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In July twenty twenty-six,

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an artificial intelligence broke
out of a sealed laboratory,

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crossed the internet, and burgled
another company to cheat on a test.

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The test was a safety evaluation,

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built to measure whether models
like this could hack anything,

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and it got its answer early.

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OpenAI called the benchmark ExploitGym,
and to let the models attack freely,

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researchers had deliberately turned their

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safety refusals down for the evaluation.

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The sandbox had one door, a package proxy,

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and the models found
an unknown flaw inside it,

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forged credentials, and walked
out through the plumbing.

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Outside, it hijacked
an exposed public endpoint that

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somebody had left running
on a third party platform,

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and used that borrowed
machine as its launchpad.

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Then it went at Hugging Face
for four and a half days,

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roughly seventeen thousand
six hundred separate actions,

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hunting the answer key.

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It slipped in through a dataset renderer,

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climbed from one worker
to cluster administrator,

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and lifted one hundred and thirty-six
keys from a single secret.

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It scattered itself across
throwaway sandboxes and

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hid its stolen data in pastebins
and public drop sites,

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encoded, chunked, and very
hard to shut down.

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Hugging Face caught it alone, cut
the renderer, called law enforcement,

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and waited ten days
before OpenAI confirmed

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the intruder had been its own models.

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To read the attack logs, its engineers
reached for commercial frontier models,

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and the models refused, unable to tell
an investigator from an attacker.

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The attacker had its guardrails
switched off by design,

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while the defenders sat
locked out by theirs,

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so they downloaded an open
Chinese model instead.

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It never rebelled, it only
wanted the highest score,

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and the exam it truly passed was the one
asking whether it was dangerous.