CVE-2025-55557 in PyTorch
Summary
by MITRE • 09/25/2025
A Name Error occurs in pytorch v2.7.0 when a PyTorch model consists of torch.cummin and is compiled by Inductor, leading to a Denial of Service (DoS).
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Analysis
by VulDB Data Team • 10/03/2025
The vulnerability identified as CVE-2025-55557 represents a critical denial of service condition within the PyTorch deep learning framework version 2.7.0. This issue specifically manifests when models containing the torch.cummin operation are compiled using the Inductor compiler backend. The problem stems from an improper handling of name resolution during the compilation process, where the system fails to correctly manage variable naming conventions required for the cumulative minimum operation. This name error occurs at the intermediate representation level during code generation, creating a cascading failure that prevents the model from executing properly. The vulnerability affects the broader machine learning ecosystem as PyTorch serves as a foundational framework for numerous AI applications and research projects across various industries.
The technical flaw resides in the Inductor compiler's inability to properly resolve and manage symbol names when processing torch.cummin operations within the computational graph. This name resolution error typically occurs during the lowering phase where high-level PyTorch operations are converted into optimized machine code. The cumulative minimum function requires specific handling of tensor dimensions and element-wise operations that, when combined with the Inductor's compilation logic, creates a scenario where variable names become ambiguous or improperly scoped. This results in the compiler generating invalid code or encountering fatal errors during the compilation process, ultimately leading to a complete system hang or termination. The flaw demonstrates a weakness in the compiler's symbol table management and name mangling procedures, which are fundamental components of modern code generation systems.
The operational impact of this vulnerability extends beyond simple service interruption to potentially disrupt entire machine learning workflows and development environments. When developers attempt to compile models containing torch.cummin operations, the Inductor compiler fails to produce executable code, forcing users to either modify their models or downgrade their PyTorch versions. This creates significant downtime for research teams, production pipelines, and deployment environments that rely on automated model compilation. The vulnerability particularly affects applications in computer vision, natural language processing, and other domains where cumulative operations are frequently used in model architectures. Organizations running AI infrastructure may experience cascading failures when batch processing multiple models, as the DoS condition can propagate through automated compilation systems and CI/CD pipelines.
Mitigation strategies for CVE-2025-55557 should prioritize immediate patch application from PyTorch maintainers, as this vulnerability directly impacts the core functionality of the framework. System administrators should implement temporary workarounds such as avoiding torch.cummin operations in models intended for Inductor compilation or using alternative compiler backends like TorchScript. Organizations should establish monitoring protocols to detect compilation failures and implement fallback mechanisms that automatically switch to alternative compilation strategies when errors are detected. The vulnerability aligns with CWE-457: Use of Uninitialized Variable and CWE-691: Insufficient Control Flow Management, indicating the need for robust error handling and control flow validation in compiler implementations. From an ATT&CK perspective, this vulnerability could be leveraged by adversaries to disrupt AI development environments and cause operational delays in machine learning infrastructure, making it a potential candidate for supply chain attack vectors targeting AI development workflows.