// The policy is embedded as a JSON-escaped value inside a structured JSON object.
// This prevents prompt injection via policy content — any special characters,
// delimiters, or instruction-like text in the policy are safely escaped by
// json.Marshal rather than concatenated as raw text.
Really cool! I'm also building something in this space but taking a slightly different approach. I'm glad to see more focus on security for production agentic workflows though, as I think we don't talk about it enough when it comes to claws and other autonomous agents.
I think you're spot on with the fact that it's so far it's been either all or nothing. You either give an agent a lot of access and it's really powerful but proportionally dangerous or you lock it down so much that it's no longer useful.
I like a lot of the ideas you show here, but I also worry that LLM-as-a-judge is fundamentally a probabilistic guardrail that is inherently limited. How do you see this? It feels dangerous to rely on a security system that's not based on hard limitations but rather probabilities?
It looks as if this tool has traditional static rules to allow/deny requests, as well as a secondary LLM-as-a-judge layer for, I imagine, the kinds of rules that would be messy or too convoluted to implement using standard rules.
I think the parent’s point is that this should be implemented using e.g. Bayesian statistics rather than an LLM, as the judge LLM is vulnerable to the exact same types of attacks that it’s trying to protect against.
> We’re supposed to be fixing LLM security by adding a non-LLM layer to it,
If people said "we build a ML-based classifier into our proxy to block dangerous requests" would it be better? Why does the fact the classifier is a LLM make it somehow worse?
The fact that LLMs are "smarter" is also their weakness. An oldschool classifier is far from foolproof, but you won't get past it by telling it about your grandma's bedtime story routine.
If you're working in a mission-critical field like healthcare, defense, etc. you need a way to make static and verifiable guarantees that you can't leak patient data, fighter jet details etc. through your software. This is either mandated by law or in your contract details.
The entire purpose of LLMs is to be non-static: they have no deterministic output and can't be validated the same way a non-LLM function can be. Adding another LLM layer is just adding another layer of swiss cheese and praying the holes don't line up. You have no way of predicting ahead of time whether or not they will.
You might say this hasn't prevented leaks/CVEs in exisiting mission-critical software and this would be correct. However, the people writing the checks do not care. You get paid as long as you follow the spec provided. How then, in a world which demands rigorous proof do you fit in an LLM judge?
I think this can be great as additional layer of security. Where you can have a non llm layer do some analysis with some static rules and then if something might seem phishy run it through the llm judge so that you don’t have to run every request through it, which would be very expensive.
Edit: actually looks like it has two policy engines embedded
And we don't think the judge can/will be gamed? Also... It's an LLM, it's going to add delay and additional token burn. One subjective black box protecting another subjective black box. I mean, what couldn't go wrong?
How can it result in a higher level of control? I don't see why the "judge" should have access to anything except one tool that allows it to send an "accept" or "deny" command.
I do think this is likely to make things more secure but it's also dangerous by potentially giving users a false sense of complete security when the security layer is probabilistic rather than deterministic.
EDIT: it does seem to have a deterministic layer too and I think that's great
I think you're spot on with the fact that it's so far it's been either all or nothing. You either give an agent a lot of access and it's really powerful but proportionally dangerous or you lock it down so much that it's no longer useful.
I like a lot of the ideas you show here, but I also worry that LLM-as-a-judge is fundamentally a probabilistic guardrail that is inherently limited. How do you see this? It feels dangerous to rely on a security system that's not based on hard limitations but rather probabilities?
not adding LLM layers to stuff to make them inherently less secure.
This will be a neat concept for the types of tools that come after the present iteration of LLMs.
Unless I’m sorely mistaken.
Most proper LLM guardrails products use both.
If people said "we build a ML-based classifier into our proxy to block dangerous requests" would it be better? Why does the fact the classifier is a LLM make it somehow worse?
The entire purpose of LLMs is to be non-static: they have no deterministic output and can't be validated the same way a non-LLM function can be. Adding another LLM layer is just adding another layer of swiss cheese and praying the holes don't line up. You have no way of predicting ahead of time whether or not they will.
You might say this hasn't prevented leaks/CVEs in exisiting mission-critical software and this would be correct. However, the people writing the checks do not care. You get paid as long as you follow the spec provided. How then, in a world which demands rigorous proof do you fit in an LLM judge?
Edit: actually looks like it has two policy engines embedded
EDIT: it does seem to have a deterministic layer too and I think that's great