CVE-2025-55557 is a vulnerability identified in PyTorch version 2.7.0, specifically triggered when a PyTorch model utilizes the function torch.cummin and is compiled using the Inductor compiler backend. The issue manifests as a Name Error during the compilation or execution process, which results in a Denial of Service (DoS) condition. This means that the affected PyTorch model fails to run properly, potentially causing the application or service relying on this model to crash or become unresponsive. The root cause appears to be a missing or improperly referenced identifier within the Inductor compilation process when handling torch.cummin operations. Since PyTorch is a widely used open-source machine learning framework, this vulnerability could impact any system or service that compiles models with Inductor and uses torch.cummin, particularly in production environments where model availability is critical. No known exploits are currently reported in the wild, and no patches or fixes have been linked yet. The absence of a CVSS score indicates that the vulnerability is newly published and may require further analysis for precise risk quantification.

For European organizations, the impact of CVE-2025-55557 could be significant in sectors relying heavily on AI and machine learning models built with PyTorch, such as finance, healthcare, automotive, and research institutions. A Denial of Service caused by this vulnerability could disrupt critical AI-driven services, leading to operational downtime, degraded service quality, or failure of automated decision-making systems. This could result in financial losses, reputational damage, and potential regulatory scrutiny, especially under GDPR where service availability and integrity are important. Organizations deploying PyTorch models in cloud environments or on-premises AI infrastructures that utilize Inductor compilation are particularly at risk. The vulnerability does not appear to allow unauthorized access or data leakage but impacts availability, which is a key component of cybersecurity resilience.

To mitigate this vulnerability, European organizations should first identify if they are using PyTorch version 2.7.0 with models that include torch.cummin compiled via Inductor. Until an official patch is released, organizations should consider the following measures: 1) Avoid compiling models with Inductor if they use torch.cummin, or refactor models to replace torch.cummin with alternative functions if feasible. 2) Implement robust monitoring and alerting for model execution failures to quickly detect DoS conditions. 3) Test model deployments in staging environments to identify potential failures before production rollout. 4) Engage with PyTorch community and vendors for updates and patches. 5) Consider fallback mechanisms or redundancy in AI service architectures to maintain availability if a model fails. 6) Maintain strict version control and environment isolation to prevent inadvertent deployment of vulnerable PyTorch versions.