CVE-2025-71011 identifies a vulnerability in the OneFlow open-source machine learning framework, specifically in version 0.9.0. The flaw resides in the input validation logic of the tensor creation functions flow.Tensor.new_empty, flow.Tensor.new_ones, and flow.Tensor.new_zeros. These functions are responsible for allocating tensors with specified shapes and initial values. Improper validation of input parameters allows an attacker to craft inputs that can cause the system to allocate excessive resources or trigger unexpected behavior, leading to a Denial of Service (DoS). This DoS could manifest as application crashes, memory exhaustion, or unresponsiveness, disrupting services dependent on OneFlow for tensor computations. The vulnerability does not require authentication or user interaction, making it easier to exploit in exposed environments. No CVSS score has been assigned yet, and no public exploits are known. The lack of patch links suggests remediation is pending or in development. This vulnerability primarily affects availability, with no direct impact on confidentiality or integrity. OneFlow is used in AI model training and inference pipelines, so affected systems may include research institutions, enterprises deploying AI solutions, and cloud services integrating OneFlow. Attackers could leverage this flaw to disrupt AI workloads, causing operational downtime and potential financial or reputational damage.

For European organizations, the primary impact is operational disruption due to Denial of Service conditions triggered by malicious inputs to OneFlow tensor creation functions. Organizations relying on OneFlow for AI model training, inference, or data processing may experience service outages or degraded performance. This could affect sectors such as finance, healthcare, automotive, and research institutions where AI workloads are critical. The DoS could interrupt automated decision-making systems or delay time-sensitive analytics. While confidentiality and integrity are not directly compromised, availability loss can have cascading effects on business continuity and service level agreements. Additionally, organizations without robust input validation or monitoring may be more vulnerable. The absence of known exploits reduces immediate risk but does not eliminate the threat, especially as OneFlow adoption grows in Europe. The vulnerability could also be leveraged as part of multi-stage attacks aiming to distract or degrade defenses.

European organizations should implement strict input validation controls on all user-supplied parameters that interact with OneFlow tensor creation APIs. Until an official patch is released, consider restricting access to OneFlow services to trusted users and networks to reduce exposure. Monitor logs and telemetry for unusual tensor allocation requests or spikes in resource consumption indicative of exploitation attempts. Employ resource limits and quotas at the container or orchestration level to prevent resource exhaustion. Engage with the OneFlow community or vendors for timely updates and patches addressing this vulnerability. Conduct security testing and fuzzing on tensor creation inputs to identify and remediate similar issues proactively. Integrate anomaly detection tools to alert on abnormal AI workload behaviors. Finally, maintain incident response plans that include scenarios involving AI framework DoS attacks to minimize downtime.