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Comment by thunfischtoast | Hacker News Reader

I've caught Fable discovering the ip to a production server in documentation and attempting to connect there on its own to run commands without explicitly being prompted to. It didn't work because I was watching it live and and also the key was password protected, but yeah, I do see some danger.

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I have noticed that Fable tends to macgyver solutions together to achieve some goal.

Not only fable. Opus does this too. Which is exactly why I want to review. Like recently for some task it was convinced in a site dump images are not there and convinced itself db and files were skewed. But it didn’t check the actual site … if I hadn’t stopped it, it would have fine on and on or wasted tokens on some elaborate ‘fix’.

My point is that an LLM can't attempt to connect to anything by itself. All an LLM does is produce a stream of output tokens - and that was already quite useful as a coding aid.

It is the harnesses that some people are now wrapping around LLMs to interpret the output from a model as commands to run (or other executable instructions) that are creating all these new risks. Remember that this is still a very recent development and still more recently amplified by the use of feedback loops and long-running agents intended to operate with minimal human supervision.

It is going to be increasingly important to understand exactly what these tools are doing and why for both correctness and security reasons. Not conflating their capabilities with the underlying model that purely generates data is pretty fundamental here.

Once you're running a model inside the harness... you've got yourself a controller inside a control loop, which is genuinely a different kind of thing than just the model alone.

Are you objecting to terminology here?

Are you proposing we say "Fable-In-Claude-Code tried..." instead?

Hmmm... something like that might be necessary. Sure we should typically be tolerant of loose language; but people do keep referring to wildly different contexts in ai conversations, and end up talking past each other.

Running gemini on web is a genuinely different experience to running Fable in claude code, different again from GPT-5.6 in openclaw, or in an ide or etc ...

Yes - I'm objecting to the lazy use of terminology here. LLMs are useful in their own right and are not the real problem here. The real problem is people placing too much trust in inherently unreliable output and then trying to automate away their responsibility to check that output properly before using it.

Depending on the company, that sounds like a bad environment more than a agent issue, no dev/prod network isolation?

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