CVE-2025-55558 is a buffer overflow vulnerability identified in PyTorch version 2.7.0, a widely used open-source machine learning framework. The vulnerability specifically arises when a PyTorch model incorporates a combination of torch.nn.Conv2d (a convolutional neural network layer), torch.nn.functional.hardshrink (a non-linear activation function), and a sequence of tensor operations involving torch.Tensor.view followed by torch.mv (matrix-vector multiplication), and the model is compiled using the Inductor backend compiler. The buffer overflow occurs during the compilation or execution phase, leading to memory corruption. This memory corruption can cause a Denial of Service (DoS) condition, where the affected application or service crashes or becomes unresponsive. The vulnerability does not currently have a CVSS score, nor are there known exploits in the wild. No patches or fixes have been published yet. The lack of a CVSS score and patch indicates that this vulnerability is newly disclosed and may require urgent attention from organizations using PyTorch 2.7.0 with the Inductor compiler. The technical root cause is a buffer overflow, a classic memory safety issue that can be exploited to disrupt service availability. However, there is no indication that this vulnerability leads to code execution or data leakage. The vulnerability is triggered by a very specific model architecture and compilation path, which may limit its exposure but still poses a risk to systems running such models in production or research environments.

For European organizations, the primary impact of CVE-2025-55558 is a Denial of Service affecting machine learning workloads that utilize PyTorch 2.7.0 with the Inductor compiler and the specified model components. This can disrupt AI-driven services, data processing pipelines, or research computations, potentially causing downtime and loss of productivity. Organizations relying on AI for critical functions such as healthcare diagnostics, financial modeling, autonomous systems, or industrial automation may face operational interruptions. Although no data breach or code execution is reported, the DoS can degrade service reliability and trustworthiness. The impact is particularly relevant for sectors with high AI adoption, including technology companies, research institutions, and industries implementing AI at scale. Since PyTorch is popular across Europe, especially in countries with strong AI research and development ecosystems, the vulnerability could affect a broad range of organizations. The absence of known exploits reduces immediate risk, but the vulnerability's presence in a core AI framework necessitates proactive mitigation to avoid potential future exploitation or accidental service outages.

European organizations should immediately audit their use of PyTorch 2.7.0, specifically checking for models that use torch.nn.Conv2d, torch.nn.functional.hardshrink, and tensor operations involving view and mv compiled with Inductor. Until an official patch is released, organizations should consider the following mitigations: 1) Avoid using the Inductor compiler for models matching the vulnerable pattern or revert to an earlier stable compiler backend; 2) Refactor models to exclude the specific combination of layers and operations triggering the overflow; 3) Implement runtime monitoring and alerting for crashes or abnormal terminations in AI workloads; 4) Isolate AI workloads in sandboxed or containerized environments to limit the impact of potential DoS; 5) Engage with PyTorch community and vendors for updates and patches, and apply them promptly once available; 6) Conduct thorough testing of AI models after any changes to detect instability; 7) Maintain backups and failover mechanisms for critical AI services to minimize downtime. These targeted mitigations go beyond generic advice by focusing on the specific conditions that trigger the vulnerability and the operational context of AI workloads.