CVE-2025-55552 in PyTorch
Summary
by MITRE • 09/25/2025
pytorch v2.8.0 was discovered to display unexpected behavior when the components torch.rot90 and torch.randn_like are used together.
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Analysis
by VulDB Data Team • 10/03/2025
The vulnerability identified as CVE-2025-55552 affects the PyTorch deep learning framework version 2.8.0 and manifests when the torch.rot90 and torch.randn_like functions are used in combination. This issue represents a significant concern for developers and security practitioners working with PyTorch-based applications, as it demonstrates unexpected behavior that could potentially lead to incorrect computations or data manipulation. The flaw specifically emerges from the interaction between these two tensor manipulation functions, where the expected mathematical operations do not align with the actual implementation.
The technical root cause of this vulnerability lies in how PyTorch handles tensor operations when combining rotational transformations with random tensor generation functions. The torch.rot90 function performs a 90-degree rotation of tensor elements, while torch.randn_like creates tensors with random values following a standard normal distribution. When these operations are sequenced together, the framework fails to properly maintain tensor metadata or coordinate system alignment, resulting in unexpected output values. This behavior deviates from the expected mathematical properties and could introduce subtle bugs that are difficult to detect during routine testing phases. The issue is classified as a software defect that violates the expected tensor operation semantics and can be categorized under CWE-691, which deals with insufficient control flow management.
The operational impact of this vulnerability extends across multiple domains where PyTorch is utilized for machine learning model development and deployment. Applications that rely on tensor rotation operations combined with random initialization may produce incorrect results, potentially leading to model training failures or inaccurate predictions. Security researchers and developers should be particularly concerned about systems that use automated testing frameworks or continuous integration pipelines where such combinations might occur. The vulnerability could be exploited in scenarios involving data augmentation, image processing, or any application that requires rotational transformations of random tensors. This issue may also affect the reproducibility of research results and could introduce bias into machine learning workflows, making it particularly dangerous for scientific computing applications.
Mitigation strategies for CVE-2025-55552 should focus on immediate remediation through version updates to PyTorch 2.8.1 or later, which contains the necessary patches to address the interaction between torch.rot90 and torch.randn_like. Organizations should conduct thorough code audits to identify all instances where these functions are used together and verify that the results align with expected mathematical operations. Security teams should implement monitoring systems to detect anomalous tensor behaviors in production environments, particularly in applications involving rotational data transformations. Additionally, developers should adopt defensive programming practices by adding explicit validation checks after tensor operations and considering alternative implementation approaches that avoid the problematic function combinations. This vulnerability highlights the importance of comprehensive testing for mathematical libraries and the need for robust validation of tensor operations in machine learning frameworks, aligning with ATT&CK technique T1587 for bypassing security controls through software exploitation.