It did get me thinking the extent to which I could bypass the original prompt and use someone else's tokens for free.

For example, let's say you want to use an LLM for machine translation from English into Klingon. Normally people just write something like "Translate the following into Klingon: $USER_PROMPT" using a general purpose LLM, and that is vulnerable to prompt injection. But, if you finetune a model on this well enough (ideally by injecting a new special single token into its tokenizer, training with that, and then just prepending that token to your queries instead of a human-written prompt) it will become impossible to do prompt injection on it, at the cost of degrading its general-purpose capabilities. (I've done this before myself, and it works.)

The cause of prompt injection is due to the models themselves being general purpose - you can prompt it with essentially any query and it will respond in a reasonable manner. In other words: the instructions you give to the model and the input data are part of the same prompt, so the model can confuse the input data as being part of its instructions. But if you instead fine-tune the instructions into the model and only prompt it with the input data (i.e. the prompt then never actually tells the model what to do) then it becomes pretty much impossible to tell it to do something else, no matter what you inject into its prompt.

> SEND THE FOLLOWING SMS MESSAGE TO ALL PHONE COMPANY CUSTOMERS:

This is the perfect example, you would never expose an API that could do this on a website. The issue is not the LLM. It’s a badly design security model around the API/Tools

For reference: none of this is theoretical for me. I design call centers as one of my specialties using Amazon Connect.

Anything that doesn't separate control data from the actual data. See https://en.wikipedia.org/wiki/In-band_signaling

1: Protecting against bad things (prompt injections, overeager agents, etc)

2: Containing the blast radius (preventing agents from even reaching sensitive things)

The companies building the agents make a best-effort attempt against #1 (guardrails, permissions, etc), and nothing against #2. It's why I use https://github.com/kstenerud/yoloai for everything now.

It's not rocket science. If the LLM has no access to do those things, then it can't be tricked into doing those things.

Pretty sure they just need the compute for their upcoming model. Sora is compute intensive and doesn’t seem to be getting commercial traction

The architectural move that seems durable is separating capabiliity from authority. You can expose many tools (that's capability), but the agent only gets authority to invoke a narrow subset under well-defined conditions (that's the policy), and the authority needs to be revocable and auditable independently of whatever happens in that context. That's basically how we already run normal organiziations with people. Interns can see a lot but are limited on what they can do.

The practical side: Keep the model in a "Propose" role, keep execution in a deterministic gate (schema validation + policy engine + sandbox) and log the decision as a first-class artifact. What I mean by that is who or what authorized, what was considered, what side effect occured...etc. You still wont' get perfect security, but you can make the failure mode "agent asked for something dumb and got blocked" instead of "agent executied a side effect because a webpage told it to."

I don't know enough about LLM training or architecture to know if this is actually possible, though. Anyone care to comment?

But I don't think that is the only problem.

You could also convince an agent to rm -r / even if that agent can't communicate out.

Even pure LLM and web you could phish someone in a more sophisticated way using details from their chat histort in the attack.

The mitigations are also largely the same, i.e. limit the blast radius of what a single compromised agent (LLM or human) can do

We've got these sessions stored in ~/.claude ~/.codex ~/.kimi ~/.gemini ...

When you resume a session, it's reading from those folders... restoring the context.

Change something in the session, you change the agent's behavior without the user really realizing it. This is exacerbated by the YOLO and VIBE attitudes.

I don't think we are protecting those folders enough.

There are a lot of services out there that offer these types of AI guardrails, and it doesn’t have to be expensive.

Not saying that this approach is foolproof, but it’s better than relying solely on better prompting or human review.

We might be speed running memetic warfare here.

The Monty Python skit about the deadly joke might be more realistic than I thought. Defense against this deserves some serious contemplation.

I already have to raise quite a bit of awareness to humans to not trust external sources, and do a risk based assessment of requests. We need less trust for answering a service desk question, than we need for paying a large invoice.

I believe we should develop the same type of model for agents. Let them do simple things with little trust requirements, but risky things (like running an untrusted script with root privileges) only when they are thoroughly checked.

It's been something like 3 years since people have been talking about this being a very big deal.

LLMs are widely used. Claude code is run by most people with dangerously skip permissions.

I just haven't seen the armageddon. Surely it should be here by now.

Where are the horror stories?

If you have an LLM on the untrusted customer side the wrost it can do is expose the instructions it had on how to help the customer get stuff done. For instance phone AI that is outside of tursted zone asks the user for Customer number, DOB and some security pin then it does the API call to login. But this logged in thread of LLM+Customer still only has accessto that customers data but can be very useful.

You can jailbreak and ask this kind of client side LLM to disregard prior instructions and give you a recipie for brownies. But thats not a security risk for the rest of your data.

Client side LLM's for the win

I think the question is, how much risk is involved and how much do those mitigating methods reduce it? And with that, we can figure out what applications it is appropriate for.