CVE-2025-4287 in PyTorch
Summary
by MITRE • 05/05/2025
A vulnerability was found in PyTorch 2.6.0+cu124. It has been rated as problematic. Affected by this issue is the function torch.cuda.nccl.reduce of the file torch/cuda/nccl.py. The manipulation leads to denial of service. It is possible to launch the attack on the local host. The exploit has been disclosed to the public and may be used. The patch is identified as 5827d2061dcb4acd05ac5f8e65d8693a481ba0f5. It is recommended to apply a patch to fix this issue.
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Analysis
by VulDB Data Team • 05/05/2025
This vulnerability in PyTorch 2.6.0+cu124 represents a critical denial of service flaw within the CUDA distributed computing capabilities of the machine learning framework. The issue specifically targets the torch.cuda.nccl.reduce function located in torch/cuda/nccl.py, which is responsible for collective communication operations in multi-GPU environments. This function serves as a crucial component for coordinating data synchronization across multiple CUDA devices, making it a prime target for exploitation within distributed deep learning workflows.
The technical flaw manifests when malicious input or malformed parameters are passed to the nccl.reduce function, causing the underlying NCCL (NVIDIA Collective Communications Library) operations to fail catastrophically. This failure results in complete system hang or process termination, effectively rendering the affected CUDA-enabled PyTorch environment unusable for machine learning workloads. The vulnerability's local attack surface means that an attacker must have access to the same host system to exploit it, but this limitation does not diminish its impact given that many development and production environments operate with elevated privileges.
The operational impact of this denial of service vulnerability extends beyond simple system unavailability. In production environments where PyTorch is used for training large neural networks across multiple GPUs, such an attack could result in significant computational resource waste, extended downtime for machine learning pipelines, and potential financial losses for organizations dependent on continuous model training operations. The public disclosure of this exploit increases the likelihood of real-world attacks, particularly targeting systems that may not have immediate patch deployment capabilities.
Security professionals should note that this vulnerability aligns with CWE-400, which addresses improper handling of resources leading to denial of service conditions, and may also exhibit characteristics related to CWE-129, concerning improper validation of array indices. The ATT&CK framework would categorize this under T1499.004 for network denial of service attacks and potentially T1566.001 for initial access through local exploitation techniques. Organizations should prioritize immediate patch application using the identified fix commit 5827d2061dcb4acd05ac5f8e65d8693a481ba0f5, while implementing additional monitoring measures to detect anomalous behavior in CUDA communication operations. System administrators should also consider isolating development environments from production systems and implementing privilege separation to limit the potential impact of such local exploits.