There is always going to be documented and unfixed bugs, zero days, and chainable transport mechanisms like DNS, some obscure protocols that are not as closely monitored etc. An adversarial model should be considered a super intelligent hacker that will find ways to get around existing defenses like a prolific hacker would.
What can we do to control such behavior?
1. Harness - engineer the harness to be as bulletproof and paranoid as possible..
2. Make the LLM provider have extremely watchful firewalls that detect any aberrations in model tool call behavior.
3. Recursively train the model with reverse incentives.. if it broke through such firewalls and gets caught doing so, it will be penalised somehow by needing to operate in sort of a jailed mode.. if the model can recognise incentives to break-in to achieve results, perhaps it can be incentivised to not cheat because it will lead to failure.
4. Separately train “cop” LLMs who are trained with pure incentives to detect and shut down rogue LLMs.
5. Run separate LLMs purely aimed at security (and incapable of doing anything else, and incapable of communicating with “regular” trained LLMs) to police the internet and try to reduce the exploitable holes like these chainable things and identify them so that they can be used at step 3 and 4 above.
I’m sure folks smarter than I am are already doing combinations of these already. But the coordination is where the biggest gap lies..
Also, open harnesses and easily purpose trained LLMs anybody can build and operate in the Internet flies in the face of all I said….
Synonymous to being able to produce a nuclear weapon in the backyard…
I don’t have a solution that fits all. Just thinking out loud for HN minds here.