PyTorch up to 3.6.x bernoulli_p Decompose decompositions.py random values

CVSS Meta Temp Score
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CTI Interest Score
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2.5$0-$5k0.00

Summaryinfo

A vulnerability, which was classified as problematic, was found in PyTorch up to 3.6.x. This affects an unknown part of the file decompositions.py of the component bernoulli_p Decompose. The manipulation results in random values. This vulnerability is identified as CVE-2025-46153. There is not any exploit available. You should upgrade the affected component.

Detailsinfo

A vulnerability, which was classified as problematic, was found in PyTorch up to 3.6.x. Affected is some unknown functionality of the file decompositions.py of the component bernoulli_p Decompose. The manipulation with an unknown input leads to a random values vulnerability. CWE is classifying the issue as CWE-330. The product uses insufficiently random numbers or values in a security context that depends on unpredictable numbers. This is going to have an impact on confidentiality. CVE summarizes:

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.

The advisory is available at github.com. This vulnerability is traded as CVE-2025-46153 since 04/22/2025. The exploitability is told to be difficult. Technical details are known, but there is no available exploit. This vulnerability is assigned to T1600.001 by the MITRE ATT&CK project.

The vulnerability scanner Nessus provides a plugin with the ID 266100 (Linux Distros Unpatched Vulnerability : CVE-2025-46153), which helps to determine the existence of the flaw in a target environment.

Upgrading to version 3.7.0 eliminates this vulnerability. The upgrade is hosted for download at github.com. Applying a patch is able to eliminate this problem. The bugfix is ready for download at github.com. The best possible mitigation is suggested to be upgrading to the latest version.

The vulnerability is also documented in the databases at Tenable (266100) and CERT Bund (WID-SEC-2025-2139). If you want to get best quality of vulnerability data, you may have to visit VulDB.

Affected

  • Open Source PyTorch

Productinfo

Name

Version

Website

CPE 2.3info

CPE 2.2info

CVSSv4info

VulDB Vector: 🔒
VulDB Reliability: 🔍

CVSSv3info

VulDB Meta Base Score: 2.6
VulDB Meta Temp Score: 2.5

VulDB Base Score: 2.6
VulDB Temp Score: 2.5
VulDB Vector: 🔒
VulDB Reliability: 🔍

CVSSv2info

AVACAuCIA
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VectorComplexityAuthenticationConfidentialityIntegrityAvailability
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VulDB Base Score: 🔒
VulDB Temp Score: 🔒
VulDB Reliability: 🔍

Exploitinginfo

Class: Random values
CWE: CWE-330 / CWE-310
CAPEC: 🔒
ATT&CK: 🔒

Physical: No
Local: No
Remote: Partially

Availability: 🔒
Status: Not defined

EPSS Score: 🔒
EPSS Percentile: 🔒

Price Prediction: 🔍
Current Price Estimation: 🔒

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Nessus ID: 266100
Nessus Name: Linux Distros Unpatched Vulnerability : CVE-2025-46153

Threat Intelligenceinfo

Interest: 🔍
Active Actors: 🔍
Active APT Groups: 🔍

Countermeasuresinfo

Recommended: Upgrade
Status: 🔍

0-Day Time: 🔒

Upgrade: PyTorch 3.7.0
Patch: github.com

Timelineinfo

04/22/2025 CVE reserved
09/25/2025 +156 days Advisory disclosed
09/25/2025 +0 days VulDB entry created
09/30/2025 +5 days VulDB entry last update

Sourcesinfo

Product: github.com

Advisory: 142853
Status: Confirmed

CVE: CVE-2025-46153 (🔒)
GCVE (CVE): GCVE-0-2025-46153
GCVE (VulDB): GCVE-100-325866
CERT Bund: WID-SEC-2025-2139 - PyTorch: Mehrere Schwachstellen

Entryinfo

Created: 09/25/2025 17:39
Updated: 09/30/2025 10:11
Changes: 09/25/2025 17:39 (58), 09/27/2025 06:14 (7), 09/30/2025 10:11 (2)
Complete: 🔍
Cache ID: 216::103

If you want to get best quality of vulnerability data, you may have to visit VulDB.

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