Total
13 CVE
| CVE | Vendors | Products | Updated | CVSS v2 | CVSS v3 |
|---|---|---|---|---|---|
| CVE-2024-31580 | 1 Linuxfoundation | 1 Pytorch | 2025-06-10 | N/A | 4.0 MEDIUM |
| 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. | |||||
| CVE-2024-31583 | 1 Linuxfoundation | 1 Pytorch | 2025-06-10 | N/A | 7.8 HIGH |
| Pytorch before version v2.2.0 was discovered to contain a use-after-free vulnerability in torch/csrc/jit/mobile/interpreter.cpp. | |||||
| CVE-2024-31584 | 1 Linuxfoundation | 1 Pytorch | 2025-06-03 | N/A | 5.5 MEDIUM |
| Pytorch before v2.2.0 has an Out-of-bounds Read vulnerability via the component torch/csrc/jit/mobile/flatbuffer_loader.cpp. | |||||
| CVE-2025-2998 | 1 Linuxfoundation | 1 Pytorch | 2025-05-29 | 4.3 MEDIUM | 5.3 MEDIUM |
| A vulnerability was found in PyTorch 2.6.0. It has been declared as critical. Affected by this vulnerability is the function torch.nn.utils.rnn.pad_packed_sequence. The manipulation leads to memory corruption. Local access is required to approach this attack. The exploit has been disclosed to the public and may be used. | |||||
| CVE-2025-2999 | 1 Linuxfoundation | 1 Pytorch | 2025-05-29 | 4.3 MEDIUM | 5.3 MEDIUM |
| A vulnerability was found in PyTorch 2.6.0. It has been rated as critical. Affected by this issue is the function torch.nn.utils.rnn.unpack_sequence. The manipulation leads to memory corruption. Attacking locally is a requirement. The exploit has been disclosed to the public and may be used. | |||||
| CVE-2025-3000 | 1 Linuxfoundation | 1 Pytorch | 2025-05-29 | 4.3 MEDIUM | 5.3 MEDIUM |
| A vulnerability classified as critical has been found in PyTorch 2.6.0. This affects the function torch.jit.script. The manipulation leads to memory corruption. It is possible to launch the attack on the local host. The exploit has been disclosed to the public and may be used. | |||||
| CVE-2025-3001 | 1 Linuxfoundation | 1 Pytorch | 2025-05-29 | 4.3 MEDIUM | 5.3 MEDIUM |
| A vulnerability classified as critical was found in PyTorch 2.6.0. This vulnerability affects the function torch.lstm_cell. The manipulation leads to memory corruption. The attack needs to be approached locally. The exploit has been disclosed to the public and may be used. | |||||
| CVE-2025-3730 | 1 Linuxfoundation | 1 Pytorch | 2025-05-28 | 1.7 LOW | 3.3 LOW |
| 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 real existence of this vulnerability is still doubted at the moment. The name of the patch is 46fc5d8e360127361211cb237d5f9eef0223e567. It is recommended to apply a patch to fix this issue. The security policy of the project warns to use unknown models which might establish malicious effects. | |||||
| CVE-2025-3136 | 1 Linuxfoundation | 1 Pytorch | 2025-05-28 | 1.7 LOW | 3.3 LOW |
| A vulnerability, which was classified as problematic, has been found in PyTorch 2.6.0. This issue affects the function torch.cuda.memory.caching_allocator_delete of the file c10/cuda/CUDACachingAllocator.cpp. The manipulation leads to memory corruption. An attack has to be approached locally. The exploit has been disclosed to the public and may be used. | |||||
| CVE-2025-32434 | 1 Linuxfoundation | 1 Pytorch | 2025-05-28 | N/A | 9.8 CRITICAL |
| 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. | |||||
| CVE-2025-3121 | 1 Linuxfoundation | 1 Pytorch | 2025-05-27 | 1.7 LOW | 3.3 LOW |
| A vulnerability classified as problematic has been found in PyTorch 2.6.0. Affected is the function torch.jit.jit_module_from_flatbuffer. The manipulation leads to memory corruption. Local access is required to approach this attack. The exploit has been disclosed to the public and may be used. | |||||
| CVE-2022-45907 | 1 Linuxfoundation | 1 Pytorch | 2025-04-25 | N/A | 9.8 CRITICAL |
| In PyTorch before trunk/89695, torch.jit.annotations.parse_type_line can cause arbitrary code execution because eval is used unsafely. | |||||
| CVE-2025-2953 | 1 Linuxfoundation | 1 Pytorch | 2025-04-22 | 1.7 LOW | 3.3 LOW |
| 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. The real existence of this vulnerability is still doubted at the moment. The security policy of the project warns to use unknown models which might establish malicious effects. | |||||