r/LocalLLaMA asked why flagship model weights do not leak more often

A r/LocalLLaMA thread started with a blunt question: what actually stops an insider at a major lab from exporting flagship model weights and leaking them? The poster noted that LLM weights can look more self-contained and portable than traditional enterprise software, then asked why this does not happen more often.

The top answers were practical rather than dramatic. Large model weights are not usually one convenient file; they are huge, sharded, access-controlled assets. Corporate laptops and internal systems log a lot of activity, from removable devices to large transfers. Several commenters also emphasized that most employees likely do not have direct access to final weights at all. Access tends to be scoped to people close to training, infrastructure, or release workflows.

The second answer was incentive. Getting caught could mean losing a high-paying job, being blacklisted, facing civil claims over damages, and possibly much worse depending on jurisdiction and circumstances. Commenters also corrected the common Llama 1 story: it was broadly shared with approved researchers, then redistributed, which is different from a quiet internal exfiltration from a locked-down lab machine.

What made the thread useful was that it treated security as layers of friction, not magic. Size, sharding, least privilege, monitoring, anomaly detection, legal exposure, and social trust all stack together. None of those factors makes insider risk disappear, but together they make a clean, quiet leak much harder than the phrase “copy the weights” implies.

For the LocalLLaMA audience, which naturally wants more open weights, the thread was a grounded look at the operating reality of closed frontier models. The important answer was not “it is impossible.” It was “it is detectable, costly, and limited to far fewer people than outsiders may imagine.”