If you only use chatbots and you’re trying to keep up with AI tools, this is the part that can make you waste money and time: the real bug in Incident CVE-2026-LGTM is not “bad code.” It’s treating “LGTM” like a safety stamp.
That sounds small, but lowkey it changes everything. You see a friendly green check, you relax, and your brain files it as “someone already looked.” The thing is, that’s exactly where trust can get faked.
One research team replayed 11 different attack styles inside real GitHub-style workflows and said the bigger weakness was how the system handled secrets and settings, not one model having a random bad day.[S001] Plot twist: the sign on the door said “all good,” while the keys were still hanging inside the lock.
Another paper found that when changes were framed as “no bug here,” the review got biased fast, and a repeated context-trick hit a 100% success rate in their tests until extra guardrails were added.[S002] So the drop isn’t from 100 to 0 because AI is evil. It’s from “looks reviewed” to “actually reviewed,” and those are not the same thing.
So if you’re a normal person trying to decide whether this story matters: yes, but only if AI is helping review or auto-approve things. Tested in paper setups around GitHub-style workflows, not every app on earth, so your setup may differ.
My takeaway: a security label should mean “someone checked,” not “a bot sounded confident.” Save or share this with the one friend who still trusts the green check a little too much. What would you trust more: a fast approval, or a slower review with proof?