CVE-2025-46708 in Graphics DDK
Summary
by MITRE • 06/27/2025
Software installed and running inside a Guest VM may conduct improper GPU system calls to prevent other Guests from running work on the GPU.
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Analysis
by VulDB Data Team • 10/22/2025
This vulnerability represents a critical hypervisor-level security flaw that enables malicious virtual machines to manipulate GPU resources in ways that can severely impact system integrity and performance. The issue stems from inadequate isolation mechanisms within virtualized GPU environments where guest operating systems can execute improper system calls that interfere with GPU operations. Such behavior violates fundamental virtualization principles and creates potential for resource exhaustion attacks that can affect multiple virtual machines sharing the same physical GPU hardware. The vulnerability specifically targets the communication pathways between guest VMs and the underlying GPU subsystem, allowing for unauthorized access patterns that can prevent legitimate workloads from utilizing GPU resources effectively.
The technical implementation of this flaw involves improper GPU system calls that bypass normal access controls and resource management protocols. These calls typically occur through hypervisor interfaces that manage GPU virtualization, where insufficient input validation and privilege checking allows guest VMs to manipulate GPU state directly. The vulnerability may leverage mechanisms such as GPU memory mapping, command buffer execution, or hardware context switching to achieve its disruptive effects. Attackers can exploit this weakness by crafting specific GPU operations that either consume excessive GPU resources or manipulate GPU scheduling mechanisms to starve other VMs of necessary computational capacity. This type of vulnerability directly relates to CWE-264, which addresses permissions, privileges, and access controls, and falls under the broader category of privilege escalation issues in virtualized environments.
The operational impact of this vulnerability extends beyond simple performance degradation to potentially enable complete system compromise and denial of service conditions across multiple virtualized workloads. When exploited, malicious VMs can effectively monopolize GPU resources, causing legitimate workloads to fail or experience severe performance degradation. This creates cascading effects in cloud computing environments where multiple tenants share GPU hardware, potentially allowing one compromised tenant to affect all other users on the same physical infrastructure. The attack surface is particularly concerning in multi-tenant cloud deployments, containerized environments, and high-performance computing clusters where GPU virtualization is extensively utilized. The vulnerability can also facilitate more sophisticated attacks such as side-channel information leakage or cache poisoning that leverage GPU-specific characteristics to extract sensitive data from other VMs.
Mitigation strategies for this vulnerability must address both the immediate security gap and the underlying architectural weaknesses that enable such exploitation. Organizations should implement strict hypervisor-level controls that monitor and restrict GPU system calls from guest VMs, particularly those that manipulate GPU state or access memory directly. The implementation of GPU resource quotas and isolation policies can help prevent any single VM from consuming excessive GPU resources. Regular hypervisor updates and patches should be applied immediately to address known vulnerabilities, while security monitoring systems should be enhanced to detect unusual GPU access patterns that may indicate exploitation attempts. Network segmentation and micro-segmentation approaches can further limit the scope of potential attacks by restricting GPU access between VMs. Additionally, organizations should consider implementing GPU-specific security controls such as memory protection units, hardware-based isolation mechanisms, and regular security audits of GPU virtualization configurations. The remediation efforts should also include comprehensive testing of GPU virtualization environments to ensure that proper isolation boundaries are maintained between different VM workloads.