What is the CVSS score of CVE-2025-55560?

CVE-2025-55560 has a CVSS 3.1 base score of 7.5 (High). CVSS vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H. EPSS exploitation probability: 0.1%.

Which versions of AI / ML are affected by CVE-2025-55560?

Organizations using pytorch face notable risk from CVE-2025-55560, a resource exhaustion vulnerability rated CVSS 7.5.. A patch is available.

How to fix or mitigate CVE-2025-55560?

Within 7 days: Identify all affected systems running pytorch and apply vendor patches promptly. Vendor patch is available.

AI / ML CVE-2025-55560

HIGH

Uncontrolled Resource Consumption (CWE-400)

2025-09-25 cve@mitre.org

Denial Of Service Red Hat AI / ML Pytorch

7.5

CVSS 3.1

CVSS VectorNVD

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Attack Vector

Network

Attack Complexity

Low

Privileges Required

None

User Interaction

None

Scope

Unchanged

Confidentiality

None

Integrity

None

Availability

High

Lifecycle Timeline

Analysis Generated

Mar 28, 2026 - 19:14 vuln.today

Patch released

Mar 28, 2026 - 19:14 nvd

Patch available

CVE Published

Sep 25, 2025 - 16:15 nvd

HIGH 7.5

Blast Radius

ecosystem impact

† from your stack dependencies † transitive graph · vuln.today resolves 4-path depth

212 pypi packages depend on torch (185 direct, 27 indirect)

Ecosystem-wide dependent count for version 2.7.1.

DescriptionNVD

An issue in pytorch v2.7.0 can lead to a Denial of Service (DoS) when a PyTorch model consists of torch.Tensor.to_sparse() and torch.Tensor.to_dense() and is compiled by Inductor.

AnalysisAI

An issue in pytorch v2.7.0 can lead to a Denial of Service (DoS) when a PyTorch model consists of torch.Tensor.to_sparse() and torch.Tensor.to_dense() and is compiled by Inductor. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. This Uncontrolled Resource Consumption vulnerability could allow attackers to cause denial of service by exhausting system resources.

Technical ContextAI

This vulnerability is classified as Uncontrolled Resource Consumption (CWE-400), which allows attackers to cause denial of service by exhausting system resources. An issue in pytorch v2.7.0 can lead to a Denial of Service (DoS) when a PyTorch model consists of torch.Tensor.to_sparse() and torch.Tensor.to_dense() and is compiled by Inductor. Affected products include: Linuxfoundation Pytorch.

RemediationAI

A vendor patch is available. Apply the latest security update as soon as possible. Implement rate limiting, set resource quotas, validate input sizes, use timeouts.