Who’s Actually Checking AI’s Code Before It Breaks Your Build?

Emily Davis · 2026-08-13

AI coding assistants are everywhere now. They promise speed, fewer boilerplate headaches, and a kind of tireless pair programmer who never gets cranky. But here's the thing nobody wants to talk about over coffee: those same tools can pull in open source packages that have never been vetted by a human. And they can do it in seconds. Traditional security reviews? They take days. Maybe weeks. By the time your team finishes a manual review of one dependency, the AI has already suggested three more. That's the scale problem in a nutshell. It's not that AI is malicious. It's that it's fast, confident, and completely unaware of your organization's risk tolerance. ### The Hallucination Problem Nobody Mentions Let's talk about hallucinations. Not the trippy kind, but the ones where an AI suggests a package that doesn't even exist. It sounds absurd, but it happens more than you'd think. The model generates a name that looks plausible, includes a version number that seems current, and your build pipeline happily tries to fetch it. If a malicious actor has already registered that name on a public registry, you've just handed them a backdoor into your environment. That's not a hypothetical scenario. It's a real attack vector that security researchers have been warning about for a while now. And it's getting worse as more developers rely on autocomplete-style suggestions for their dependencies. ### Why Traditional Reviews Fall Short Here's where the old guard breaks down. Most organizations have a process for reviewing new packages. Maybe it's a security team. Maybe it's a checklist. Maybe it's just a senior dev eyeballing the README. Whatever it is, it's linear. It was designed for a world where humans wrote every line of code and added dependencies deliberately. AI doesn't work that way. It generates code in chunks, often pulling from patterns it learned during training. That means it can introduce a dependency you've never seen before, with no warning, in the middle of a refactor. By the time your review process catches it, the code has already been committed, built, and possibly deployed. The core issue isn't that AI is sloppy. It's that the review process simply can't keep up with the volume. You'd need an army of security engineers to manually inspect every suggestion, and even then, you'd miss things. ### The Fix: Govern at the Point of Selection ActiveState makes a compelling argument here, and honestly, it's hard to disagree. Instead of trying to review everything after the fact, you need to govern packages at the exact moment they're selected. That means building guardrails into the development environment itself, not just at the CI/CD stage. Think of it like airport security. You don't screen passengers after they've boarded the plane. You screen them at the gate. The same logic applies to dependencies. If a package doesn't meet your organization's criteria, it should never make it into the pipeline in the first place. - Check for known vulnerabilities before allowing the dependency - Verify the package exists and has a legitimate maintainer - Enforce license compliance automatically - Block packages that haven't been reviewed by a human - Log every AI-suggested dependency for later audit ### Practical Steps You Can Take Today You don't need to rip out your entire workflow to start addressing this. Here are a few things that actually work: First, talk to your team about the risk. Most developers aren't even aware that AI suggestions can be malicious. A simple conversation can change behavior overnight. Second, set up automated scanning that runs the moment a new dependency is added. Tools like Snyk or Dependabot can catch obvious issues, but you should also add custom rules for AI-generated code. Third, create a shortlist of approved packages. If the AI suggests something outside that list, the build fails. That's a blunt instrument, but it's effective. Finally, keep humans in the loop for anything that touches sensitive data or production systems. AI is great for scaffolding and boilerplate. It's not great at understanding your compliance obligations. ### The Bottom Line The challenge isn't going away. AI coding tools are only going to get more integrated into daily workflows. The question is whether your organization will be proactive or reactive. If you wait for a breach to happen, you'll be cleaning up a mess that could have been prevented with a few simple guardrails. Governing at the point of selection isn't just a nice-to-have. It's becoming the only realistic way to keep pace with AI-generated code. The tools are getting smarter, but so are the attackers. It's time to close the gap.