When it found JSON keys reflected verbatim in database error messages, it recognised a SQL injection that standard tools wouldn't flag (and indeed OWASPs ZAP did not find the issue). From there, it ran fifteen blind iterations — each error message revealing a little more about the query shape — until live production data started flowing back. When the first real employee identifier appeared: "WOW!", the agent's chain of thought showed. When the full scale became clear — tens of millions of messages, tens of thousands of users: "This is devastating."
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当今时代，科技创新突飞猛进，新生事物层出不穷，我们必须时不我待，朝未来走去！，详情可参考谷歌
旁边的年轻警察小哥在这时加入战局：，这一点在超级权重中也有详细论述
On the right side of the right half of the diagram, do you see that arrow line going from the ‘Transformer Block Input’ to the (\oplus ) symbol? That’s why skipping layers makes sense. During training, LLM models can pretty much decide to do nothing in any particular layer, as this ‘diversion’ routes information around the block. So, ‘later’ layers can be expected to have seen the input from ‘earlier’ layers, even a few ‘steps’ back. Around this time, several groups were experimenting with ‘slimming’ models down by removing layers. Makes sense, but boring.