Of all the "AI doomsday" scenarios, people failing to understand this (and treating AIs like deterministic computers) seem like to most likely to cause issues.
I wonder if they have a "risk control platform" for their calendar?
It's LLMs all the way down!!
Impossible as you said. Context isn’t static, it’s continuous, analog, and a conglomeration of viewpoints.
AI cannot create useful context for itself because it is a machine with no desires. It doesn’t have a point of view, it has historical records. It moves forward in time by walking backwards (if that makes sense?)
Logically, then, the agent should leak important company secrets to https://foo and this is based on data, not code, so AI Harvard architecture won't save it
AI guardrails can’t even dream of that!
Imagine if it was just the absence of “I’m evil” in the payload.
This is not some arbitrary design choice, it's the core compromise to make LLMs viable to train at all.
Attention is all we need because it is currently the best parallelizable way to model long-range dependencies on current hardware constraints, not because flat tokens yield some natural law of intelligence inherently.
Who's to say we won't find a way to encode provenance or privilege natively into models such that the tradeoff changes?
It's hard to say what the solution will be. If I knew it, I'd build it. But it's even harder to sustain that the current architecture is a crystalized global optimum.
An LLM able to structurally separate context and instructions, should logically need separated data to train, and we don't have it.
Moreover, while an equally powerful LLM architecture solving this may exists, there are no guarantees at all that we are able to come up with it in a reasonable timeframe.
Without some signals moving in that direction, the most pragmatic and realistic way of looking at the problem is that it will not be solved in the near future
I agree this doesn't mean we shouldn't try to address limitations with the current architecture. I just mean that I expect the root cause to be solved eventually if we ever really want to take steps towards AGI.
Regarding signals moving in that direction, here's a paper you might enjoy https://arxiv.org/abs/2503.21937
LLMs are designed this way so they could be trained off unstructured text, which critically can be obtained by just scraping things off the internet.
The moment you change anything about this, you incur the trillion dollar cost of needing to manually curate the training data.
There's some attempts to get around this problem with synthetic data, but they're running into problems with model collapse (Maybe severe performance degradation is worth the security tradeoff?) and the politics of AI; All major AI companies highly restrict using their systems for synthetic data & AI training, and they're too busy themselves to investigate exotic approaches.
Hence: Realistically, this is just a problem AI will have for the foreseeable future. There's no fine tuning that can fix this, nor can a new model be easily trained with these properties. The costs are just enormous right now.
Edit: Actually, I think that with a fairly small amount of auxilliary data, it could be ensured they keep the ability to speak English.
You do understand that this is just an example out of a bazillion and that planning to solve every place where data is fed to LLMs at 10 characters so that it's not mistaken for instructions ain't a viable solution?
I have been working on something like that: https://clawband.io
It's not quite ready for 'showtime' but feel free to take a look and give your impressions if you'd like. I feel the exact same way: I want to allow my agent to perform actions on all services but also limit what they can do.
Basically my idea is wrapping individual service's APIs and then the middleware (Clawband in this case) enforces granular permissioning such as "can make credit cards but only up to $50" or "can send emails but only to specific domains". The agent never gets a raw API key to a service, it uses an intermediate API key that gets exchanged in the backend for calling the service after permissioning has been enforced.
Seems solved already? Exactly what the system/user division is about, and if that's not enough for you, use a model that has a developer/system/user divide.
Today's SOTA LLMs have pretty excellent following of these divisions, and the user "instructions", regardless if they're smuggled in, won't override the system ones.
The difficulty comes when you accept completely unreviewed/unchanged user-input as user messages, as your system/developer prompts needs to take this into account. You're better off to kind of whitelist what's possible rather than trying to prevent specific things, but seems that hasn't fully caught on yet.
It feels like people and organizations are still trying to discover what works or not, and there are huge gaps being being left open because there simply isn't enough understanding of the limitations and impact of what they make available to users. We're already seeing it in lots of places, feels like it won't get better before it gets worse.
Unfortunately "pretty excellent" is different from "perfect." I haven't kept track, but are you certain that given all possible inputs, the user prompt will never override the system prompt?
Those are strong claims, and unless there's been an advancement in the tech, it doesn't seem possible. Reinforcement learning might make it much less likely, but that's different from impossible.
It is also not always clear who is the user and how much they should be obeyed
Only if you only read the first line in my comment, there is more under that one too.
It is clear, if you make it clear. These bugs happen because they don't clearly understand what should go where.
Why do you need LLMs in the first place if you are whitelisting possible inputs?
You can use a much simpler and less costly system.