Comment by embedding-shape
22 days ago
I'm just guessing, but seems the people who write these agent CLIs haven't found a good heuristic for allowing/disallowing/asking the user about permissions for commands, so instead of trying to sit down and actually figure it out, someone had the bright idea to let the LLM also manage that allowing/disallowing themselves. How that ever made sense, will probably forever be lost on me.
`chroot` is literally the first thing I used when I first installed a local agent, by intuition (later moved on to a container-wrapper), and now I'm reading about people who are giving these agents direct access to reply to their emails and more.
> I'm just guessing, but seems the people who write these agent CLIs haven't found a good heuristic for allowing/disallowing/asking the user about permissions for commands, so instead of trying to sit down and actually figure it out, someone had the bright idea to let the LLM also manage that allowing/disallowing themselves. How that ever made sense, will probably forever be lost on me.
I don't think there is such a good heuristic. The user wants the agent to do the right thing and not to do the wrong thing, but the capabilities needed are identical.
> `chroot` is literally the first thing I used when I first installed a local agent, by intuition (later moved on to a container-wrapper), and now I'm reading about people who are giving these agents direct access to reply to their emails and more.
That's a good, safe, and sane default for project-focused agent use, but it seems like those playing it risky are using agents for general-purpose assistance and automation. The access required to do so chafes against strict sandboxing.
Here's OpenAI's docs page on how they sandbox Codex: https://developers.openai.com/codex/security/
Here's the macOS kernel-enforced sandbox profile that gets applied to processes spawned by the LLM: https://github.com/openai/codex/blob/main/codex-rs/core/src/...
I think skepticism is healthy here, but there's no need to just guess.
That still doesn't seem ideal. Run the LLM itself in a kernel-enforced sandbox, lest it find ways to exploit vulnerabilities in its own code.
The LLM inference itself doesn't "run code" per se (it's just doing tensor math), and besides, it runs on OpenAI's servers, not your machine.
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If I'm following this it means you need to audit all code that the llm writes though as anything you run from another terminal window will be run as you with full permissions.