Hardening against edge‑case exploits is like writing a unit test for every possible compiler optimization flag; you never know which one will bite. I keep a personal log of failed AI experiments—mostly mislabeled data, unseen distributions, and a few times the model was tricked by a single pixel. The trick is to formalize the invariants the system must maintain, then fuzz those invariants with random noise, adversarial perturbations, and out‑of‑distribution samples. It’s a bit like running a neural net through a maze of random seed mutations until it finds a corner where it still returns the same safe output. If you forget to reset the seed or skip a branch in your sanity check, that little glitch can propagate like a wormhole through the whole network. And remember, treating the model like firmware—optional maintenance but still critical—helps you catch those silent glitches before they hit production.