CVE-2025-55551 in PyTorch
Summary
by MITRE • 09/25/2025
An issue in the component torch.linalg.lu of pytorch v2.8.0 allows attackers to cause a Denial of Service (DoS) when performing a slice operation.
If you want to get best quality of vulnerability data, you may have to visit VulDB.
Analysis
by VulDB Data Team • 10/03/2025
The vulnerability identified as CVE-2025-55551 resides within the torch.linalg.lu component of the PyTorch machine learning framework version 2.8.0, representing a critical denial of service weakness that can be exploited by remote attackers. This issue specifically manifests during slice operations performed on linear algebra computations, where the underlying implementation fails to properly validate input parameters or handle edge cases in the LU decomposition algorithm. The flaw enables malicious actors to craft specific input sequences that trigger unexpected behavior in the computational pipeline, potentially leading to system resource exhaustion or complete application termination.
The technical root cause of this vulnerability stems from inadequate bounds checking and error handling within the slice operation processing logic of the LU decomposition function. When PyTorch processes tensor slices during linear algebra operations, the system does not sufficiently validate the dimensions and indices provided by users, allowing malformed input to propagate through the computational graph. This weakness aligns with CWE-129, which addresses insufficient validation of length of input buffers, and CWE-691, which covers insufficient control of a resource through a long-term exposure. The vulnerability specifically impacts the mathematical computation engine where tensor slicing operations interact with the LU decomposition algorithm, creating a pathway for attackers to manipulate the execution flow through carefully constructed input parameters.
The operational impact of CVE-2025-55551 extends beyond simple service interruption to potentially compromise entire machine learning workflows and data processing pipelines. In production environments where PyTorch serves as a core component for deep learning model training or inference, this vulnerability could enable attackers to disrupt critical operations, particularly in cloud-based machine learning services or AI-powered applications. The denial of service condition can manifest as complete application hang, memory exhaustion, or process termination, affecting not only individual computations but also cascading failures in distributed computing environments where multiple processes depend on the affected system. Organizations utilizing PyTorch for automated model training, real-time inference, or batch processing operations face significant risk from this vulnerability, as it can be exploited through various attack vectors including API endpoints, web interfaces, or direct tensor manipulation.
Mitigation strategies for CVE-2025-55551 require immediate attention from system administrators and security teams responsible for maintaining PyTorch-based environments. The primary recommendation involves upgrading to the latest stable version of PyTorch where the vulnerability has been addressed through enhanced input validation and proper error handling mechanisms. Organizations should implement comprehensive monitoring solutions to detect anomalous tensor operations that might indicate exploitation attempts, particularly focusing on slice operations within linear algebra functions. Additionally, input sanitization measures should be implemented at application boundaries to validate tensor dimensions and indices before processing, reducing the attack surface for this specific vulnerability. From an ATT&CK framework perspective, this vulnerability maps to technique T1499.004, which covers network disruption through resource exhaustion, and T1595.001, involving network reconnaissance through information gathering. Security teams should also consider implementing rate limiting and input validation controls within their machine learning pipelines to prevent exploitation of similar weaknesses in other mathematical computation functions.