2026 developer competency in securing LLM applications

SecureFlag analyzed 420,383 hands-on lab attempts across the OWASP Top 10 for Large Language Model Applications, completed by intermediate-level practitioners who had already finished prior security training. The data looks at how developers who already understand classical vulnerabilities such as SQL injection and cross-site scripting handle the newer threat categories that come with building LLM-powered applications.

The report breaks down solve rates, hint usage, average attempts, and common failure patterns for each LLM risk category, and shows where classical security instincts transfer well and where they do not. It offers a data-driven look at categories ranging from prompt injection to excessive agency, with practical takeaways for teams building and securing AI-powered software. Developers can build these skills directly in SecureFlag's OWASP Top 10 for LLM Applications labs and Agentic Coding Labs.