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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 restrictio
by digitaltrees 5mo ago
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.
- suchar 5mo agoThis is not privilege separation/sandboxing. Separate virtual machine for an agent with limited credentials is reasonably safe approach
- digitaltrees 5mo agoI 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
- Anon1096 5mo agoThe 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.
- digitaltrees 5mo agoI 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?