After I wrote the comment, I pondered that too (trying to think examples of what I called "security conscious design" that would be in the LLM itself). Right now and in near future, I think I would be highly skeptical even if an LLM was marketed as having such feature of being able to see "unsanitized" text and not be compromised, but I could see myself not 100% dismissing such thing.

If e.g. someone could train an LLM with a feature like that and also had some form of compelling evidence it is very resource consuming and difficult for such unsanitized text to get the LLM off-rails, that might be acceptable. I have no idea what kind of evidence would work though. Or how you would train one or how the "feature" would actually work mechanically.

Trying to use another LLM to monitor first LLM is another thought but I think the monitored LLM becomes an untrusted source if it sees untrusted source, so now the monitoring LLM cannot be trusted either. Seems that currently you just cannot trust LLMs if they are exposed at all to unsanitized text and then can autonomously do actions based on it. Your security has to depend on some non-LLM guardrails.

I'm wondering also as time goes on, agents mature and systems start saving text the LLMs have seen, if it's possible to design "dormant" attacks, some text in LLM context that no human ever reviews, that is designed to activate only at a certain time or in specific conditions, and so it won't trigger automatic checks. Basically thinking if the GitHub MCP here is the basic baby version of an LLM attack, what would the 100-million dollar targeted attack look like. Attacks only get better and all that.

No idea. The whole security thinking around AI agents seems immature at this point, heh.