  Graphic Processing Units (GPUs) have transcended their traditional use-case
of rendering graphics and nowadays also serve as a powerful platform for
accelerating ubiquitous, non-graphical rendering tasks. One prominent task is
inference of neural networks, which process vast amounts of personal data, such
as audio, text or images. Thus, GPUs became integral components for handling
vast amounts of potentially confidential data, which has awakened the interest
of security researchers. This lead to the discovery of various vulnerabilities
in GPUs in recent years. In this paper, we uncover yet another vulnerability
class in GPUs: We found that some GPU implementations lack proper register
initialization routines before shader execution, leading to unintended register
content leakage of previously executed shader kernels. We showcase the
existence of the aforementioned vulnerability on products of 3 major vendors -
Apple, NVIDIA and Qualcomm. The vulnerability poses unique challenges to an
adversary due to opaque scheduling and register remapping algorithms present in
the GPU firmware, complicating the reconstruction of leaked data. In order to
illustrate the real-world impact of this flaw, we showcase how these challenges
can be solved for attacking various workloads on the GPU. First, we showcase
how uninitialized registers leak arbitrary pixel data processed by fragment
shaders. We further implement information leakage attacks on intermediate data
of Convolutional Neural Networks (CNNs) and present the attack's capability to
leak and reconstruct the output of Large Language Models (LLMs).
