NVIDIA GPU Display Driver Kernel Mode Layer Vulnerability Leading to Privilege Escalation
Overview
NVIDIA released a security bulletin detailing a high-severity vulnerability in the kernel mode layer of its GPU Display Driver for both Windows and Linux. The vulnerability, tracked as CVE-2025-48211, could allow a local attacker with basic user privileges to achieve arbitrary code execution in the kernel, leading to a full system compromise. The root cause is a use-after-free error in how the driver handles memory mapping for certain graphics processing operations. A malicious local application can send a specially crafted sequence of API calls to the driver to trigger this condition. By carefully manipulating system memory shortly after, an attacker can overwrite a freed function pointer in kernel space and hijack the system's execution flow when the driver later attempts to call it. Successful exploitation grants the attacker the equivalent of `NT AUTHORITY\SYSTEM` or `root` privileges, allowing them to bypass all security measures, disable security software, and install persistent malware. The vulnerability affects a wide range of consumer (GeForce) and enterprise (Quadro, Tesla) GPUs. This highlights the critical importance of keeping GPU drivers, which operate at a highly privileged level, up to date as part of a regular patch management cycle, especially on multi-user systems or MLOps infrastructure where users can submit and run arbitrary code.
Affected Systems
Testing Guide
1. **Check Driver Version (Windows):** Open the NVIDIA Control Panel, go to `Help` -> `System Information`. Compare the `Driver version` with the patched versions mentioned in the NVIDIA security bulletin. 2. **Check Driver Version (Linux):** Run the command `nvidia-smi` in your terminal. The driver version is displayed in the top right corner of the output. Compare this with the patched versions. 3. **Vulnerability Scanning:** Use a system vulnerability scanner that has been updated with plugins for this CVE to scan affected hosts.
Mitigation Steps
1. **Update Drivers:** Download and install the latest NVIDIA GPU driver for your specific hardware from the official NVIDIA website or through your Linux distribution's package manager. 2. **Restrict GPU Access:** In multi-tenant environments (e.g., JupyterHub, Kubernetes), use security controls to limit which users and pods can access the GPU device nodes (`/dev/nvidia*`). 3. **Use Secure Base Images:** For containerized AI workloads, ensure your base images use the latest patched versions of the NVIDIA drivers and CUDA toolkit.
Patch Details
Patches are available in NVIDIA driver versions 545.31 (Windows) and 545.87 (Linux) and later.