GPU Driver Privilege Escalation: From User Mode to Kernel via a Single CVE
For executives
The NVIDIA GPU driver is one of the largest kernel-mode codebases in regular use. It runs with kernel privileges, handles inputs from every user-space GPU process, and processes those inputs in code that spans hundreds of thousands of lines. A single exploitable vulnerability in that code — a buffer overflow, a use-after-free, an improper ioctl handler — gives an unprivileged local user kernel-level code execution. This article covers the privilege escalation path from GPU driver exploitation, which exploited CVEs from this library make it traversable, and why the GPU driver represents a high-value escalation target in AI infrastructure.
What GPU driver privilege escalation looks like
A standard user on a Linux GPU server has access to the GPU device files (/dev/nvidia0, /dev/nvidiactl, /dev/nvidia-uvm). Every CUDA operation the user's process performs routes through an ioctl call to one of these device files, which the kernel-mode NVIDIA driver handles.
The driver's ioctl handlers receive arguments from user space. Where they fail to validate those arguments adequately, an attacker can send crafted arguments that cause the handler to write beyond a buffer boundary, access memory after it has been freed, or perform operations on out-of-range indices. Any of these outcomes in kernel-mode code produces privilege escalation.
Exploitable CVEs from this library
CVE-2024-0150: out-of-bounds write in the NVIDIA GPU display driver for Windows and Linux. CVSS 7.1.
CVE-2025-23244: unprivileged attacker can escalate permissions via the NVIDIA GPU Driver for Linux kernel mode. Code execution, privilege escalation.
CVE-2026-24187: use-after-free in the NVIDIA Display Driver for Linux. CVSS 8.8.
CVE-2022-34669: Windows GPU driver user mode flaw. CVSS 8.8. Code execution, privilege escalation.
CVE-2024-36342: AMD GPU driver heap overflow. CVSS 8.8. Arbitrary code execution. Affects Instinct MI300 series.
Why GPU driver privilege escalation is a high-value target in AI infrastructure
Privilege escalation on a GPU server gives access to: GPU memory containing model weights, inference data, and users' personal data; CUDA contexts belonging to other containers and workloads; container runtime credentials for lateral movement; and kubelet credentials for API server access.
The patching cadence reality
GPU driver patching in production AI infrastructure is operationally difficult. Updating the NVIDIA GPU driver requires stopping running workloads, updating the driver (which typically requires a node restart), and restarting workloads. In environments running continuous training jobs, driver updates are operationally disruptive. A CVSS 8.8 use-after-free may sit unpatched on training nodes for weeks or months after disclosure.