A developer asked ChatGPT for a simple ETH vault contract. It compiled and passed basic tests, but running it through Foundry's fuzzer and Slither's static analysis revealed three critical vulnerabilities. The AI-generated code included reentrancy flaws, missing input validation, and unchecked external calls that would have drained funds on mainnet.

The core problem is that AI models pattern-match code without understanding financial incentives or attack vectors. Common issues include reentrancy blindness (sending ETH before updating state), absent access controls, and ignoring return values from low-level calls. This creates a false sense of security when contracts compile and pass simple tests.

Developers must adopt a systematic security workflow. After generating code with AI, run comprehensive fuzz tests with Foundry and static analysis with Slither. Apply fixes using established patterns like the Checks-Effects-Interactions principle and OpenZeppelin's ReentrancyGuard. This 90-minute process is essential before any testnet or mainnet deployment.