| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| A vulnerability was found in PyTorch 2.6.0+cu124. It has been declared as critical. Affected by this vulnerability is the function torch.ops.profiler._call_end_callbacks_on_jit_fut of the component Tuple Handler. The manipulation of the argument None leads to memory corruption. The attack can be launched remotely. The complexity of an attack is rather high. The exploitation appears to be difficult. |
| A vulnerability was found in PyTorch 2.6.0+cu124. It has been rated as problematic. Affected by this issue is the function nnq_Sigmoid of the component Quantized Sigmoid Module. The manipulation of the argument scale/zero_point leads to improper initialization. The attack needs to be approached locally. The complexity of an attack is rather high. The exploitation is known to be difficult. The exploit has been disclosed to the public and may be used. |
| PyTorch is a Python package that provides tensor computation. Prior to version 2.10.0, a vulnerability in PyTorch's `weights_only` unpickler allows an attacker to craft a malicious checkpoint file (`.pth`) that, when loaded with `torch.load(..., weights_only=True)`, can corrupt memory and potentially lead to arbitrary code execution. Version 2.10.0 fixes the issue. |
| An issue was discovered in PyTorch v2.5 and v2.7.1. Omission of profiler.stop() can cause torch.profiler.profile (PythonTracer) to crash or hang during finalization, leading to a Denial of Service (DoS). |
| 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. |
| An issue in pytorch v2.7.0 can lead to a Denial of Service (DoS) when a PyTorch model consists of torch.Tensor.to_sparse() and torch.Tensor.to_dense() and is compiled by Inductor. |
| pytorch v2.8.0 was discovered to display unexpected behavior when the components torch.rot90 and torch.randn_like are used together. |
| A syntax error in the component proxy_tensor.py of pytorch v2.7.0 allows attackers to cause a Denial of Service (DoS). |
| pytorch v2.8.0 was discovered to contain an integer overflow in the component torch.nan_to_num-.long(). |
| 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). |
| A buffer overflow occurs in pytorch v2.7.0 when a PyTorch model consists of torch.nn.Conv2d, torch.nn.functional.hardshrink, and torch.Tensor.view-torch.mv() and is compiled by Inductor, leading to a Denial of Service (DoS). |
| In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistance(p=2) produces incorrect results. |
| In PyTorch before 2.7.0, when inductor is used, nn.Fold has an assertion error. |
| In PyTorch before 2.7.0, when torch.compile is used, FractionalMaxPool2d has inconsistent results. |
| In PyTorch before 2.7.0, bitwise_right_shift produces incorrect output for certain out-of-bounds values of the "other" argument. |
| PyTorch before 3.7.0 has a bernoulli_p decompose function in decompositions.py even though it lacks full consistency with the eager CPU implementation, negatively affecting nn.Dropout1d, nn.Dropout2d, and nn.Dropout3d for fallback_random=True. |
| 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. |
| In PyTorch <=2.4.1, the RemoteModule has Deserialization RCE. NOTE: this is disputed by multiple parties because this is intended behavior in PyTorch distributed computing. |
| PyTorch before v2.2.0 was discovered to contain a heap buffer overflow vulnerability in the component /runtime/vararg_functions.cpp. This vulnerability allows attackers to cause a Denial of Service (DoS) via a crafted input. |
| Pytorch before version v2.2.0 was discovered to contain a use-after-free vulnerability in torch/csrc/jit/mobile/interpreter.cpp. |
