CVE-2025-55557 is a high-severity vulnerability identified in PyTorch version 2.7.0, specifically affecting models that utilize the torch.cummin operation when compiled using the Inductor compiler backend. The vulnerability manifests as a Name Error during execution, which leads to a Denial of Service (DoS) condition. This occurs because the Inductor compiler fails to correctly handle the torch.cummin function, causing the runtime to encounter an unhandled exception that crashes the model execution process. The vulnerability is classified under CWE-248 (Uncaught Exception), indicating that the software does not properly handle an error condition, resulting in a crash or service disruption. The CVSS v3.1 base score of 7.5 reflects a high severity level, with an attack vector of network (AV:N), low attack complexity (AC:L), no privileges required (PR:N), no user interaction needed (UI:N), and impact limited to availability (A:H) without affecting confidentiality or integrity. No known exploits are currently reported in the wild, and no patches have been published yet. This vulnerability primarily affects environments where PyTorch 2.7.0 is used for model compilation and execution, particularly in automated or production AI/ML pipelines that rely on Inductor for performance optimization. The failure mode leads to service unavailability, potentially disrupting AI-driven applications or services that depend on these models.

For European organizations, the impact of CVE-2025-55557 can be significant in sectors heavily reliant on AI and machine learning, such as finance, healthcare, automotive, and telecommunications. Organizations using PyTorch 2.7.0 with Inductor for model compilation may experience unexpected service outages or disruptions due to the DoS condition triggered by this vulnerability. This can lead to downtime in AI-powered applications, loss of productivity, and potential financial losses. In critical infrastructure or real-time systems, such as autonomous driving or medical diagnostics, such disruptions could have safety implications or degrade service quality. Since the vulnerability does not affect confidentiality or integrity, data breaches are unlikely; however, availability loss can still undermine trust and operational continuity. The lack of required privileges or user interaction means attackers can remotely trigger the DoS simply by submitting crafted inputs to vulnerable models, increasing the risk of exploitation in exposed AI services. The absence of known exploits currently reduces immediate risk but does not eliminate the threat, especially as awareness grows.

To mitigate CVE-2025-55557, European organizations should first audit their AI/ML environments to identify usage of PyTorch version 2.7.0 and determine if the Inductor compiler backend is employed, particularly in models using torch.cummin. Until an official patch is released, organizations should consider the following specific actions: 1) Temporarily disable or avoid using the Inductor compiler for models involving torch.cummin operations, reverting to alternative compilation backends or running models in eager mode to prevent the Name Error; 2) Implement input validation and sanitization to detect and block inputs that could trigger the vulnerability, reducing exposure to crafted malicious inputs; 3) Monitor AI/ML service logs and runtime errors for signs of this specific Name Error or unexpected crashes to enable rapid detection and response; 4) Isolate AI/ML inference services behind network controls and apply rate limiting to reduce the risk of remote DoS attempts; 5) Engage with PyTorch maintainers and subscribe to security advisories to promptly apply patches once available; 6) Consider deploying fallback mechanisms or redundancy in AI services to maintain availability during potential disruptions. These targeted mitigations go beyond generic advice by focusing on the specific compiler backend and function involved.