arXiv:2607.01313v1 Announce Type: cross Abstract: In practice, most commercial LLM providers do not publicly release details of underlying LLM architectures. However, prior work has shown that given limited API access to an LLM (namely, top-$k$ logits and/or a logit bias function), one can recover certain architectural details of an LLM, such as the hidden dimension of the feed-forward network. Perhaps in response to these results, most commercial LLM providers have restricted their APIs to expose only the single logit for each decoded token, and they no longer give users the ability to bias l
The increasing sophistication of LLM inference techniques and commercial providers' responses to protect their intellectual property are driving this development.
This indicates a growing cat-and-mouse game between researchers attempting to reverse-engineer LLMs and providers seeking to maintain proprietary control over their models' architectures.
The ability to infer architectural properties of black-box LLMs becomes significantly harder, pushing researchers to develop more advanced inference methods or shifting focus to other attack vectors.
- · LLM providers prioritizing proprietary architecture
- · Researchers developing advanced obfuscation techniques
- · Researchers relying on simpler API inference methods
- · Third-party auditing firms validating LLM properties
Further restrictions on LLM APIs, making even basic model introspection challenging.
Increased investment by LLM providers in security and obfuscation to prevent architectural inference.
A potential chilling effect on independent research into LLM safety, bias, and robustness if internal mechanisms become completely opaque.
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Read at arXiv cs.CL