CVE-2025-4287 is a medium-severity vulnerability identified in PyTorch version 2.6.0+cu124, specifically affecting the function torch.cuda.nccl.reduce located in the torch/cuda/nccl.py file. This function is part of PyTorch's CUDA backend, which leverages NVIDIA's NCCL (NVIDIA Collective Communications Library) for efficient multi-GPU communication. The vulnerability allows an attacker with local access and low privileges to manipulate the function in a way that triggers a denial of service (DoS) condition. The attack does not require user interaction or elevated privileges beyond low-level local access, and it does not impact confidentiality, integrity, or availability beyond causing service disruption. The vulnerability is exploitable locally, meaning an attacker must have access to the host running the vulnerable PyTorch version. The exploit has been publicly disclosed, increasing the risk of exploitation, although no known widespread exploitation in the wild has been reported to date. A patch has been identified (commit 5827d2061dcb4acd05ac5f8e65d8693a481ba0f5) and should be applied to remediate the issue. The CVSS 4.0 base score is 4.8, reflecting the local attack vector, low complexity, no privileges required, and limited impact scope. This vulnerability primarily affects environments running PyTorch 2.6.0+cu124 with CUDA-enabled GPUs, commonly used in machine learning and AI workloads, especially in research, development, and production systems that rely on GPU acceleration for deep learning tasks.

For European organizations, the impact of CVE-2025-4287 centers on the potential disruption of AI and machine learning workloads that utilize PyTorch with CUDA-enabled GPUs. Organizations in sectors such as automotive, finance, healthcare, and research institutions that deploy GPU-accelerated AI models may experience service interruptions or degraded performance due to denial of service conditions triggered by this vulnerability. While the vulnerability does not compromise data confidentiality or integrity, the availability impact could delay critical AI-driven operations, model training, or inference tasks, potentially affecting business continuity and operational efficiency. The local attack vector limits the threat to insiders or compromised local accounts, reducing the risk of remote exploitation. However, in multi-tenant or shared computing environments common in European research centers and cloud providers, an attacker with access to a shared host could disrupt other users’ workloads. Given the increasing reliance on AI technologies across Europe, even temporary denial of service can have cascading effects on dependent services and research outcomes.

European organizations should take the following specific mitigation steps: 1) Immediately apply the official patch identified by commit 5827d2061dcb4acd05ac5f8e65d8693a481ba0f5 to all PyTorch installations running version 2.6.0+cu124 with CUDA support. 2) Restrict local access to systems running vulnerable PyTorch versions by enforcing strict access controls and monitoring local user activities to prevent unauthorized exploitation. 3) In multi-tenant or shared GPU environments, implement containerization or virtualization isolation techniques to limit the impact of a local denial of service attack on other tenants. 4) Monitor system logs and GPU utilization metrics for unusual activity that may indicate exploitation attempts. 5) Coordinate with cloud service providers to ensure patched PyTorch versions are deployed in managed environments. 6) Educate developers and data scientists about the importance of updating PyTorch dependencies promptly and verifying the security posture of their AI infrastructure. 7) Consider implementing runtime protections or watchdog mechanisms that can detect and recover from GPU-related service disruptions to minimize downtime.