CVE-2026-47536 in Virtual GPU Manager
Summary
by MITRE • 09/30/2026
NVIDIA vGPU Virtual GPU Manager for Linux contains a vulnerability in the kernel mode layer where an attacker could cause an out-of-bounds read. A successful exploit of this vulnerability might lead to code execution, denial of service, escalation of privileges, information disclosure, and data tampering.
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Analysis
by VulDB Data Team • 09/30/2026
The NVIDIA vGPU Virtual GPU Manager for Linux presents a critical security risk due to a flaw located within the kernel mode layer of its software stack. This specific component is responsible for managing virtualized graphics processing units in server environments, facilitating resource sharing among multiple virtual machines or containers. The vulnerability manifests as an out-of-bounds read condition, which occurs when the application attempts to access memory locations that lie outside the intended buffer boundaries. In kernel mode operations, such memory safety violations are particularly severe because they bypass standard user-space protections and operate with high-level system privileges. An attacker who can trigger this flaw may be able to read arbitrary data from the kernel's virtual address space, potentially exposing sensitive information stored in memory or causing instability within the host operating system.
From a technical perspective, an out-of-bounds read is classified under CWE-125, which describes reading past the end of a buffer. This type of vulnerability often arises due to insufficient validation of input parameters or incorrect calculations regarding array indices and pointer arithmetic during runtime operations. In the context of NVIDIA's vGPU Manager, this flaw likely stems from improper handling of data structures related to virtual GPU contexts or memory mappings. When an attacker crafts specific inputs that exploit these miscalculations, they can force the kernel to retrieve data from unintended memory addresses. This capability allows for information disclosure where confidential system details, cryptographic keys, or other privileged data may be leaked to the attacker. Furthermore, depending on the exact nature of the accessed memory and subsequent operations, this flaw could potentially lead to more severe consequences such as denial of service through system crashes or even code execution if combined with other exploitation techniques like heap spraying or specific kernel gadget chains.
The operational impact of exploiting this vulnerability is significant for organizations relying on NVIDIA vGPU technology for their infrastructure. A successful exploit could result in a complete compromise of the host server, leading to unauthorized access to virtual machines hosted on that machine. This scenario aligns with ATT&CK technique T1059, Command and Scripting Interpreter, if used as part of a broader attack chain to execute malicious commands within the compromised environment. Additionally, it relates to ATT&CK technique T1083, File and Directory Discovery, or T1005, Data from Local System, when focusing on information disclosure aspects. The ability to escalate privileges is also a major concern, as kernel-mode exploits typically allow attackers to gain root-level access, thereby undermining the isolation guarantees provided by virtualization technologies. This could lead to data tampering where critical application states or persistent storage are modified without authorization, compromising integrity and availability of services.
Mitigation strategies for this vulnerability primarily involve applying vendor-provided patches that address the underlying memory safety issues within the kernel mode driver. Organizations should prioritize updating their NVIDIA vGPU software packages to versions that include fixes for CWE-125 vulnerabilities. In addition to patching, implementing defense-in-depth measures is crucial. This includes restricting access to the management interfaces of virtualization platforms and ensuring that only authorized users can interact with GPU resources. Network segmentation can also help limit lateral movement in case a single host is compromised. Furthermore, enabling kernel hardening features such as Kernel Page Table Isolation (KPTI) or using security modules like SELinux or AppArmor can provide additional layers of protection against exploitation attempts. Regular vulnerability scanning and monitoring for anomalous behavior related to GPU resource usage are recommended practices to detect potential exploitation activities early in the attack lifecycle.