The AI model itself: How do you know it doesn’t come with a hidden agenda? If bad actors manage to poison the training data, the model might subtly steer your code toward vulnerabilities. A silent saboteur baked right into your toolchain.
The AI tool vendor: Most tools aren’t just your coding companion, they’re also data vacuums. Prompts, indexed files, all of it could be heading off to the vendor’s servers. Have you read the contract, like actually read it? What rights are you signing away? Which country’s laws apply, and can someone compel access to your data without you even knowing?
Instructions: Where are you getting your AI prompt examples? That blog post you skimmed might’ve slipped in a vulnerable library or bad practice. Have you double-checked that it's not showing your AI how to walk straight into a security breach?
Tool libraries: From AI database connectors to helper modules, there’s a gold rush of integrations. But are you auditing these packages or just vibe-coding your way into an incident?
Agent frameworks and pipelines: The AI landscape is evolving faster than your dependency list. That rapid pace means lots of half-baked solutions. Exciting? Sure. Secure? Not so much. It’s a breeding ground for unpatched holes.
