CVE-2025-46153
PyTorch Bernoulli Decomposition Inconsistency
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.
INFO
Published Date :
Sept. 25, 2025, 3:16 p.m.
Last Modified :
Sept. 25, 2025, 3:16 p.m.
Remotely Exploit :
No
Source :
[email protected]
Affected Products
The following products are affected by CVE-2025-46153
vulnerability.
Even if cvefeed.io is aware of the exact versions of the
products
that
are
affected, the information is not represented in the table below.
No affected product recoded yet
Solution
- Update PyTorch to version 3.7.0 or later.
- Verify dropout layer behavior in your application.
- Test fallback_random=True behavior.
References to Advisories, Solutions, and Tools
Here, you will find a curated list of external links that provide in-depth
information, practical solutions, and valuable tools related to
CVE-2025-46153.
CWE - Common Weakness Enumeration
While CVE identifies
specific instances of vulnerabilities, CWE categorizes the common flaws or
weaknesses that can lead to vulnerabilities. CVE-2025-46153 is
associated with the following CWEs:
Common Attack Pattern Enumeration and Classification (CAPEC)
Common Attack Pattern Enumeration and Classification
(CAPEC)
stores attack patterns, which are descriptions of the common attributes and
approaches employed by adversaries to exploit the CVE-2025-46153
weaknesses.
We scan GitHub repositories to detect new proof-of-concept exploits. Following list is a collection of public exploits and proof-of-concepts, which have been published on GitHub (sorted by the most recently updated).
Results are limited to the first 15 repositories due to potential performance issues.
The following list is the news that have been mention
CVE-2025-46153 vulnerability anywhere in the article.
The following table lists the changes that have been made to the
CVE-2025-46153 vulnerability over time.
Vulnerability history details can be useful for understanding the evolution of a vulnerability, and for identifying the most recent changes that may impact the vulnerability's severity, exploitability, or other characteristics.
-
New CVE Received by [email protected]
Sep. 25, 2025
Action Type Old Value New Value Added 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. Added Reference https://gist.github.com/shaoyuyoung/4bcefba4004f8271e64b5185c95a248a Added Reference https://gist.github.com/shaoyuyoung/e636f2e7a306105b7e96809e2b85c28a Added Reference https://github.com/pytorch/pytorch/compare/v2.6.0...v2.7.0 Added Reference https://github.com/pytorch/pytorch/issues/142853 Added Reference https://github.com/pytorch/pytorch/pull/143460