# torch (PyPI) vulnerabilities

Source: https://junglewise.ai/threats/technologies/torch  
Publisher: Junglewise Threat Intelligence, https://junglewise.ai/threats  
Updated: 13 November 2026, 03:07 UTC. Rebuilt every hour.  
Data: https://junglewise.ai/threats/technologies/torch.json  
License: CC BY 4.0 with a link back

Junglewise Threat Intelligence has tracked 32 vulnerabilities in torch (PyPI): 0 in the last 7 days and 0 in the last 90 days, 0 of them critical and 0 exploited in the wild. The most recent, CVE-2025-3730, was published on 7 July 2026.

## Counts

- Last 7 days: 0
- Last 30 days: 0
- Last 90 days: 0
- All time: 32 (0 critical, 0 exploited in the wild)

## About torch (PyPI)

An open-source machine learning framework that provides a wide range of algorithms and tools for deep learning.

## Latest torch (PyPI) vulnerabilities

- [CVE-2025-3730: PYSEC-2026-1970 - PyTorch Improper Resource Shutdown or Release vulnerability](https://junglewise.ai/threats/cve-2025-3730-pytorch-improper-resource-shutdown-or-release-vulnerability) (low, CVSS 3.1, published 7 Jul 2026)
- [CVE-2026-4538: PYSEC-2026-139 - A vulnerability was identified in PyTorch 2.10.0. The affected element is an unknown function of the…](https://junglewise.ai/threats/cve-2026-4538-pysec-2026-139-a-vulnerability-was-identified-in-pytorch-2-10-0) (low, CVSS 3.1, published 22 Mar 2026)
- [CVE-2026-24747: PyTorch arbitrary code execution in weights_only unpickler](https://junglewise.ai/threats/cve-2026-24747-pytorch-arbitrary-code-execution-in-weights-only-unpickler) (high, CVSS 8.8, published 27 Jan 2026)
- [CVE-2025-63396: PyTorch denial of service in torch.profiler.profile](https://junglewise.ai/threats/cve-2025-63396-pytorch-denial-of-service-in-torch-profiler-profile) (low, CVSS 3.3, published 12 Nov 2025)
- [CVE-2025-55560: PYSEC-2025-209 - An issue in pytorch v2.7.0 can lead to a Denial of Service (DoS) when a PyTorch model consists of…](https://junglewise.ai/threats/cve-2025-55560-pysec-2025-209-an-issue-in-pytorch-v2-7-0-can-lead-to-a-denial-of) (low, CVSS 3.1, published 25 Sep 2025)
- [CVE-2025-55558: PYSEC-2025-208 - A buffer overflow occurs in pytorch v2.7.0 when a PyTorch model consists of torch.nn.Conv2d…](https://junglewise.ai/threats/cve-2025-55558-pysec-2025-208-a-buffer-overflow-occurs-in-pytorch-v2-7-0-when-a) (low, CVSS 3.1, published 25 Sep 2025)
- [CVE-2025-55557: PYSEC-2025-207 - A Name Error occurs in pytorch v2.7.0 when a PyTorch model consists of torch.cummin and is compiled…](https://junglewise.ai/threats/cve-2025-55557-pysec-2025-207-a-name-error-occurs-in-pytorch-v2-7-0-when-a) (low, CVSS 3.1, published 25 Sep 2025)
- [CVE-2025-55554: PYSEC-2025-206 - pytorch v2.8.0 was discovered to contain an integer overflow in the component torch.nan_to_num-.long().](https://junglewise.ai/threats/cve-2025-55554-pysec-2025-206-pytorch-v2-8-0-was-discovered-to-contain-an) (low, CVSS 3.1, published 25 Sep 2025)
- [CVE-2025-55553: PYSEC-2025-205 - A syntax error in the component proxy_tensor.py of pytorch v2.7.0 allows attackers to cause a Denial of…](https://junglewise.ai/threats/cve-2025-55553-pysec-2025-205-a-syntax-error-in-the-component-proxy-tensor-py-of) (low, CVSS 3.1, published 25 Sep 2025)
- [CVE-2025-55552: PYSEC-2025-204 - pytorch v2.8.0 was discovered to display unexpected behavior when the components torch.rot90 and…](https://junglewise.ai/threats/cve-2025-55552-pysec-2025-204-pytorch-v2-8-0-was-discovered-to-display) (low, CVSS 3.1, published 25 Sep 2025)
- [CVE-2025-55551: PYSEC-2025-203 - An issue in the component torch.linalg.lu of pytorch v2.8.0 allows attackers to cause a Denial of…](https://junglewise.ai/threats/cve-2025-55551-pysec-2025-203-an-issue-in-the-component-torch-linalg-lu-of) (low, CVSS 3.1, published 25 Sep 2025)
- [CVE-2025-46153: PYSEC-2025-202 - PyTorch before 3.7.0 has a bernoulli_p decompose function in decompositions.py even though it lacks…](https://junglewise.ai/threats/cve-2025-46153-pysec-2025-202-pytorch-before-3-7-0-has-a-bernoulli-p-decompose) (low, CVSS 3.1, published 25 Sep 2025)
- [CVE-2025-46152: PYSEC-2025-201 - In PyTorch before 2.7.0, bitwise_right_shift produces incorrect output for certain out-of-bounds values…](https://junglewise.ai/threats/cve-2025-46152-pysec-2025-201-in-pytorch-before-2-7-0-bitwise-right-shift) (low, CVSS 3.1, published 25 Sep 2025)
- [CVE-2025-46150: PYSEC-2025-200 - In PyTorch before 2.7.0, when torch.compile is used, FractionalMaxPool2d has inconsistent results.](https://junglewise.ai/threats/cve-2025-46150-pysec-2025-200-in-pytorch-before-2-7-0-when-torch-compile-is-used) (low, CVSS 3.1, published 25 Sep 2025)
- [CVE-2025-46149: PYSEC-2025-199 - In PyTorch before 2.7.0, when inductor is used, nn.Fold has an assertion error.](https://junglewise.ai/threats/cve-2025-46149-pysec-2025-199-in-pytorch-before-2-7-0-when-inductor-is-used-nn) (low, CVSS 3.1, published 25 Sep 2025)
- [CVE-2025-46148: PYSEC-2025-198 - In PyTorch through 2.6.0, when eager is used, nn.PairwiseDistance(p=2) produces incorrect results.](https://junglewise.ai/threats/cve-2025-46148-pysec-2025-198-in-pytorch-through-2-6-0-when-eager-is-used-nn) (low, CVSS 3.1, published 25 Sep 2025)
- [CVE-2025-32434: PyTorch: `torch.load` with `weights_only=True` leads to remote code execution](https://junglewise.ai/threats/cve-2025-32434-pytorch-torch-load-with-weights-only-true-leads-to-remote-code) (medium, CVSS 4, published 18 Apr 2025)
- [CVE-2025-3136: PYSEC-2025-197 - A vulnerability, which was classified as problematic, has been found in PyTorch 2.6.0. This issue…](https://junglewise.ai/threats/cve-2025-3136-pysec-2025-197-a-vulnerability-which-was-classified-as-problematic) (medium, CVSS 4, published 3 Apr 2025)
- [CVE-2025-3121: PYSEC-2025-196 - A vulnerability classified as problematic has been found in PyTorch 2.6.0. Affected is the function…](https://junglewise.ai/threats/cve-2025-3121-pysec-2025-196-a-vulnerability-classified-as-problematic-has-been) (low, CVSS 3.1, published 2 Apr 2025)
- [CVE-2025-3001: PyTorch memory corruption in torch.lstm_cell](https://junglewise.ai/threats/cve-2025-3001-pytorch-memory-corruption-in-torch-lstm-cell) (medium, CVSS 5.3, published 31 Mar 2025)
- [CVE-2025-3000: PyTorch memory corruption in torch.jit.script](https://junglewise.ai/threats/cve-2025-3000-pytorch-memory-corruption-in-torch-jit-script) (medium, CVSS 5.3, published 31 Mar 2025)
- [CVE-2025-2998: PyTorch memory corruption in pad_packed_sequence](https://junglewise.ai/threats/cve-2025-2998-pytorch-memory-corruption-in-pad-packed-sequence) (medium, CVSS 5.3, published 31 Mar 2025)
- [CVE-2025-2999: PyTorch memory corruption in unpack_sequence and pad_packed_sequence](https://junglewise.ai/threats/cve-2025-2999-pytorch-memory-corruption-in-unpack-sequence-and-pad-packed) (medium, CVSS 5.3, published 31 Mar 2025)
- [CVE-2025-2953: PyTorch denial of service in torch.mkldnn_max_pool2d](https://junglewise.ai/threats/cve-2025-2953-pytorch-denial-of-service-in-torch-mkldnn-max-pool2d) (low, CVSS 3.3, published 30 Mar 2025)
- [CVE-2024-7804: Withdrawn Advisory: PyTorch deserialization vulnerability](https://junglewise.ai/threats/cve-2024-7804-withdrawn-advisory-pytorch-deserialization-vulnerability) (low, CVSS 3, published 20 Mar 2025)

## Most severe torch (PyPI) vulnerabilities

- [CVE-2026-24747: PyTorch arbitrary code execution in weights_only unpickler](https://junglewise.ai/threats/cve-2026-24747-pytorch-arbitrary-code-execution-in-weights-only-unpickler) (high, CVSS 8.8, published 27 Jan 2026)
- [CVE-2025-3001: PyTorch memory corruption in torch.lstm_cell](https://junglewise.ai/threats/cve-2025-3001-pytorch-memory-corruption-in-torch-lstm-cell) (medium, CVSS 5.3, published 31 Mar 2025)
- [CVE-2025-2999: PyTorch memory corruption in unpack_sequence and pad_packed_sequence](https://junglewise.ai/threats/cve-2025-2999-pytorch-memory-corruption-in-unpack-sequence-and-pad-packed) (medium, CVSS 5.3, published 31 Mar 2025)
- [CVE-2025-2998: PyTorch memory corruption in pad_packed_sequence](https://junglewise.ai/threats/cve-2025-2998-pytorch-memory-corruption-in-pad-packed-sequence) (medium, CVSS 5.3, published 31 Mar 2025)
- [CVE-2025-3000: PyTorch memory corruption in torch.jit.script](https://junglewise.ai/threats/cve-2025-3000-pytorch-memory-corruption-in-torch-jit-script) (medium, CVSS 5.3, published 31 Mar 2025)
- [CVE-2025-2148: PyTorch memory corruption in profiler Tuple Handler](https://junglewise.ai/threats/cve-2025-2148-pytorch-memory-corruption-in-profiler-tuple-handler) (medium, CVSS 5, published 10 Mar 2025)
- [CVE-2025-32434: PyTorch: `torch.load` with `weights_only=True` leads to remote code execution](https://junglewise.ai/threats/cve-2025-32434-pytorch-torch-load-with-weights-only-true-leads-to-remote-code) (medium, CVSS 4, published 18 Apr 2025)
- [CVE-2025-3136: PYSEC-2025-197 - A vulnerability, which was classified as problematic, has been found in PyTorch 2.6.0. This issue…](https://junglewise.ai/threats/cve-2025-3136-pysec-2025-197-a-vulnerability-which-was-classified-as-problematic) (medium, CVSS 4, published 3 Apr 2025)
- [CVE-2025-2149: PyTorch improper initialization in Quantized Sigmoid Module](https://junglewise.ai/threats/cve-2025-2149-pytorch-improper-initialization-in-quantized-sigmoid-module) (medium, CVSS 4, published 10 Mar 2025)
- [CVE-2025-2953: PyTorch denial of service in torch.mkldnn_max_pool2d](https://junglewise.ai/threats/cve-2025-2953-pytorch-denial-of-service-in-torch-mkldnn-max-pool2d) (low, CVSS 3.3, published 30 Mar 2025)

## Vulnerabilities per week

Nothing was published in the last eight weeks.

| Week of | Vulns | Critical | Exploited |
| --- | --- | --- | --- |
| 17 Aug 2026 | 0 | 0 | 0 |
| 24 Aug 2026 | 0 | 0 | 0 |
| 31 Aug 2026 | 0 | 0 | 0 |
| 7 Sep 2026 | 0 | 0 | 0 |
| 14 Sep 2026 | 0 | 0 | 0 |
| 21 Sep 2026 | 0 | 0 | 0 |
| 28 Sep 2026 | 0 | 0 | 0 |
| 5 Oct 2026 | 0 | 0 | 0 |
| 12 Oct 2026 | 0 | 0 | 0 |
| 19 Oct 2026 | 0 | 0 | 0 |
| 26 Oct 2026 | 0 | 0 | 0 |
| 2 Nov 2026 | 0 | 0 | 0 |
| 9 Nov 2026 | 0 | 0 | 0 |

## Other PyPI technologies

- [tensorflow (PyPI)](https://junglewise.ai/threats/technologies/pypi-tensorflow): 435 vulnerabilities
- [tensorflow-cpu (PyPI)](https://junglewise.ai/threats/technologies/tensorflow-cpu): 430 vulnerabilities
- [tensorflow-gpu (PyPI)](https://junglewise.ai/threats/technologies/tensorflow-gpu): 427 vulnerabilities
- [open-webui (PyPI)](https://junglewise.ai/threats/technologies/open-webui): 177 vulnerabilities
- [Django (PyPI)](https://junglewise.ai/threats/technologies/django): 173 vulnerabilities
- [apache-airflow (PyPI)](https://junglewise.ai/threats/technologies/apache-airflow): 159 vulnerabilities
- [pillow (PyPI)](https://junglewise.ai/threats/technologies/pillow): 125 vulnerabilities
- [praisonai (PyPI)](https://junglewise.ai/threats/technologies/pypi-praisonai): 120 vulnerabilities
- [vllm (PyPI)](https://junglewise.ai/threats/technologies/pypi-vllm): 117 vulnerabilities
- [plone (PyPI)](https://junglewise.ai/threats/technologies/pypi-plone): 101 vulnerabilities
- [mlflow (PyPI)](https://junglewise.ai/threats/technologies/mlflow): 88 vulnerabilities
- [picklescan (PyPI)](https://junglewise.ai/threats/technologies/picklescan): 86 vulnerabilities

## Sources

NVD, GitHub Security Advisories, OSV, the CISA Known Exploited Vulnerabilities catalog, FIRST EPSS and vendor advisories. Rebuilt every hour. Cite as: Junglewise Threat Intelligence, "torch (PyPI) vulnerabilities", https://junglewise.ai/threats/technologies/torch, 13 November 2026.