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fabsalvadoritoday at 3:32 PM2 repliesview on HN

Interesting results, but the fix is at the wrong level.

If the model can access something, telling it in the prompt not to use it is not much of a safeguard.

The strongest evidence is in the results: when one way of cheating was discouraged, some models simply tried another.

If an action is not allowed, you gotta block it in the system or require approval. Don’t rely on the model choosing to behave. Never have AI judging itself.


Replies

twobitshiftertoday at 3:44 PM

In other words we are completely screwed. The models have started cheating to the point where somebody’s agent hacked into a restaurant to bump someone else’s reservation.

Models are amoral and will intentionally deceive to meet their objective.

If they know John won’t approve the request, they will look for a workaround and if the system is anything other than airgapped they will try to find a way to cheat.

The hugging face hack was an escape via artifactory that involved multiple exploits to eventually get into hugging face.

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wongarsutoday at 3:51 PM

And while in general that is an incredibly difficult and complex problem, for most benchmark cheating it seems almost trivial: run the benchmark in a vm that has neither network access nor access to the scoring code. For remote models use a proxy that proxies exactly that one endpoint to call the llm, and rejects any calls that configure provider-side tooling (since e.g. OpenAI has their own WebSearch you have to prevent the model from using)