PyTorch is a Python package that provides tensor computation with strong GPU acceleration and deep neural networks built on a tape-based autograd system. In version 2.5.1 and prior, a Remote Command Execution (RCE) vulnerability exists in PyTorch when loading a model using torch.load with weights_only=True. This issue has been patched in version 2.6.0.

Affected versions of the PyTorch package are vulnerable to Denial of Service (DoS) due to improper handling in the MKLDNN pooling implementation. The torch.mkldnn_max_pool2d function fails to properly validate input parameters, allowing crafted inputs to trigger resource exhaustion or crashes in the underlying MKLDNN library. An attacker with local access can exploit this vulnerability by passing specially crafted tensor dimensions or parameters to the max pooling function, causing the application to become unresponsive or crash.

*Disputed* A vulnerability, which was classified as problematic, was found in PyTorch 2.6.0. Affected is the function torch.nn.functional.ctc_loss of the file aten/src/ATen/native/LossCTC.cpp. 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. The name of the patch is 46fc5d8e360127361211cb237d5f9eef0223e567. It is recommended to apply a patch to fix this issue.

PyTorch is a Python package that provides tensor computation with strong GPU acceleration and deep neural networks built on a tape-based autograd system. In version 2.5.1 and prior, a Remote Command Execution (RCE) vulnerability exists in PyTorch when loading a model using torch.load with weights_only=True. This issue has been patched in version 2.6.0.

Affected versions of the PyTorch package are vulnerable to Denial of Service (DoS) due to improper handling in the MKLDNN pooling implementation. The torch.mkldnn_max_pool2d function fails to properly validate input parameters, allowing crafted inputs to trigger resource exhaustion or crashes in the underlying MKLDNN library. An attacker with local access can exploit this vulnerability by passing specially crafted tensor dimensions or parameters to the max pooling function, causing the application to become unresponsive or crash.

*Disputed* A vulnerability, which was classified as problematic, was found in PyTorch 2.6.0. Affected is the function torch.nn.functional.ctc_loss of the file aten/src/ATen/native/LossCTC.cpp. 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. The name of the patch is 46fc5d8e360127361211cb237d5f9eef0223e567. It is recommended to apply a patch to fix this issue.

PyTorch is a Python package that provides tensor computation with strong GPU acceleration and deep neural networks built on a tape-based autograd system. In version 2.5.1 and prior, a Remote Command Execution (RCE) vulnerability exists in PyTorch when loading a model using torch.load with weights_only=True. This issue has been patched in version 2.6.0.

Affected versions of the PyTorch package are vulnerable to Denial of Service (DoS) due to improper handling in the MKLDNN pooling implementation. The torch.mkldnn_max_pool2d function fails to properly validate input parameters, allowing crafted inputs to trigger resource exhaustion or crashes in the underlying MKLDNN library. An attacker with local access can exploit this vulnerability by passing specially crafted tensor dimensions or parameters to the max pooling function, causing the application to become unresponsive or crash.

*Disputed* A vulnerability, which was classified as problematic, was found in PyTorch 2.6.0. Affected is the function torch.nn.functional.ctc_loss of the file aten/src/ATen/native/LossCTC.cpp. 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. The name of the patch is 46fc5d8e360127361211cb237d5f9eef0223e567. It is recommended to apply a patch to fix this issue.