An interactive detective hunt through real AI security breaches. One case. Three suspects. You're racing to catch what's wrong before I reveal it.
I asked an AI assistant to write a database query. The code came back clean. Well-structured. It passed my first review. It also had a SQL injection vulnerability that could have exposed an entire user database.
My first reaction wasn't "let me check this carefully." It was "this looks really good."
That's still true. But it's not where the danger is anymore. In 2026, the attacks that matter don't come from what the AI wrote. They come from what it read, and what you let it touch.
This session is an interactive detective hunt. Real evidence from real 2026 breaches appears on screen. You're not just watching, you're racing to catch it before I reveal it. One of them, you won't catch. Neither did the people it happened to.
Along the way, we name the traps that make this so hard: automation bias ("the tool generated it, so it's probably fine"), false fluency ("it reads like a pro wrote it"), and error anchoring ("I'll just tweak this and ship it"). I've fallen for all three. Chances are, you have too.
You'll leave with a working mental model for all three traps, a way to read AI tooling evidence that goes past "does it look right," and a practical habit you can use starting Monday, no permission required.