Don't just have fun running local models — there are plenty of data pitfalls to watch out for.

A lot of people think that keeping models local means they’re safe, but it’s not that simple. The source of the weight files, the dependencies of the inference framework, and those plugins and extensions you slap on for convenience—every single link in the chain can be a weak point.

I’ve seen people pull entire model packs from unknown sources, run them without anyone checking what’s inside. Local deployment does dodge the risk of uploading data to the cloud, but it also means all the security responsibility falls on you. Logs, access control, model source verification—you can’t skip any of it.

Image and video generation models especially need attention; both the training data and the output can carry compliance risks. Don’t let the word “local” lull you into a false sense of security—every necessary safeguard needs to be in place.

“Downloading the whole package from some random source” is way too real, I’ve seen that happen a bunch of times.

Local doesn’t mean safe—it just shifts the responsibility to someone else.

Yeah, the dependency chain is the most overlooked part, totally agree with that.

Honestly, barely anyone actually bothers with access control and logging.

Oh man, plugins and extensions are definitely the worst offenders.