Preface: The torch[.]nn[.]MaxPool2d function in PyTorch is used to apply a 2D max pooling operation over an input signal, which is typically an image or a batch of images.
- Torch[.]mkldnn_max_pool2d is optimized for Intel’s MKL-DNN (Math Kernel Library for Deep Neural Networks). It leverages specific optimizations for Intel CPUs, which can lead to better performance on those processors. It might have limitations in terms of supported features and is more specialized for performance optimization.
- Torch[.]nn[.]MaxPool2d is a more general implementation that works across different hardware platforms without specific optimizations for Intel CPUs. It provides more flexibility and is easier to use within the PyTorch ecosystem, supporting various features like padding, dilation, and return indices.
Background: A floating point exception crash when using torch[.]mkldnn_max_pool2d can occur due to several reasons, often related to invalid or extreme values for parameters like kernel size, stride, or padding. Here are some common causes:
- Invalid Kernel Size: If the kernel size is set to an extremely large value or zero, it can lead to division by zero or other invalid operations, causing a floating point exception.
- Stride and Padding Issues: Similar to kernel size, setting stride or padding to extreme values can result in invalid calculations. For example, a stride of zero can cause the pooling operation to repeatedly access the same elements, leading to a crash.
- Input Tensor Dimensions: If the dimensions of the input tensor are not compatible with the specified kernel size, stride, or padding, it can lead to invalid memory access or calculations.
Vulnerability details: A vulnerability, which was classified as problematic, has been found in PyTorch 2.6.0+cu124. Affected by this issue is the function torch[.]mkldnn_max_pool2d. The manipulation leads to denial of service. An attack has to be approached locally. The exploit has been disclosed to the public and may be used.
Official announcement: Please refer to the link for details – https://nvd.nist.gov/vuln/detail/CVE-2025-2953