CVE-2025-46149 is a vulnerability identified in the PyTorch machine learning framework versions prior to 2.7.0. The issue arises specifically when the 'inductor' backend is used in conjunction with the nn.Fold module, which is a neural network layer used to fold patches of an input tensor into a larger tensor. The vulnerability manifests as an assertion error within nn.Fold, which likely causes the application to crash or behave unexpectedly during runtime. While the exact root cause details are not provided, assertion errors typically indicate a failure in internal consistency checks, potentially triggered by malformed inputs or unexpected states during tensor operations. Since PyTorch is widely used for developing and deploying machine learning models, especially in research and production environments, this vulnerability could disrupt AI workflows that rely on the inductor backend and nn.Fold layer. The absence of a CVSS score and known exploits in the wild suggests that this vulnerability is newly disclosed and may not yet be actively exploited, but it poses a risk of denial of service or application instability. The vulnerability does not appear to directly enable code execution or data leakage, but the assertion failure could be leveraged to cause service interruptions or potentially be chained with other vulnerabilities for more severe impact.

For European organizations, the impact of CVE-2025-46149 depends largely on their reliance on PyTorch for AI and machine learning workloads, particularly those using the inductor backend and nn.Fold layer. Organizations in sectors such as automotive, finance, healthcare, and research institutions that deploy AI models for critical decision-making or customer-facing services could experience disruptions if their applications crash or behave unpredictably due to this assertion error. This could lead to downtime, degraded service quality, and potential loss of trust from customers or partners. Additionally, organizations using automated AI pipelines might face delays or failures in model training and inference processes. While the vulnerability does not appear to compromise data confidentiality or integrity directly, denial of service conditions could indirectly affect availability of AI-driven services. Given the increasing adoption of AI technologies across Europe, especially in countries with strong AI research and industrial sectors, the operational impact could be significant if unmitigated.

To mitigate this vulnerability, European organizations should prioritize upgrading PyTorch installations to version 2.7.0 or later, where the assertion error in nn.Fold when using the inductor backend is resolved. Until the patch is applied, organizations should consider disabling the inductor backend if feasible or avoiding the use of the nn.Fold layer in their models to prevent triggering the assertion error. Rigorous testing of AI models and pipelines in staging environments before deployment can help identify if the vulnerability affects specific workflows. Monitoring application logs for assertion failures or crashes related to nn.Fold can provide early detection of exploitation attempts or accidental triggers. Additionally, organizations should maintain an inventory of AI workloads and dependencies to quickly assess exposure and respond to updates. Collaborating with AI framework vendors and staying informed through security advisories will ensure timely application of fixes. For critical production environments, implementing redundancy and failover mechanisms for AI services can reduce the impact of potential service interruptions caused by this vulnerability.