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.
This vulnerability carries a MEDIUM severity rating with a CVSS v3.1 score of 5.3, indicating it can be exploited remotely over the network with relatively low complexity without requiring user interaction and does not require pre-existing privileges . The vulnerability impacts limited data confidentiality, for affected systems. Impacting 1 product from linuxfoundation organizations running these solutions should prioritize assessment and patching.
Reported in 2025, this vulnerability emerged during an era marked by increased sophistication in supply chain attacks, cloud infrastructure vulnerabilities, and software-as-a-service (SaaS) security challenges. Security practices during this period emphasized zero-trust architectures, container security, and API protection.
2025-09-25T15:16:12.603
2025-10-03T17:56:58.610
Analyzed
CVSSv3.1: 5.3 (MEDIUM)
| Type | Vendor | Product | Version/Range | Vulnerable? |
|---|---|---|---|---|
| Application | linuxfoundation | pytorch | < 2.7.0 | Yes |
SecUtils normalizes and enriches National Vulnerability Database (NVD) records by standardizing vendor and product identifiers, aggregating vulnerability metadata from both NVD and MITRE sources, and providing structured context for security teams. For linuxfoundation's affected products, we extract Common Platform Enumeration (CPE) data, Common Weakness Enumeration (CWE) classifications, CVSS severity metrics, and reference data to enable rapid vulnerability prioritization and asset correlation. This record contains no exploit code, proof-of-concept instructions, or attack methodologies—only defensive intelligence necessary for patch management, risk assessment, and security operations.