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Comment by digitaltrees | Hacker News Reader

I have personally seen AI bypass this multiple times.

Sounds like they're still giving the model the keys to the kingdom, which is my point, stop giving the model the avenue to do catastrophic mistakes, it makes no sense.

If you’re message is in response to me, which I think it is, I deliberately don’t give access to credentials and env variables. I’ve worked to create restrictions and seen AI models use very interesting methods to bypass them.

Even now my prompt says the AI must verify the path of the files it intends to edit, and get permission before editing one file at a time and only after permission. I stop it from ignoring those rules once a day at least.

This is not privilege separation/sandboxing. Separate virtual machine for an agent with limited credentials is reasonably safe approach

I built www.propelcode.app with separate Linux containers, unless you disconnect the container and your computer from the internet the models can escape the sandbox and get information off of your machine.

I am open to being corrected and learning from you if you have a better method of sandboxing

The best way to use LLMs is via tmux where it's running on a disposable VM. 0 chance of it getting information from your local machine.

I am using tmux but not disposable vm. I have thought about something like that but honestly some of the debugging work makes ephemeral environments hard to work with. How are you doing that in your workflow?

We kinda need to architect things with the assumption that all token-output from an LLM can be unpredictably sneaky and malicious.

Alas, humans suck at constant vigilance, we're built to avoid it whenever possible, so a "reverse centaur" future of "do what the AI says but only if you see it's good" is going to suck.

I built my own IDE to replace vscode / cursor so I could design the harness and ensure that the model tool access was secure and limited. But the rest of the industry is YOLO

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