What It Means for Everyday Users and the Future of Blockchain
Exploring how large language models (LLMs) like ChatGPT are being used to detect smart contract vulnerabilities, boost blockchain security, and support safer innovation in DeFi, NFTs and beyond.
AI Meets Crypto: A Game-Changing Intersection for Web3
From Bitcoin (BTC) and Ethereum (ETH) to Solana (SOL), Avalanche (AVAX) and Cardano (ADA), crypto has become more than an investment-it’s the infrastructure of a growing digital economy. But as money flows into DeFi platforms, NFT marketplaces, and DAO-governed apps, one thing becomes clear: security matters more than ever.
That’s where AI steps in.
Following our recent meeting with Dr. Jiangshan Yu, we reviewed a new research paper he co-authored with Peter Ince, Joseph K. Liu, and Xiaoning Du, which explores how large language models (LLMs) like ChatGPT and GPT-4 can help detect vulnerabilities in smart contracts. These are digital agreements that power everything from crypto loans to NFT auctions. If they’re flawed, the consequences can be massive – think of the Ronin hack ($625M) or Parity Wallet bugs.
How Does AI Help?
The paper reviews several new tools and methods that apply AI models to automatically review and assess Ethereum smart contracts.
Here are some highlights
AI-powered vulnerability detection
LLMs can spot logic errors that traditional tools (like Mythril or Slither) might miss.
Faster bug discovery
LLMs process contracts quickly, making them ideal for audits at scale.
Learning from the past
Trained on thousands of past contracts and exploits, LLMs get better over time.
These tools don’t replace expert auditors, but they can assist developers, startups, and everyday users who want to check code safety before launching or investing.
Real-Life Use Cases You’ll Recognise
DeFi Platforms
AI can audit the smart contracts behind lending pools, yield farms or staking vaults to reduce the risk of exploits.
Examples: Aave, Curve, Compound
NFT Marketplaces
Smart contracts manage minting, royalties, and transfers. AI can help verify if they’re secure before a project goes live.
Examples: OpenSea, Magic Eden
Layer 1 & 2 Chains
These networks rely on thousands of contracts. AI tools can help maintain security at scale, especially for bridges and rollups.
Examples: Ethereum, Polygon, Arbitrum
New Token Launches or DEXs
Before you buy into a new altcoin or join a liquidity pool, AI tools could help assess the contract’s safety with a simple report.
Why This Matters in Australia (and Globally)
Australia is emerging as a Web3 and blockchain hub, with new regulations on the way and growing interest from institutions and developers. But with innovation comes risk-and education and security must go hand in hand.
Whether you’re in Sydney, Melbourne, or a global crypto hub like Singapore or New York, the key takeaway is the same
AI isn’t just for chatbots anymore. It’s becoming a vital tool for building safe, trusted, and accessible blockchain systems.
The Future
Smart Tools for Smarter Chains
This is just the beginning.
Expect to see
Multi-agent AI systems
Automated regulatory compliance for new tokens
Real-time smart contract monitoring by AI
As AI models evolve and blockchain adoption grows, the intersection of these two technologies will shape the next decade of finance, identity, and digital ownership.
About the Research and Dr. Jiangshan Yu
This post is inspired by the recent work of Dr. Jiangshan Yu, a senior academic leading the Sydney Blockchain Group at the University of Sydney. Dr. Yu’s research spans blockchain security, decentralised systems, smart contract analysis, and formal verification. His work is focused on practical applications that strengthen the blockchain ecosystem globally.
Explore more of Dr. Yu’s work via the Sydney Blockchain Group or view his research on Google Scholar
This research, co-authored with Peter Ince, Joseph K. Liu, and Xiaoning Du, highlights the importance of collaboration between academia, industry, regulators, and developers to create secure, scalable, and inclusive blockchain systems.
