Hardware and Software Platform Inference
Authors: name: Cheng Zhang, name: Hanna Foerster, name: Robert D. Mullins, name: Yiren Zhao, name: Ilia Shumailov

It is now a common business practice to buy access to large language model
(LLM) inference rather than self-host, because of significant upfront hardware
infrastructure and energy costs. However, as a buyer, there is no mechanism to
verify the authenticity of the advertised service including the serving
hardware platform, e.g. that it is actually being served using an NVIDIA H100.
Furthermore, there are reports suggesting that model providers may deliver
models that differ slightly from the advertised ones, often to make them run on
less expensive hardware. That way, a client pays premium for a capable model
access on more expensive hardware, yet ends up being served by a (potentially
less capable) cheaper model on cheaper hardware. In this paper we introduce
\textbf{hardware and software platform inference (HSPI)} -- a method
for identifying the underlying  architecture and software stack of a
(black-box) machine learning model solely based on its input-output behavior.
Our method leverages the inherent differences of various  architectures
and compilers to distinguish between different  types and software
stacks. By analyzing the numerical patterns in the model's outputs, we propose
a classification framework capable of accurately identifying the  used
for model inference as well as the underlying software configuration. Our
findings demonstrate the feasibility of inferring  type from black-box
models. We evaluate HSPI against models served on different real hardware and
find that in a white-box setting we can distinguish between different s
with between 83.9% and 100% accuracy. Even in a black-box setting we are
able to achieve results that are up to three times higher than random guess
accuracy.