CVE-2025-2953
mediumpriority · track · 22EPSS 0.003 · 18 %ileDEEUUSEPSS · scored 2026-10-27
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.
cvss
Technical severity, 0–10. One row per scoring source.
base score 0–10 · higher = more severe · hover a vector to decode⚠ sources disagree · low–medium · 1.1–5.5
ssvc
A stakeholder decision — act, attend, or track — from exploitation and impact.
weaknesses
The CWE weakness classes this flaw belongs to.
affected products (6)
Products and version ranges known to be affected.
advisories (4)
Vendor and ecosystem advisories describing this.
- WID-SEC-W-2025-0654PyTorch: Schwachstelle ermöglicht Denial of ServiceDeutsch2025-03-30csaf · cert_bundDE
Ein lokaler Angreifer kann eine Schwachstelle in PyTorch ausnutzen, um einen Denial of Service Angriff durchzuführen.
- GHSA-3749-ghw9-m3mgPyTorch susceptible to local Denial of Service2025-03-30ghsa · osvUS
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.
- BIT-pytorch-2025-2953PyTorch torch.mkldnn_max_pool2d denial of service2025-04-16vendor · osvUS
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.
- PYSEC-2025-1912025-03-30vendor · osvUS
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.
also known as (7)
Other names and identifiers for this entity.
sources
Which sources assert this record, and when.
references (19)
External citations — patches, advisories, analysis.
- https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-modelscnnvd
- https://github.com/pytorch/pytorch/issues/149274cnnvd
- https://github.com/pytorch/pytorch/issues/149274#issue-2923122269cnnvd
- https://github.com/pytorch/pytorch/releasesPatchcnnvd
- https://vuldb.com/?ctiid.302006cnnvd
- https://vuldb.com/?id.302006cnnvd
- https://vuldb.com/?submit.521279cnnvd
- https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-modelsenisa_euvd
Attackers weaponise vulnerabilities like this against internet-exposed services. See what is exposed on your perimeter with a free passive scan.
Free scan