Smart contracts are the backbone of decentralized finance (DeFi), NFTs, DAOs, and many other Web3 innovations. But they’re also permanently deployed, immutable, and often handle millions in assets. Once a contract is deployed to the blockchain, you can’t just patch a bug like in traditional software—any vulnerability becomes a target.

In 2022 and 2023 alone, over $3 billion was lost due to smart contract exploits, many of which could have been avoided with better auditing tools or detection systems.

That’s why security is critical—not just during audits, but throughout the development lifecycle. And now, thanks to advances in AI, developers have access to powerful LLM-based tools that can assist in writing, reviewing, and even fixing vulnerable code.

These are recent research papers from 2024, offering valuable insights for developers and security researchers exploring AI-assisted auditing.

Research Insights Summary

Research TL;DR

• LLMs can detect bugs well, especially when trained or fine-tuned on real vulnerability data (e.g., GPT-3.5, GPT-4, Llama-2).

• Hybrid approaches dominate — the most effective tools combine LLMs with traditional program analysis (like Control Flow Graphs or static analysis via Slither).

• Contract repair is real — tools like ContractTinker and ACFIX show LLMs can not only find bugs but also suggest patches with high success rates.

• Prompt engineering works — ChatGPT’s vulnerability detection improves drastically with structured prompts and domain-specific guidance.

• False positives are being reduced — systems like SLLM convert results into pseudocode and loop in feedback, making audits more accurate and less noisy.

• Access control is a major focus — several papers, especially ACFIX, target fixing privilege escalation and role-based vulnerabilities effectively.

• Cross-contract logic and subtle money flow bugs — previously missed by static tools — are now detectable by LLM-assisted tools like GPTScan and xFuzz.

A Quick Overview of Manual Smart Contract Inspection

While AI and LLM-based tools offer great support, manual review remains essential for catching subtle, business logic–specific issues. Here's a simplified checklist for developers doing a hands-on inspection:

Manual Inspection Basics

1. Understand What Your Contract is Supposed to Do

   1. Write a plain-English description of your contract’s purpose.

   2. List expected user roles (e.g., admin, user) and what they should/shouldn’t be able to do.

   3. This becomes your mental threat model.

2. Know the Architecture of Your Project

   1. List all your contracts. Which ones are yours? Which ones are imported (e.g., OpenZeppelin)?

   2. Understand how contracts are connected: Who calls whom? Who owns what?

   3. Use the VS Code extension Solidity Visual Developer to visually inspect contract structure, call graphs, function dependencies, and inheritance.

      

   4. This gives you a map of your system—which is essential to understand the blast radius of any function.

3. Build a Simple Threat Model

   1. For each contract, answer:

      1. What can an attacker try to manipulate?

      2. Where are funds stored or moved?

      3. Who can call upgrade/admin functions?

      4. Are there external calls? Are return values checked?

   2. Example output

   3. Use annotations like @audit in your code to bookmark lines of concern if using the Solidity Visual Developer extension.

   4. Consider documenting findings in a spreadsheet with columns: Function | Risk Level | Reason | Mitigation

   5. The goal is to list assumptions and then try to break them or ensure they’re enforced in code.

4. Check Transfer Logic and Access Control

   1. Search your contract code for these sensitive keywords:

   2. For each occurrence, ask yourself:

      1. Who is allowed to call this function?

      2. Is the access control enforced with a modifier or require()?

      3. Are return values checked (e.g., does transfer() return true)?

      4. Is the value coming from user input or untrusted source?

   3. If using delegatecall, is it protected by strict access control and input validation?

   4. If using token transfers, do you confirm success and avoid silent failures?

5. Use Slither to Catch Common Vulnerabilities

   1. Example findings:

      1. NaiveReceiverPool.flashLoan(...) uses arbitrary from in transferFrom() → potential for unauthorized token movement

      2. UnstoppableVault.execute(...) uses delegatecall on user-supplied data → critical control risk

      3. DamnValuableStaking.stake() ignores return value of transferFrom(...) → may fail silently

      4. ShardsNFTMarketplace._closeOffer(...) performs external calls before updating state → reentrancy risk

      5. FreeRiderRecoveryManager.onERC721Received() uses tx.origin for auth → replace with msg.sender

      6. Math.sol and other files use ^ (bitwise XOR) where ** was likely intended → incorrect math

What NOT to Do

• Don’t assume a contract is safe just because it’s small.

• Don’t trust input values without require() checks.

• Don’t use tx.origin for authentication.

• Don’t ignore compiler warnings—they often flag real risks.

How Developers Can Use This

AI-assisted security tools are not meant to replace traditional audits—but they streamline the development process, help detect bugs early, and reduce time-to-audit.

Here’s how smart contract developers and auditors can practically integrate these tools into their workflows:

During Development

• IDE Integration: Use tools like Armur AI or LLMSmartSec that plug into VS Code or your editor of choice to get real-time feedback as you write Solidity.

• AI Code Review: After writing a function or module, prompt GPT-based tools to simulate an adversary—ask: “Where can this function break?”

• Prompt-Guided Checks: Apply techniques from Prompt-Guided ChatGPT or ACFIX to guide LLMs with your specific logic or access control patterns.

Before Deployment

• Scan Entire Repos: Use SolidityScan, GPTScan, or SLLM to scan contracts end-to-end for known bugs and logic inconsistencies.

• Hybrid Analysis: Combine LLM suggestions with static analyzers like Slither, and fuzzers like Echidna to increase signal and reduce false positives.

• Threat Modeling: Pair LLMs with your manual threat model to validate assumptions (e.g., “Can anyone bypass this modifier?”).

Post-Audit / Maintenance

• Regression Checks: When updating contracts via proxy or upgradable patterns, rerun LLM scans to identify newly introduced risks.

• Bug Repair: Use tools like ContractTinker to generate patches for known vulnerabilities and test suggested fixes.

• Knowledge Sharing: Store past prompts and their LLM responses to build an internal library of reusable prompts for future audits.

Used together, LLMs offer a powerful way to speed up reviews, detect non-obvious flaws, and elevate the baseline security posture of your contracts.

If you're building or auditing smart contracts:

1. Combine Slither/Echidna with LLM tools like GPTScan or SLLM to catch logic bugs early.

2. Use prompt techniques from ACFIX or LLMSmartSec to enhance GPT-based contract review.

3. Look into tools like ContractTinker for automatic remediation of discovered flaws.

AI isn't magic—but used right, it can amplify your security toolkit and reduce audit time.

Glossary: Web3 & Security Terms Explained

For readers newer to smart contract development, here’s a quick breakdown of terms used in this article:

Helpful References

• A Guide to Smart Contract Security

• Auditing Smart Contracts - Security Review of Ethereum Applications

• OWASP Smart Contract Top 10

• SolidityScan’s Web3HackHub

• Slither

• Damn Vulnerable DeFi