CVE-2025-46153
Sept. 26, 2025, 2:32 p.m.
5.3
Medium
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.
Product(s) Impacted
| Vendor | Product | Versions |
|---|---|---|
| Pytorch |
|
|
Weaknesses
Common security weaknesses mapped to this vulnerability.
CWE-1176
Inefficient CPU Computation
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.
*CPE(s)
Affected systems and software identified for this CVE.
| Type | Vendor | Product | Version | Update | Edition | Language | Software Edition | Target Software | Target Hardware | Other Information |
|---|---|---|---|---|---|---|---|---|---|---|
| a | pytorch | pytorch | <3.7.0 | / | / | / | / | / | / | / |
References
Tags
CVSS Score
CVSS Data - 3.1
- Attack Vector: NETWORK
- Attack Complexity: LOW
- Privileges Required: NONE
- Scope: UNCHANGED
- Confidentiality Impact: LOW
- Integrity Impact: NONE
- Availability Impact: NONE
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N
Timeline
Published: Sept. 25, 2025, 3:16 p.m.
Last Modified: Sept. 26, 2025, 2:32 p.m.
Last Modified: Sept. 26, 2025, 2:32 p.m.
Status : Undergoing Analysis
CVE has been recently published to the CVE List and has been received by the NVD.
More infoSource
cve@mitre.org
*Disclaimer: Some vulnerabilities do not have an associated CPE. To enhance the data, we use AI to infer CPEs based on CVE details. This is an automated process and might not always be accurate.