CVE-2025-2953 is a denial of service (DoS) vulnerability identified in the PyTorch machine learning framework, specifically affecting version 2.6.0+cu124. The vulnerability resides in the function torch.mkldnn_max_pool2d, which is part of PyTorch's implementation of max pooling operations optimized with Intel's MKL-DNN (oneDNN) backend. The flaw allows an attacker with local access to trigger a denial of service condition by manipulating inputs to this function, causing the application or service running PyTorch to crash or become unresponsive. Exploitation requires local access, meaning the attacker must have the ability to execute code or commands on the affected system. The vulnerability has been publicly disclosed, but there is some uncertainty regarding its practical existence or exploitability, as noted by the original report. No patches or fixes have been published at this time, and no known exploits are currently observed in the wild. The PyTorch project security policy also warns users to exercise caution when using unknown or untrusted models, as these could potentially trigger malicious effects, possibly related to this or other vulnerabilities. Overall, this vulnerability highlights a risk in the handling of specific tensor operations within PyTorch's MKL-DNN optimized layers that could be leveraged to disrupt service availability on affected systems.

For European organizations, the impact of this vulnerability primarily concerns availability disruptions in environments running PyTorch 2.6.0+cu124, especially those utilizing the MKL-DNN backend for performance optimization. Organizations relying on PyTorch for AI model training, inference, or deployment in research, finance, healthcare, or industrial automation could face service interruptions if an attacker gains local access and triggers the DoS condition. Although the attack requires local access, the risk is elevated in multi-tenant environments such as shared research clusters, cloud-based AI platforms, or enterprise servers where multiple users have execution privileges. Disruptions could delay critical AI workloads, impact business operations, or degrade service quality. The lack of a patch and the public disclosure increase the risk of future exploitation attempts. However, the uncertainty about the vulnerability's practical exploitability and the absence of known exploits somewhat mitigate immediate risk. Confidentiality and integrity impacts appear minimal, as the vulnerability specifically causes denial of service rather than data leakage or corruption.

Given the local access requirement and the specific function affected, European organizations should implement the following targeted mitigations: 1) Restrict local execution privileges to trusted users only, minimizing the risk of malicious local code execution. 2) Avoid running untrusted or unknown PyTorch models, especially those sourced externally, to reduce the chance of triggering the vulnerability. 3) Monitor and audit usage of PyTorch environments, particularly focusing on the invocation of MKL-DNN optimized functions like mkldnn_max_pool2d. 4) Employ containerization or sandboxing techniques to isolate PyTorch workloads, limiting the blast radius of potential DoS attacks. 5) Maintain up-to-date backups and implement robust service restart and recovery procedures to minimize downtime if a DoS occurs. 6) Engage with PyTorch community and security advisories to track the release of patches or updates addressing this vulnerability. 7) Consider temporarily disabling MKL-DNN acceleration if feasible, or using alternative backends until a fix is available, to avoid triggering the vulnerable code path.