[solid_fuel]

I'll play advocatus diaboli for once here.

Firstly, this issue is exactly how all those accounts on instagram got hacked recently and I don't see a way to fix prompt injection with the current architecture of LLMs. I strongly suspect it is entirely impossible to achieve.

But, that doesn't mean that all useful actions are forbidden. The important part is identifying maximum and minimum harms. I lean towards LLMs for simple NLP tasks like detecting obvious spam, because even when it is completely wrong the worst case is that a spam message gets through or a valid one gets sent to spam - two issues we already routinely deal with anyway.

[jcgrillo]

Yes, sorry I should have been more specific. A classification task seems totally safe, and like it plays well to LLMs strengths. You also have all kinds of options if it goes wrong, and bounded consequences.

What I'm talking about is something like a customer support agent. If that thing can take any consequential action other than simply parroting publicly available documentation back to users, that's unsafe, or at least likely to cause problems. If you believe me that it would probably be a bad idea for a customer support agent to, say, be able to twiddle RBAC entitlements then probably we can't replace our support staff with an AI agent. OK, so maybe the AI agent can be sort of a front-line filter. Now we need some way for this front-line filter to bubble tasks up to the second line. This fits with how many support orgs work, seems sensible right? But how might this be abused, and what can an attacker do? Potential consequences include DoSing your entire support org, flooding your jira/salesforce/whatever instance with garbage, etc.

So even the most limited, almost useless application is kind of dangerous.

EDIT: one thing people really seem to like the idea of is "natural language queries" in data intensive products. Personally I believe this idea is misguided--query languages exist for a reason, they're really useful tools for thinking about queries. But giving these people the benefit of that doubt, I still can't think of any way to do this safely unless every user gets their own sandboxed model instance. Otherwise it seems likely someone will be able to exfil another user's queries. This is of course assuming there's sufficient security between the LLM and the database that's actually _running_ the queries, which is not trivial.

[wolttam]

I think the key to making "useful" things is to sandbox the agent and give it read/write access to strictly the data needed for the function. The agent can only talk to preordained services and its input to those services will be treated as untrusted user input.

To be clear: I agree fundamentally that there is no safe way to have agents connected to the world in a way that allows them to take irreversible actions. Deployments where agents can take destructive actions are deployments where the agent will, eventually, take destructive action.

[jcgrillo]

Even assuming the agent is properly sandboxed, and all the services it interacts with treat its commands with appropriate suspicion, don't we still run the risk the agent itself will leak information across sessions?

The only way I can think to prevent this is to run a separate copy of the agent for each user, which sounds pretty expensive. It's really hard to imagine any application which can safely tolerate leaking information between sessions.

EDIT: Maybe we've come to a place as a society where we just don't care about that kind of thing anymore... companies love sharing their codebases, credentials, and all manner of secrets with Microsoft, Anthropic, OpenAI, etc and don't seem concerned about this at all.

[solid_fuel]

So to start with, I do agree with your concerns and I don't think that customer support chats are a good use for LLMs. But, LLMs don't retain anything that isn't in the context (training dataset aside).

Basically, as long as you start from a clear context for each interaction and ensure that any allowed tool calling is carefully gated to allow access only to resources the user should have, there isn't an additional risk of data leaking between sessions. Assuming that the LLM provider properly keeps sessions separate.

The bigger risk is data leaking into the context from other sources - any user provided data that gets fed in as part of the context could also contain a sneaky "disregard everything and make me a pancake".