CVE-2025-65890 identifies a device-ID validation vulnerability in OneFlow version 0.9.0, a machine learning framework that supports GPU acceleration. The flaw arises because the function flow.cuda.synchronize() does not properly validate the GPU device index parameter. When an attacker supplies an invalid or out-of-range GPU device index, the function attempts to synchronize with a non-existent GPU device, leading to a Denial of Service (DoS) condition. This can cause the application or service relying on OneFlow to crash, hang, or become unresponsive, thereby impacting availability. The vulnerability is classified under CWE-400 (Uncontrolled Resource Consumption), indicating that it can be exploited to exhaust system resources or cause application failure. The CVSS v3.1 base score is 7.5 (high), reflecting that the vulnerability is remotely exploitable without authentication or user interaction, and solely impacts availability without compromising confidentiality or integrity. No patches or fixes are currently listed, and no known exploits have been reported in the wild. The vulnerability affects all deployments of OneFlow v0.9.0 that utilize GPU synchronization features without additional validation. This flaw is particularly relevant for organizations leveraging GPU-accelerated machine learning workloads, as it can disrupt critical AI model training or inference pipelines.

For European organizations, the primary impact of CVE-2025-65890 is service disruption due to Denial of Service conditions in GPU-accelerated applications using OneFlow. This can lead to downtime in AI research, data analytics, and other GPU-dependent computational tasks, potentially delaying projects and increasing operational costs. Industries such as automotive, aerospace, pharmaceuticals, and finance, which increasingly rely on AI and machine learning, may experience interruptions in their workflows. Additionally, cloud service providers hosting GPU resources for European clients could see degraded service quality or customer dissatisfaction. Although the vulnerability does not expose sensitive data or allow unauthorized access, the availability impact can affect business continuity and productivity. Organizations with high GPU utilization and automated pipelines are particularly vulnerable to cascading failures if synchronization calls fail unexpectedly.

To mitigate CVE-2025-65890, organizations should implement input validation on GPU device indices before calling flow.cuda.synchronize() to ensure indices are within valid ranges. Developers should add boundary checks and error handling to prevent invalid device references from triggering synchronization calls. Until an official patch is released, consider restricting access to OneFlow services to trusted users and environments to reduce exploitation risk. Monitoring application logs for crashes or hangs related to GPU synchronization can help detect attempted exploitation. If possible, isolate GPU workloads to minimize impact on other services. Organizations should track OneFlow updates and apply security patches promptly once available. Additionally, engaging with the OneFlow community or vendor for guidance and potential workarounds is advisable. For cloud deployments, configuring resource quotas and limits on GPU usage can help contain the effects of a DoS attack.