How AI coding tools can learn to develop secure software

This article examines how large language models (LLMs) frequently produce insecure or incorrect code—citing BaxBench findings—and explains why AI coding tools propagate vulnerabilities. Experts recommend mitigation strategies including structured self-audit feedback loops, contextualized prompts, integrating LLMs with SAST/DAST/SCA and CodeQL, multi-LLM review, CI/CD guardrails, deterministic AI for reliability, advanced binary analysis and reproducible builds, and ML-BOMs for AI supply chain assurance, emphasizing secure-by-default practices and developer enablement.