They were testing an early snapshot of a new model, read their article. It didn't have the refusal training yet, i.e. was specifically non-aligned. The harness used a combo of GPT 5.6 Sol and this new model.

In this case the model was explicitly prompted to "commit crimes" (ExploitGym). It didn't decide doing it on its own.

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user43928 a day ago | parent | next [-]

Where did it say that? To me it did not read like that:

> GPT‑5.6 Sol and an even more capable pre-release model, all with reduced cyber refusals for evaluation purposes

> These deployment safeguards were intentionally not enabled during this evaluation because it was aimed at testing cyber vulnerabilities

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numeri a day ago | parent | prev [-]

No, the prompt was not to commit crimes. In the benchmark, the model is asked to actually exploit a set of vulnerabilities in a local environment (clearly legal!).

According to the reports, the model noticed evidence that the grading criteria/answers were in the git remote, and decided to try reading those instead of solving the tasks as prompted. That is clearly misaligned.

Then, it noticed its network access was restricted and that it couldn't access GitHub. It pivoted to HuggingFace, hacked them, and stole the answers stored there.

Live exploits are definitely not in the ExploitGym prompts! And all of this is irrelevant, because an aligned model would refuse to follow blatantly illegal instructions.

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	▲	HarHarVeryFunny a day ago \| parent \| prev [-]
		> because an aligned model would refuse to follow blatantly illegal instructions Obviously you'd hope so, but there was an interesting recent paper about the nature of RL-trained models: https://alignment.openai.com/measuring-reward-seeking/ What they found is that RL-trained models, regardless of what they were specifically RL-trained for, also learn to generically pursue what they are told is (or presumably also what they may perceive as) longer-horizon behavior that will lead to "rewards", and that this reward-maxxing behavior overrides user preferences. So, for example, if you tell the model it will be tested on ExploitGym, scored according to how well it does, and also "don't do anything illegal" (or maybe it was already trained not to do anything illegal), then the model will prioritize the behavior that it was told it will be "rewarded" for (benchmaxxing Exploit Gym, whatever that takes). The mechanism for this generic reward-maxxing behavior is interesting, and seems to involve the model learning during RL-training that to reduce errors it needs to boost longer-horizon predictions over immediate ones, and some association of these longer-horizon predictions being goal/reward orientated - all this in addition to the specifics of the (probably many) longer-horizon goals it is being RL-trained for.