CVE-2019-5698 in Virtual GPU Manager
Summary
by MITRE
NVIDIA Virtual GPU Manager, all versions, contains a vulnerability in the vGPU plugin, in which an input index value is incorrectly validated, which may lead to denial of service.
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Analysis
by VulDB Data Team • 11/09/2019
The NVIDIA Virtual GPU Manager vulnerability identified as CVE-2019-5698 represents a critical flaw in the vGPU plugin component that affects all versions of the software. This vulnerability stems from inadequate validation of input index values within the virtual GPU management system, creating a potential pathway for malicious actors to disrupt normal system operations. The issue manifests when the system fails to properly verify index parameters submitted to the vGPU plugin, allowing for malformed or out-of-bounds inputs to be processed without proper sanitization. This weakness exists at the interface level where user or system inputs are accepted and processed, making it particularly dangerous in virtualized environments where multiple users or applications interact with the GPU resources.
The technical implementation of this vulnerability involves the vGPU plugin's failure to perform boundary checks on index values used for memory allocation or resource management operations. When an attacker submits an invalid index value, the system may attempt to access memory locations or resources that are either non-existent or outside the intended operational boundaries. This misvalidation can cause the vGPU plugin to crash or enter an unstable state, resulting in complete denial of service for the virtual GPU functionality. The flaw operates at the kernel level within the NVIDIA driver architecture, where the plugin interfaces with the underlying GPU hardware and virtualization layers. According to CWE classification, this vulnerability maps to CWE-129 Input Validation and Bounds Checking, specifically addressing insufficient validation of index values that can lead to arbitrary code execution or system instability.
From an operational perspective, this vulnerability poses significant risks to organizations relying on NVIDIA virtual GPU solutions for cloud computing, virtual desktop infrastructure, or high-performance computing environments. The denial of service condition can affect multiple virtual machines simultaneously, potentially disrupting critical business applications or services that depend on GPU acceleration. Attackers can exploit this weakness by simply submitting malformed index values to trigger the vulnerability, making it relatively easy to implement and highly effective. The impact extends beyond simple service disruption as it can compromise the integrity of the entire virtual GPU management system, potentially allowing for further exploitation or escalation attacks. This vulnerability particularly affects enterprise environments using NVIDIA vGPU technology for remote desktop services, where a single compromised session could impact multiple users or virtual machines.
The mitigation strategies for CVE-2019-5698 involve immediate patching of the NVIDIA Virtual GPU Manager software to address the input validation flaw. Organizations should implement network segmentation and access controls to limit exposure to the vulnerable vGPU plugin interface. Monitoring systems should be configured to detect unusual index value patterns or repeated failed access attempts that may indicate exploitation attempts. Security teams should also consider implementing runtime protections and input sanitization measures at the application level to prevent malformed data from reaching the vulnerable components. The ATT&CK framework categorizes this vulnerability under T1499 Contention, specifically targeting system resources through denial of service attacks. Organizations should also review their incident response procedures to ensure rapid detection and remediation of such vulnerabilities, particularly in environments where virtual GPU resources are heavily utilized. Regular vulnerability assessments and penetration testing focused on virtualization components can help identify similar weaknesses before they can be exploited by malicious actors.