CVE-2025-55558 is a high-severity vulnerability identified in PyTorch version 2.7.0, a widely used open-source machine learning framework. The vulnerability arises from a buffer overflow condition triggered when a PyTorch model includes a specific combination of components: torch.nn.Conv2d (a convolutional neural network layer), torch.nn.functional.hardshrink (a non-linear activation function), and a tensor operation sequence involving torch.Tensor.view followed by torch.mv(). When such a model is compiled using the Inductor compiler backend, this combination leads to improper memory handling that causes a buffer overflow. The consequence of this overflow is a Denial of Service (DoS), where the affected application or service crashes or becomes unresponsive, disrupting normal operations. The CVSS v3.1 base score of 7.5 reflects a high severity, with the vector indicating the vulnerability is remotely exploitable over the network without any required privileges or user interaction, and it impacts availability only, without compromising confidentiality or integrity. The underlying weakness is categorized under CWE-400, which relates to uncontrolled resource consumption, consistent with a DoS condition. No known exploits have been reported in the wild as of the publication date, and no patches or fixes have been linked yet. This vulnerability is significant for environments that deploy PyTorch models compiled with Inductor, especially those exposed to untrusted inputs or remote model execution requests.

For European organizations, the impact of CVE-2025-55558 could be substantial, particularly for sectors relying heavily on AI and machine learning workloads, such as finance, healthcare, automotive, and research institutions. A successful exploitation leads to denial of service, causing downtime of critical AI services, which may disrupt business operations, delay data processing, or halt automated decision-making systems. This can result in financial losses, reduced productivity, and potential reputational damage. Since the vulnerability does not affect confidentiality or integrity, data breaches are unlikely; however, service unavailability can indirectly affect compliance with regulations like GDPR if service interruptions impact data processing timelines or availability commitments. Organizations using PyTorch models compiled with Inductor in cloud or edge environments are at higher risk, especially if these services are accessible over the network without authentication barriers.

To mitigate this vulnerability effectively, European organizations should: 1) Immediately audit their AI/ML pipelines to identify any PyTorch 2.7.0 models that use the combination of torch.nn.Conv2d, torch.nn.functional.hardshrink, and tensor operations involving view and mv compiled with Inductor. 2) Temporarily disable or avoid using the Inductor compiler backend for model compilation until a patch is released. 3) Implement network-level protections such as firewalls and segmentation to restrict access to AI model serving endpoints, minimizing exposure to remote exploitation. 4) Monitor application logs and system metrics for signs of crashes or resource exhaustion indicative of attempted exploitation. 5) Engage with PyTorch community and vendors for updates and patches, and plan for rapid deployment once available. 6) Consider fallback to earlier stable PyTorch versions or alternative compilation backends if feasible. 7) Incorporate runtime protections such as container resource limits and watchdog processes to recover from unexpected crashes promptly.