CVE-2025-46153
Unknown Unknown - Not Provided
BaseFortify

Publication date: 2025-09-25

Last updated on: 2025-10-03

Assigner: MITRE

Description
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.
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Meta Information
Published
2025-09-25
Last Modified
2025-10-03
Generated
2026-06-16
AI Q&A
2025-09-25
EPSS Evaluated
2026-06-14
NVD
CVE-2025-46153
EUVD
EUVD-2025-31108
Affected Vendors & Products
Showing 1 associated CPE
Vendor Product Version / Range
linuxfoundation pytorch From 2.6.0 (inc) to 2.7.0 (exc)
Helpful Resources
Exploitability
CWE
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KEV
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CWE ID Description
CWE-1176 The product performs CPU computations using algorithms that are not as efficient as they could be for the needs of the developer, i.e., the computations can be optimized further.
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Impact Analysis

The impact of this vulnerability is that the dropout layers nn.Dropout1d, nn.Dropout2d, and nn.Dropout3d may not behave as expected when fallback_random is true, potentially leading to inconsistent or incorrect model behavior during training or inference in PyTorch.

Executive Summary

This vulnerability involves the bernoulli_p decompose function in PyTorch versions before 3.7.0. The function lacks full consistency with the eager CPU implementation, which negatively affects the behavior of nn.Dropout1d, nn.Dropout2d, and nn.Dropout3d when fallback_random is set to true.

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