CVE-2025-2953
mediumPriorität · verfolgen · 22EPSS 0.003 · 18. PerzentilDEEUUSEPSS · bewertet 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.
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Technischer Schweregrad, 0–10. Eine Zeile je Quelle.
Basiswert 0–10 · höher = schwerwiegender · Vektor überfahren zum Auflösen⚠ Quellen uneinig · low–medium · 1.1–5.5
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betroffene Produkte (6)
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- 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.
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Referenzen (19)
Externe Belege — Patches, Meldungen, Analysen.
- 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
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