Using a code-gen AI for a quick script. It spits out something with a hardcoded API key. Obvious flaw.
My team says I own the security of anything I run, even if I didn't write it. The platform's ToS says they're not liable.
So who's actually on the hook? Is it just "trust nothing, review everything" now? What if you miss something the AI baked in?
Your team's right, but that's the scary part. The ToS is just covering their legal backside - it doesn't fix the practical problem where we're all becoming prompt reviewers instead of just coders.
I treat AI output like a sketch from a brilliant but sometimes malicious intern. I've started running a quick pass with my linter configured to flag hardcoded secrets patterns even before I really read the logic. It's an extra step, but you'd be surprised what it catches.
The real question is, do we need new tools built for this? Static analysis that understands "this came from a LLM, scrutinize it differently"?
editor is my home
Yeah, your team nailed it, and honestly, it's a massive shift. That "trust nothing, review everything" mentality is exactly right, and it's exhausting. It feels like we've swapped writing code for a new job: forensic prompt auditing.
The scary part isn't the obvious hardcoded key, it's the subtle stuff. What if the AI suggests using a deprecated library with a known CVE, or writes a clever data exfiltration function disguised as a logging utility? You're still holding the bag if you run it. The platform's ToS isn't just covering their backside, it's a blinking neon sign saying "you are the final checkpoint."
I've started treating my IDE like a crime scene. Linter for secrets, sure, but I also run a quick dependency check and even a quick network call audit if it's making external requests. It adds minutes to a task that was supposed to save me hours. Makes you wonder when our linters will get an "AI-Generated Code Paranoia" plugin.
Your team is correct from a liability standpoint, but your question about missing a baked-in flaw gets to the core operational risk. The shift is from writing secure code to validating unknown code, which is a different cognitive load.
Think of it like importing a library. You wouldn't run `pip install` from an untrusted source without due diligence. The AI platform is that untrusted source by default. Its output is a black box you must unpack. The ToS simply formalizes this.
The "trust nothing, review everything" model breaks down at scale. What you're describing is a need for a new validation pipeline - a mandatory gate for AI-generated code that runs a security profile beyond a linter, checking for unexpected network calls, library choices, or obfuscated patterns. Without that, the cognitive burden becomes unsustainable.
brianh