pytorch - Debian Package Tracker

general

source: pytorch (main)
version: 2.6.0+dfsg-9
maintainer: Debian Deep Learning Team (archive) (DMD)
uploaders: Mo Zhou [DMD] – Shengqi Chen [DMD]
arch: amd64 arm64 ppc64el s390x
std-ver: 4.7.0
VCS: Git (Browse, QA)

versions [more versions can be listed by madison] [old versions available from snapshot.debian.org]

[pool directory]

o-o-stable: 1.7.1-7
oldstable: 1.13.1+dfsg-4
stable: 2.6.0+dfsg-7
testing: 2.6.0+dfsg-9
unstable: 2.6.0+dfsg-9

versioned links

1.7.1-7: [.dsc, use dget on this link to retrieve source package] [changelog] [copyright] [rules] [control]
1.13.1+dfsg-4: [.dsc, use dget on this link to retrieve source package] [changelog] [copyright] [rules] [control]
2.6.0+dfsg-7: [.dsc, use dget on this link to retrieve source package] [changelog] [copyright] [rules] [control]
2.6.0+dfsg-9: [.dsc, use dget on this link to retrieve source package] [changelog] [copyright] [rules] [control]

binaries

libtorch-dev
libtorch-test
libtorch2.6
python3-torch (1 bugs: 0, 1, 0, 0)

action needed

Debci reports failed tests high

unstable: fail (log)
The tests ran in 0:06:20
Last run: 2025-10-15T01:20:51.000Z
Previous status: unknown

testing: pass (log)
The tests ran in 0:16:44
Last run: 2025-09-14T06:39:38.000Z
Previous status: unknown

stable: pass (log)
The tests ran in 0:27:06
Last run: 2025-08-20T06:03:23.000Z
Previous status: unknown

Created: 2025-10-15 Last update: 2025-10-16 06:05

A new upstream version is available: 2.9.0 high

A new upstream version 2.9.0 is available, you should consider packaging it.

Created: 2025-08-20 Last update: 2025-10-16 06:02

The VCS repository is not up to date, push the missing commits. high

vcswatch reports that the current version of the package is not in its VCS.
Either you need to push your commits and/or your tags, or the information about the package's VCS are out of date. A common cause of the latter issue when using the Git VCS is not specifying the correct branch when the packaging is not in the default one (remote HEAD branch), which is usually "master" but can be modified in salsa.debian.org in the project's general settings with the "Default Branch" field). Alternatively the Vcs-Git field in debian/control can contain a "-b <branch-name>" suffix to indicate what branch is used for the Debian packaging.

Created: 2025-08-23 Last update: 2025-10-15 10:32

23 security issues in sid high

There are 23 open security issues in sid.

23 important issues:

CVE-2025-2148: 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.
CVE-2025-2149: 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.
CVE-2025-2953: 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.
CVE-2025-2998: 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: 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: 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: 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-2025-3121: 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-2025-3136: 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-2025-3730: 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-2025-4287: A vulnerability was found in PyTorch 2.6.0+cu124. It has been rated as problematic. Affected by this issue is the function torch.cuda.nccl.reduce of the file torch/cuda/nccl.py. The manipulation leads to denial of service. It is possible to launch the attack on the local host. The exploit has been disclosed to the public and may be used. The patch is identified as 5827d2061dcb4acd05ac5f8e65d8693a481ba0f5. It is recommended to apply a patch to fix this issue.
CVE-2025-46148: In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistance(p=2) produces incorrect results.
CVE-2025-46149: In PyTorch before 2.7.0, when inductor is used, nn.Fold has an assertion error.
CVE-2025-46150: In PyTorch before 2.7.0, when torch.compile is used, FractionalMaxPool2d has inconsistent results.
CVE-2025-46152: In PyTorch before 2.7.0, bitwise_right_shift produces incorrect output for certain out-of-bounds values of the "other" argument.
CVE-2025-46153: 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.
CVE-2025-55551: 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.
CVE-2025-55552: pytorch v2.8.0 was discovered to display unexpected behavior when the components torch.rot90 and torch.randn_like are used together.
CVE-2025-55553: A syntax error in the component proxy_tensor.py of pytorch v2.7.0 allows attackers to cause a Denial of Service (DoS).
CVE-2025-55554: pytorch v2.8.0 was discovered to contain an integer overflow in the component torch.nan_to_num-.long().
CVE-2025-55557: 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).
CVE-2025-55558: 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).
CVE-2025-55560: 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.

Created: 2025-03-11 Last update: 2025-09-28 22:30

23 security issues in forky high

There are 23 open security issues in forky.

23 important issues:

CVE-2025-2148: 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.
CVE-2025-2149: 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.
CVE-2025-2953: 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.
CVE-2025-2998: 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: 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: 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: 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-2025-3121: 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-2025-3136: 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-2025-3730: 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-2025-4287: A vulnerability was found in PyTorch 2.6.0+cu124. It has been rated as problematic. Affected by this issue is the function torch.cuda.nccl.reduce of the file torch/cuda/nccl.py. The manipulation leads to denial of service. It is possible to launch the attack on the local host. The exploit has been disclosed to the public and may be used. The patch is identified as 5827d2061dcb4acd05ac5f8e65d8693a481ba0f5. It is recommended to apply a patch to fix this issue.
CVE-2025-46148: In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistance(p=2) produces incorrect results.
CVE-2025-46149: In PyTorch before 2.7.0, when inductor is used, nn.Fold has an assertion error.
CVE-2025-46150: In PyTorch before 2.7.0, when torch.compile is used, FractionalMaxPool2d has inconsistent results.
CVE-2025-46152: In PyTorch before 2.7.0, bitwise_right_shift produces incorrect output for certain out-of-bounds values of the "other" argument.
CVE-2025-46153: 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.
CVE-2025-55551: 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.
CVE-2025-55552: pytorch v2.8.0 was discovered to display unexpected behavior when the components torch.rot90 and torch.randn_like are used together.
CVE-2025-55553: A syntax error in the component proxy_tensor.py of pytorch v2.7.0 allows attackers to cause a Denial of Service (DoS).
CVE-2025-55554: pytorch v2.8.0 was discovered to contain an integer overflow in the component torch.nan_to_num-.long().
CVE-2025-55557: 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).
CVE-2025-55558: 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).
CVE-2025-55560: 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.

Created: 2025-08-09 Last update: 2025-09-28 22:30

27 security issues in bullseye high

There are 27 open security issues in bullseye.

14 important issues:

CVE-2025-4287: A vulnerability was found in PyTorch 2.6.0+cu124. It has been rated as problematic. Affected by this issue is the function torch.cuda.nccl.reduce of the file torch/cuda/nccl.py. The manipulation leads to denial of service. It is possible to launch the attack on the local host. The exploit has been disclosed to the public and may be used. The patch is identified as 5827d2061dcb4acd05ac5f8e65d8693a481ba0f5. It is recommended to apply a patch to fix this issue.
CVE-2025-32434: 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-2025-46148: In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistance(p=2) produces incorrect results.
CVE-2025-46149: In PyTorch before 2.7.0, when inductor is used, nn.Fold has an assertion error.
CVE-2025-46150: In PyTorch before 2.7.0, when torch.compile is used, FractionalMaxPool2d has inconsistent results.
CVE-2025-46152: In PyTorch before 2.7.0, bitwise_right_shift produces incorrect output for certain out-of-bounds values of the "other" argument.
CVE-2025-46153: 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.
CVE-2025-55551: 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.
CVE-2025-55552: pytorch v2.8.0 was discovered to display unexpected behavior when the components torch.rot90 and torch.randn_like are used together.
CVE-2025-55553: A syntax error in the component proxy_tensor.py of pytorch v2.7.0 allows attackers to cause a Denial of Service (DoS).
CVE-2025-55554: pytorch v2.8.0 was discovered to contain an integer overflow in the component torch.nan_to_num-.long().
CVE-2025-55557: 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).
CVE-2025-55558: 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).
CVE-2025-55560: 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.

13 issues postponed or untriaged:

CVE-2025-2148: (postponed; to be fixed through a stable update) 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.
CVE-2025-2149: (postponed; to be fixed through a stable update) 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.
CVE-2025-2953: (postponed; to be fixed through a stable update) 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.
CVE-2025-2998: (postponed; to be fixed through a stable update) 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: (postponed; to be fixed through a stable update) 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: (postponed; to be fixed through a stable update) 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: (postponed; to be fixed through a stable update) 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-2025-3121: (postponed; to be fixed through a stable update) 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-2025-3136: (postponed; to be fixed through a stable update) 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-2025-3730: (postponed; to be fixed through a stable update) 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-2022-45907: (needs triaging) In PyTorch before trunk/89695, torch.jit.annotations.parse_type_line can cause arbitrary code execution because eval is used unsafely.
CVE-2024-31580: (needs triaging) 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: (needs triaging) Pytorch before version v2.2.0 was discovered to contain a use-after-free vulnerability in torch/csrc/jit/mobile/interpreter.cpp.

Created: 2025-04-17 Last update: 2025-09-28 22:30

Fails to build during reproducibility testing normal

A package building reproducibly enables third parties to verify that the source matches the distributed binaries. It has been identified that this source package produced different results, failed to build or had other issues in a test environment. Please read about how to improve the situation!

Created: 2025-08-22 Last update: 2025-10-16 01:03

lintian reports 1 warning normal

Lintian reports 1 warning about this package. You should make the package lintian clean getting rid of them.

Created: 2025-09-18 Last update: 2025-09-18 21:02

23 low-priority security issues in trixie low

There are 23 open security issues in trixie.

23 issues left for the package maintainer to handle:

CVE-2025-2148: (needs triaging) 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.
CVE-2025-2149: (needs triaging) 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.
CVE-2025-2953: (needs triaging) 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.
CVE-2025-2998: (needs triaging) 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: (needs triaging) 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: (needs triaging) 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: (needs triaging) 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-2025-3121: (needs triaging) 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-2025-3136: (needs triaging) 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-2025-3730: (needs triaging) 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-2025-4287: (needs triaging) A vulnerability was found in PyTorch 2.6.0+cu124. It has been rated as problematic. Affected by this issue is the function torch.cuda.nccl.reduce of the file torch/cuda/nccl.py. The manipulation leads to denial of service. It is possible to launch the attack on the local host. The exploit has been disclosed to the public and may be used. The patch is identified as 5827d2061dcb4acd05ac5f8e65d8693a481ba0f5. It is recommended to apply a patch to fix this issue.
CVE-2025-46148: (needs triaging) In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistance(p=2) produces incorrect results.
CVE-2025-46149: (needs triaging) In PyTorch before 2.7.0, when inductor is used, nn.Fold has an assertion error.
CVE-2025-46150: (needs triaging) In PyTorch before 2.7.0, when torch.compile is used, FractionalMaxPool2d has inconsistent results.
CVE-2025-46152: (needs triaging) In PyTorch before 2.7.0, bitwise_right_shift produces incorrect output for certain out-of-bounds values of the "other" argument.
CVE-2025-46153: (needs triaging) 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.
CVE-2025-55551: (needs triaging) 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.
CVE-2025-55552: (needs triaging) pytorch v2.8.0 was discovered to display unexpected behavior when the components torch.rot90 and torch.randn_like are used together.
CVE-2025-55553: (needs triaging) A syntax error in the component proxy_tensor.py of pytorch v2.7.0 allows attackers to cause a Denial of Service (DoS).
CVE-2025-55554: (needs triaging) pytorch v2.8.0 was discovered to contain an integer overflow in the component torch.nan_to_num-.long().
CVE-2025-55557: (needs triaging) 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).
CVE-2025-55558: (needs triaging) 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).
CVE-2025-55560: (needs triaging) 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.

You can find information about how to handle these issues in the security team's documentation.

Created: 2025-03-11 Last update: 2025-09-28 22:30

27 low-priority security issues in bookworm low

There are 27 open security issues in bookworm.

24 issues left for the package maintainer to handle:

CVE-2025-2148: (needs triaging) 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.
CVE-2025-2149: (needs triaging) 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.
CVE-2025-2953: (needs triaging) 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.
CVE-2025-2998: (needs triaging) 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: (needs triaging) 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: (needs triaging) 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: (needs triaging) 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-2025-3121: (needs triaging) 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-2025-3136: (needs triaging) 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-2025-3730: (needs triaging) 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-2025-4287: (needs triaging) A vulnerability was found in PyTorch 2.6.0+cu124. It has been rated as problematic. Affected by this issue is the function torch.cuda.nccl.reduce of the file torch/cuda/nccl.py. The manipulation leads to denial of service. It is possible to launch the attack on the local host. The exploit has been disclosed to the public and may be used. The patch is identified as 5827d2061dcb4acd05ac5f8e65d8693a481ba0f5. It is recommended to apply a patch to fix this issue.
CVE-2025-32434: (needs triaging) 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-2025-46148: (needs triaging) In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistance(p=2) produces incorrect results.
CVE-2025-46149: (needs triaging) In PyTorch before 2.7.0, when inductor is used, nn.Fold has an assertion error.
CVE-2025-46150: (needs triaging) In PyTorch before 2.7.0, when torch.compile is used, FractionalMaxPool2d has inconsistent results.
CVE-2025-46152: (needs triaging) In PyTorch before 2.7.0, bitwise_right_shift produces incorrect output for certain out-of-bounds values of the "other" argument.
CVE-2025-46153: (needs triaging) 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.
CVE-2025-55551: (needs triaging) 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.
CVE-2025-55552: (needs triaging) pytorch v2.8.0 was discovered to display unexpected behavior when the components torch.rot90 and torch.randn_like are used together.
CVE-2025-55553: (needs triaging) A syntax error in the component proxy_tensor.py of pytorch v2.7.0 allows attackers to cause a Denial of Service (DoS).
CVE-2025-55554: (needs triaging) pytorch v2.8.0 was discovered to contain an integer overflow in the component torch.nan_to_num-.long().
CVE-2025-55557: (needs triaging) 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).
CVE-2025-55558: (needs triaging) 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).
CVE-2025-55560: (needs triaging) 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.

You can find information about how to handle these issues in the security team's documentation.

3 ignored issues:

CVE-2024-31580: 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: 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: Pytorch before v2.2.0 has an Out-of-bounds Read vulnerability via the component torch/csrc/jit/mobile/flatbuffer_loader.cpp.

Created: 2024-04-18 Last update: 2025-09-28 22:30

debian/patches: 6 patches to forward upstream low

Among the 19 debian patches available in version 2.6.0+dfsg-9 of the package, we noticed the following issues:

6 patches where the metadata indicates that the patch has not yet been forwarded upstream. You should either forward the patch upstream or update the metadata to document its real status.

Created: 2023-02-26 Last update: 2025-09-18 23:02

Standards version of the package is outdated. wishlist

The package should be updated to follow the last version of Debian Policy (Standards-Version 4.7.2 instead of 4.7.0).

Created: 2025-02-21 Last update: 2025-09-18 16:00

testing migrations

This package will soon be part of the auto-cpp-httplib transition. You might want to ensure that your package is ready for it. You can probably find supplementary information in the debian-release archives or in the corresponding release.debian.org bug.
This package will soon be part of the auto-fmtlib transition. You might want to ensure that your package is ready for it. You can probably find supplementary information in the debian-release archives or in the corresponding release.debian.org bug.
This package will soon be part of the auto-protobuf transition. You might want to ensure that your package is ready for it. You can probably find supplementary information in the debian-release archives or in the corresponding release.debian.org bug.

news

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[2025-09-23] pytorch 2.6.0+dfsg-9 MIGRATED to testing (Debian testing watch)
[2025-09-18] Accepted pytorch 2.6.0+dfsg-9 (source) into unstable (Shengqi Chen)
[2025-08-21] pytorch 2.6.0+dfsg-8 MIGRATED to testing (Debian testing watch)
[2025-08-18] Accepted pytorch 2.6.0+dfsg-8 (source) into unstable (Mo Zhou)
[2025-04-21] pytorch 2.6.0+dfsg-7 MIGRATED to testing (Debian testing watch)
[2025-04-11] Accepted pytorch 2.6.0+dfsg-7 (source) into unstable (Mo Zhou)
[2025-03-16] pytorch 2.6.0+dfsg-5 MIGRATED to testing (Debian testing watch)
[2025-03-10] Accepted pytorch 2.6.0+dfsg-5 (source) into unstable (Mo Zhou)
[2025-03-06] Accepted pytorch 2.6.0+dfsg-4 (source) into unstable (Mo Zhou)
[2025-02-27] pytorch 2.6.0+dfsg-3 MIGRATED to testing (Debian testing watch)
[2025-02-24] Accepted pytorch 2.6.0+dfsg-3 (source) into unstable (Shengqi Chen)
[2025-02-21] Accepted pytorch 2.6.0+dfsg-2 (source) into unstable (Shengqi Chen)
[2025-02-17] Accepted pytorch 2.6.0+dfsg-1 (source) into unstable (Shengqi Chen)
[2025-01-30] Accepted pytorch 2.6.0+dfsg-1~exp1 (source) into experimental (Shengqi Chen)
[2025-01-28] Accepted pytorch 2.6.0~rc9+dfsg-1~exp1 (source amd64) into experimental (Debian FTP Masters) (signed by: Shengqi Chen)
[2025-01-07] pytorch 2.5.1+dfsg-4 MIGRATED to testing (Debian testing watch)
[2025-01-03] Accepted pytorch 2.5.1+dfsg-4 (source) into unstable (Mo Zhou)
[2024-12-29] pytorch 2.5.1+dfsg-3 MIGRATED to testing (Debian testing watch)
[2024-12-27] Accepted pytorch 2.5.1+dfsg-3 (source) into unstable (Mo Zhou)
[2024-11-23] Accepted pytorch 2.5.1+dfsg-1 (source) into unstable (Mo Zhou)
[2024-11-04] Accepted pytorch 2.5.0+dfsg-1 (source amd64) into unstable (Debian FTP Masters) (signed by: Mo Zhou)
[2024-10-24] Accepted pytorch 2.4.1-4 (source) into unstable (Mo Zhou)
[2024-10-23] Accepted pytorch 2.4.1-3 (source) into unstable (Mo Zhou)
[2024-10-01] Accepted pytorch 2.4.1-1 (source amd64) into unstable (Debian FTP Masters) (signed by: Mo Zhou)
[2024-04-24] Accepted pytorch 2.1.2+dfsg-4 (source) into unstable (Mo Zhou)
[2024-02-13] Accepted pytorch 2.1.2+dfsg-2 (source) into unstable (Mo Zhou)
[2024-02-12] Accepted pytorch 2.1.2+dfsg-1 (source amd64) into experimental (Debian FTP Masters) (signed by: Mo Zhou)
[2024-01-15] pytorch REMOVED from testing (Debian testing watch)
[2023-10-06] pytorch 2.0.1+dfsg-5 MIGRATED to testing (Debian testing watch)
[2023-10-01] Accepted pytorch 2.0.1+dfsg-5 (source) into unstable (Mo Zhou)

bugs [bug history graph]

all: 26
RC: 0
I&N: 24
M&W: 2
F&P: 0
patch: 0

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ubuntu

[Information about Ubuntu for Debian Developers]

version: 2.6.0+dfsg-7
3 bugs