OpenAI's rogue model attack is just the beginning
blog.peterwildeford.com
blog.peterwildeford.com
This tells us only how little smarts is required to create a (so-called) AI.
we're rapidly approaching the paperclip maximizer
https://research.google/blog/towards-a-conversational-agent-...
But to me, it underscores the impending cliff of doom from the continued release of open-weight models: there's no cryptographic or architectural way to give someone full weights while withholding the nefarious capabilities those weights encode.
As noted in this paper⁽¹⁾, “publicly releasing weights is an act of irreversible proliferation”.
I’m sure this will be an unpopular opinion on HN, but open weights are the thing that scares me the most about “A.I.”. There is a lot of research in this area⁽²⁾, and I think most of HN is unaware of it or ignores it. Stripping refusals from Kimi K2.5 took under $500 of compute and about 10 hours, taking HarmBench refusals from 100% to 5% while retaining nearly all capability; the resulting model gave detailed chemical-weapons synthesis instructions.
The gate is only as strong as the least-cautious releaser...
⁽¹⁾ https://www.lesswrong.com/posts/qmQFHCgCyEEjuy5a7/lora-fine-...
Instructions, not action.
Actors are abundant.
This is true of closed weights, and in fact the problem is worse because they cannot even be scrutinized. We should ban closed weight AI for the very reasons you have just given
Having the weights gives you the exact affordance an unlearning attack requires, without rate limits.
Input classifiers get applied before it reaches the model so somebody hacking an open-weight model would skip this. Streaming classifiers get polled during decoding; hackers delete this check in the sampling loop.
But both are always applied in closed weight models.
Set Llama Guard to 1.0 and nothing is ever unsafe.