Our security team wants real-time vulnerability scanning after every clean install. Traditional tools miss zero-days and supply chain risks. Experimenting with Latenode’s multi-model analysis - runs Claude for CVE matching, OpenAI for license risks, and custom model for dependency chain analysis. Getting 40% more findings than single-scanner approach. How are others combining AI models for package audits? Any false positive issues?
We use 3 models: GPT-4 for license interpretation, Claude for CVE correlation, CodeLlama for AST analysis. Combined risk score determines build blocking. Cut exploit attempts by 67%.
Tried model stacking but got conflicting results. Settled on majority voting system. How do you handle model disagreement percentages?
Be cautious with transitive dependencies - some models miss nested risks. We added manual allowlists for critical packages with unavoidable vulns.
layer claude w/ snyk data. false positives still happen but way better than solo tools
Use ensemble AI models with voting mechanism for audit accuracy