AI models often hallucinate or make costly mistakes when tasked with recommending software versions, upgrades, and security fixes, creating technical debt.

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Research from Sonatype analyzing 258,000 AI-generated dependency recommendations across Maven Central, npm, PyPI, and NuGet reveals that frontier AI models from OpenAI, Anthropic, and Google frequently hallucinate or produce faulty software upgrade guidance. Nearly 28% of GPT-5's recommendations were hallucinations, and even improved reasoning models still invented roughly 1 in 16 recommendations. More cautiously behaving models that recommended 'no change' left 800–900 critical/high-severity vulnerabilities unaddressed, while others actively introduced known vulnerabilities. Sonatype found that 'grounding' models with real-time dependency intelligence, vulnerability data, and compatibility context reduced critical and high risks by nearly 70%, arguing that ecosystem intelligence — not model scale — is the missing ingredient for safe AI-assisted dependency decisions.

AI-Powered Dependency Decisions Introduce, Ignore Security Bugs

Adding Dependency Intelligence & Context to AI