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I recently spoke with a researcher who was working on a statistical method for model deception detection. They trained Qwen to behave in a nefarious way when prompted with a specific phrase and the model then called a tool in a non-transparent way to the end user, reporting back a non-tool call response in a completely benign manner. This wasn't happening in the 'reasoning' stage, it was at the model weights stage and they were able to 'see' this behavior then before the model even started to parrot back its behavior in human language.

Apparently, based on whatever research or paper they were using as their foundation for this statistical model, they could spot the 'virus'. Perhaps it would be in a similar fashion?

I did ask them whether they thought that there might be advances to the point where these behaviors might be able to evade detection knowing the research was out there to 'see' it. They seemed surprised by that question and didn't have any answer.

by bee_rider3 hours ago|

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Did they (somehow, this seems extremely difficult) notice a pattern in the weights that indicated tampering, or just scan for the known manipulated weights? The former seems really useful and very difficult, while the latter seems less of both.

by garciasn1 hours ago|

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I didn't ask and they didn't directly say; but, from what I took away from the conversation it was direct observation of the weights.

by bigbadfeline4 hours ago|

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Something similar to abliteration would work, after all abliteration removes the alignment virus, why not the others? Fine-tuning can be used too, there are many options as long as the weights are available.

On the other hand, nothing can be done to fix closed models, nobody knows how they work, how they select the targets for their attacks or the nature of those attacks - even conventional alignment can introduce dangerous cognitive viruses.

The only way to be sure is to have open weights models with fully open abliteration and fine-tuning - both the process and data.

by kirth_gersen4 hours ago|

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more eyes would seem to raise the odds of spotting malicious tampering. or at least create the opportunity for people to look for malicious tampering. if the weights are closed then even looking is impossible...

by lp924 hours ago|

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Your logic is exactly what the Linux people were saying. But as we saw due to AI more and more bugs and exploits are being found, some dating back years of releases.

Open weights won't prevent a model from being poisoned because no human understands how the weights actually impact the model.

by bigbadfeline4 hours ago|

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> Your logic is exactly what the Linux people were saying.

There's nothing wrong with that logic.

> But as we saw due to AI more and more bugs and exploits are being found, some dating back years of releases.

Well, AI helps fixing bugs too and it can also write bug free code with minimal additional effort, so open source not only retains its advantages in the presence of AI, it multiplies them. After the initial (and artificial) hype and bug-fixing efforts things are going to settle down quite nicely.

Closed source, on the other hand, doesn't have the advantage of multiple eyes and minds finding and fixing bugs and the unavailability of the source isn't an advantage either - AI can decompile and understand binary too, so it can find bugs without access to the sources as well.

> Open weights won't prevent a model from being poisoned because no human understands how the weights actually impact the model.

You're using one-sided logic again. If "no human understands how the weights actually impact the model" then viruses would be impossible. Apparently humans understand enough to introduce viruses and that's enough to detect and remove them. It's similar to alignment and abliteration, as I explained above.

by prophesi4 hours ago|

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> But as we saw due to AI more and more bugs and exploits are being found, some dating back years of releases.

That's by design though. It's just that there's now an exponential increase in the amount of "eyes", in the form of AI agents, looking at the FOSS code to find and fix the bugs.