CVE-2025-2953 — MEDIUM (CVSS 5.5) AI Security Vulnerability

CISO Take

Low-priority patching item for teams running PyTorch locally. This is a local-only DoS requiring existing low-privileged access, making it an insider threat or post-exploitation primitive rather than a remote attack surface. Update to torch 2.7.1-rc1 when stable; no emergency response required unless your ML workstations are shared or multi-tenant.

Risk Assessment

Risk is LOW in most enterprise AI deployments. CVSS 5.5 with local attack vector means an adversary must already have a foothold on the target machine. EPSS of 0.00151 confirms negligible exploitation likelihood. The vulnerability's existence is disputed by maintainers, and PyTorch's own security policy frames it as expected behavior when executing untrusted model code. Elevated risk only in multi-tenant ML training environments or shared GPU clusters where workload isolation is weak.

Affected Systems

Package	Ecosystem	Vulnerable Range	Patched
pytorch	pip	—	No patch
99.6K OpenSSF 6.4 21.7K dependents Pushed 6d ago 8% patched ~142d to patch Full package profile →
torch	pip	< 2.7.1-rc1	`2.7.1-rc1`
99.6K OpenSSF 6.4 21.7K dependents Pushed 6d ago 8% patched ~142d to patch Full package profile →

Severity & Risk

CVSS 3.1

5.5 / 10

EPSS

0.1%

chance of exploitation in 30 days

Higher than 18% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Trivial

Exploitation Confidence

medium

○ CISA SSVC: Public PoC

○ Public PoC indexed (trickest/cve)

Composite signal derived from CISA KEV, CISA SSVC, EPSS, trickest/cve, and Nuclei templates.

Attack Surface

AV Local

AC Low

PR Low

UI None

S Unchanged

C None

I None

A High

Recommended Action

5 steps

Patch: upgrade to torch>=2.7.1-rc1 when available in stable channel.
Immediate workaround: disable MKLDNN via TORCH_BACKENDS_MKLDNN_ENABLED=0 or torch.backends.mkldnn.enabled=False if MKLDNN acceleration is not required.
Model provenance: enforce signed/verified model artifacts per PyTorch security policy — never execute models from untrusted sources.
Isolation: run inference and training jobs in containers or VMs with resource limits (cgroups) to bound DoS blast radius.
Detection: monitor for abnormal process termination or OOM events in PyTorch worker processes.

CISA SSVC Assessment

Decision Track*

Exploitation poc

Automatable No

Technical Impact partial

Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.

Classification

DoS Supply Chain Framework Inference Model AML.T0010.001 - AI Software AML.T0011.000 - Unsafe AI Artifacts AML.T0029 - Denial of AI Service

Compliance Impact

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, robustness and cybersecurity

ISO 42001

A.6.2 - AI system robustness and resilience

NIST AI RMF

MANAGE 4.1 - Risk Treatment — Residual Risk Response

Frequently Asked Questions

What is CVE-2025-2953?

Low-priority patching item for teams running PyTorch locally. This is a local-only DoS requiring existing low-privileged access, making it an insider threat or post-exploitation primitive rather than a remote attack surface. Update to torch 2.7.1-rc1 when stable; no emergency response required unless your ML workstations are shared or multi-tenant.

Is CVE-2025-2953 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2025-2953, increasing the risk of exploitation.

How to fix CVE-2025-2953?

1. Patch: upgrade to torch>=2.7.1-rc1 when available in stable channel. 2. Immediate workaround: disable MKLDNN via TORCH_BACKENDS_MKLDNN_ENABLED=0 or torch.backends.mkldnn.enabled=False if MKLDNN acceleration is not required. 3. Model provenance: enforce signed/verified model artifacts per PyTorch security policy — never execute models from untrusted sources. 4. Isolation: run inference and training jobs in containers or VMs with resource limits (cgroups) to bound DoS blast radius. 5. Detection: monitor for abnormal process termination or OOM events in PyTorch worker processes.

What systems are affected by CVE-2025-2953?

This vulnerability affects the following AI/ML architecture patterns: training pipelines, model serving, batch inference.

What is the CVSS score for CVE-2025-2953?

CVE-2025-2953 has a CVSS v3.1 base score of 5.5 (MEDIUM). The EPSS exploitation probability is 0.06%.

Technical Details

NVD Description

A vulnerability, which was classified as problematic, has been found in PyTorch 2.6.0+cu124. Affected by this issue is the function torch.mkldnn_max_pool2d. The manipulation leads to denial of service. An attack has to be approached locally. The exploit has been disclosed to the public and may be used. The real existence of this vulnerability is still doubted at the moment. The security policy of the project warns to use unknown models which might establish malicious effects.

Exploitation Scenario

An adversary with access to a shared ML training cluster (e.g., a data scientist with low-privileged shell access) crafts a PyTorch model or script that invokes torch.mkldnn_max_pool2d with malformed tensor dimensions or invalid parameters, triggering improper resource release and crashing the target process. In a multi-tenant GPU cluster, this could disrupt co-located training jobs. Alternatively, a supply chain scenario: a threat actor embeds malicious tensor operations in a publicly published model checkpoint on Hugging Face; a victim downloads and runs it locally, triggering the DoS during model initialization or forward pass.