I Checked What Security Vulnerabilities AI Coding Tools Actually Introduce

Last month I started going through PRs and open-source repos, cataloging the security vulnerabilities that AI coding tools actually introduce. Not theoretical risks. Actual patterns showing up in production code, backed by security research. The numbers are bad. Veracode tested over 100 LLMs across Java, Python, C#, and JavaScript. 45% of generated code samples failed security tests. AI tools failed to defend against XSS in 86% of relevant samples. Apiiro found that AI-assisted developers produce 3-4x more code but generate 10x more security issues. Read that again. 10x. The patterns are predictable, though. Once you know what to look for, you start seeing them everywhere. 1. SQL injection still happening in 2026 Ask ChatGPT or Copilot for a database query endpoint and you'll get something like this: // VULNERABLE app . get ( ' /user ' , async ( req , res ) => { const userId = req . query . id ; const sql = `SELECT * FROM users WHERE id = ${ userId } ` ; connection . query ( sql , ( er