralized data, preventing the sharing of raw code, and use encrypted API calls to ensure security when using external services like Azure OpenAI.

3. Outdated or Incomplete Training Data

LLMs trained on older datasets may miss newer Solidity features (e.g., Solidity 0.9+) and emerging attack vectors like ERC-777 reentrancy.

Solutions

• Continuous Fine-Tuning: We regularly update models with the latest exploit reports (e.g., Immunefi disclosures) and Solidity documentation, keeping the models aligned with new threats and features.
• Open-Source Audit Repositories: We augment training with public databases like Solidity-by-Example and the DeFi Threat Matrix to help models understand common pitfalls and new attack patterns.

4. Integration Complexity

Enterprises often struggle to integrate LLMs into existing CI/CD pipelines and sync findings with project management tools like Jira or GitHub.

Solutions

• Custom Workflow Automation: We connect LLM audits to platforms like GitHub Actions for automatic scanning of pull requests and set up Slack alerts to notify developers of critical issues in real-time.
• Unified Reporting: We generate comprehensive PDF compliance reports and create Jira tickets to track issues and their fixes efficiently.
• Enterprise-Grade Scalability: Our solution allows the deployment of distributed LLM clusters, enabling businesses to audit thousands of contracts at scale.

Essential Tools & Frameworks for LLM-Powered Solidity Audits

When it comes to LLM-powered Solidity audits, specialized tools like ChainGPT and Solidity Sentinel help pinpoint vulnerabilities specific to Solidity code, offering precise, context-aware analysis. With frameworks like LLM-SmartAudit and GPTLENS, you get multi-layered audits that reduce false positives and ensure comprehensive security for your smart contracts.

1. Specialized LLMs for Solidity

2. Multi-Agent Audit Frameworks

LLM-SmartAudit

This framework uses a multi-agent approach with three agents:

• Auditor Agent: Scans the smart contract for potential vulnerabilities, identifying issues like reentrancy or access control flaws.
• Critic Agent: Validates the findings from the Auditor Agent, minimizing false positives by confirming real vulnerabilities.
• Reporter Agent: Creates comprehensive executive summaries of the audit, making it easier for developers and stakeholders to understand and act on the findings.

GPTLENS Framework

The two-stage process involves the Auditor LLM identifying potential vulnerabilities, while the Critic LLM cross-references findings with historical exploit patterns and economic attack simulations to ensure accuracy.

Two-Stage Process:

• Auditor LLM: Conducts an initial scan to identify potential vulnerabilities.
• Critic LLM: Cross-references findings with historical exploit patterns and simulations of economic attacks to ensure no vulnerability is overlooked.

Benchmark: Using this dual-Layered LLM approach has been shown to reduce false positives by 62% compared to single-agent auditing systems, ensuring greater precision in detecting risks.

3. Developer Tool Integrations

Cursor IDE

Cursor IDE provides real-time LLM-powered code analysis during development, offering auto-suggestions for security improvements and vulnerability explanations with clickable references, helping developers write secure code more efficiently.

GitHub Copilot + Security Plugin

This flags insecure patterns as you code, suggests safer alternatives like using SafeERC20, and integrates seamlessly with GitHub Advanced Security to enhance your development workflow and ensure secure coding practices.

Custom CI/CD + LLM Modules

Use Case: Securing DeFi Lending Protocol with LLM Automation

One of our clients, a fast-growing DeFi lending protocol, approached us with a pressing challenge. As they scaled quickly, deploying new liquidity pools weekly, they found that their manual audits couldn’t keep up with the rapid pace of development. The two-week delays were bottlenecking their launches, and institutional investors demanded proof of security along with audit trails for compliance.

While traditional tools like Slither could catch basic vulnerabilities, subtle economic and logic risks were slipping through unnoticed.

Our LLM-Powered Solution

We developed an LLM-powered solution to automate smart contract audits, ensuring real-time vulnerability detection and faster, more accurate reviews. This streamlined the development pipeline, reduced audit times, and increased security, helping the DeFi protocol scale rapidly while maintaining investor trust and compliance.

A. Automated Early-Stage Audits

We deployed ChainGPT Solidity-Code-LLM to scan every Git commit for vulnerabilities, including:

• Reentrancy risks in flash loan logic
• Oracle manipulation vectors
• Tokenomics inconsistencies (e.g., interest rate miscalculations)

Custom Slack Alerts: Critical issues were flagged in real-time, instantly notifying developers for rapid resolution.

B. Seamless Dev Pipeline Integration

We integrated automated feedback directly into the development workflow, with GitHub PR Comments offering detailed, line-by-line vulnerability explanations. Additionally, Jira Tickets were automatically created for high-severity issues, ensuring that the dev team could quickly address and resolve critical security risks.

C. Hybrid Verification

LLMs flagged potential issues, which were then validated using traditional tools for accuracy. For example, the LLM identified that calculateInterest() might round down unfairly, and Slither confirmed it as an integer truncation exploit, ensuring reliable results through hybrid verification.

D. Continuous Monitoring by IdeaUsher

• Weekly Model Updates: We retrained the LLMs on emerging exploit patterns to keep pace with evolving threats.
• False Positive Tuning: Over three months, we reduced false positives by 40%, ensuring more accurate results.

Tangible Outcomes

Key Wins

• Discovered a Hidden Voting Flaw: LLMs identified a governance attack where users could manipulate delegation timing to double-count votes—an issue that was missed by manual review.
• Saved $2M+ in Potential Exploits: We prevented a liquidation logic bug that could have allowed attackers to drain liquidity pools during market volatility.
• Compliance Ready: The automated audit reports met institutional due diligence requirements, including those necessary for a Coinbase listing.

Conclusion

Traditional tools can’t keep up with the speed and complexity of today’s platforms. LLMs provide faster, more accurate audits with semantic understanding and real-time feedback, making them a game-changer for businesses. At Idea Usher, we help implement scalable, adaptable LLM audit systems that integrate smoothly into your development process, ensuring robust security as your platform grows.

Looking to Automate Solidity Code Audits Using LLMs?

At IdeaUsher, we enhance the security of your smart contracts with LLM-powered audits that are designed to be:

• 10x faster than traditional manual reviews, ensuring quick turnarounds and faster deployment.
• Sharper than standard tools, catching complex logic flaws and vulnerabilities that others may miss.
• Seamlessly integrated into your development workflow, allowing for smooth, efficient audits without interrupting your team’s productivity.

Why Us?

• 500,000+ hours of coding expertise – Our ex-FAANG/MAANG engineers build robust, battle-tested solutions.
• Proven in DeFi – Secured $50M+ in TVL for clients, ensuring safe and reliable smart contracts.
• Scalable audits – From individual contracts to large-scale enterprise protocols, we’ve got you covered.

Check out our latest LLM audit projects to see how we’re driving the future of smart contract security.

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