Your AI Copilot Might Be Poisoned: RAG Attacks and Why Static Analysis Still Wins

This week, a Hacker News post about document poisoning in RAG systems caught my attention. And over on Zenn (Japanese dev community), someone found malware disguised as a "useful tool" on GitHub . These aren't isolated incidents. They're symptoms of the same problem: the code your AI writes is only as trustworthy as its training data and context . I've been building a security scanner specifically for AI-generated code for the past two weeks. Here's what I've learned about why this matters — and what actually works to catch the problems. The Attack Surface Nobody Talks About When you use an AI coding assistant, you're trusting: The model's training data — was any of it poisoned? The RAG context — are your docs, READMEs, and examples clean? The packages it suggests — are they typosquatted? The patterns it follows — are they secure by default? The RAG poisoning paper shows how attackers can inject malicious content into the documents that AI systems use as context. Imagine someone submit