CVE-2025-3136 — LOW (CVSS 3.3) AI Security Vulnerability

CISO Take

CVE-2025-3136 is a low-severity memory corruption bug in PyTorch 2.6.0's CUDA allocator, exploitable only by local authenticated users with no data exposure risk. Impact is limited to availability—crashes or instability in GPU-accelerated workloads. Schedule a routine upgrade to a patched PyTorch release; no emergency response is warranted.

Risk Assessment

Low risk overall. The local-only attack vector (AV:L) with low-privilege requirement (PR:L) significantly constrains the attack surface—remote exploitation is not possible. CVSS impact is restricted to availability (C:N/I:N/A:L), meaning no data leakage or integrity compromise. Risk escalates slightly in multi-tenant GPU clusters or shared ML training environments where untrusted users may hold local system access. Public exploit disclosure warrants inclusion in the next scheduled maintenance window, not emergency patching.

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 →

Do you use pytorch? You're affected.

Severity & Risk

CVSS 3.1

3.3 / 10

EPSS

0.1%

chance of exploitation in 30 days

Higher than 35% 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 Low

Recommended Action

5 steps

Upgrade PyTorch beyond 2.6.0 once a patched release is available; monitor GitHub issue #149821 and official PyTorch releases for patch confirmation.
In multi-tenant GPU environments, enforce strict workload isolation—separate Kubernetes namespaces, CUDA MIG partitioning where supported.
Restrict local shell access to GPU training hosts to authorized personnel only; enforce least-privilege policies.
Monitor for anomalous PyTorch process crashes, unexpected GPU OOM errors, or training job failures as potential exploitation indicators.
If immediate upgrade is not feasible, avoid executing untrusted or third-party code on shared GPU training infrastructure.

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 Framework Inference AML.T0010.001 - AI Software 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.6.2.3 - AI system security and resilience

NIST AI RMF

MANAGE 2.2 - Mechanisms are in place to inventory AI risks

Frequently Asked Questions

What is CVE-2025-3136?

CVE-2025-3136 is a low-severity memory corruption bug in PyTorch 2.6.0's CUDA allocator, exploitable only by local authenticated users with no data exposure risk. Impact is limited to availability—crashes or instability in GPU-accelerated workloads. Schedule a routine upgrade to a patched PyTorch release; no emergency response is warranted.

Is CVE-2025-3136 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2025-3136, increasing the risk of exploitation.

How to fix CVE-2025-3136?

1. Upgrade PyTorch beyond 2.6.0 once a patched release is available; monitor GitHub issue #149821 and official PyTorch releases for patch confirmation. 2. In multi-tenant GPU environments, enforce strict workload isolation—separate Kubernetes namespaces, CUDA MIG partitioning where supported. 3. Restrict local shell access to GPU training hosts to authorized personnel only; enforce least-privilege policies. 4. Monitor for anomalous PyTorch process crashes, unexpected GPU OOM errors, or training job failures as potential exploitation indicators. 5. If immediate upgrade is not feasible, avoid executing untrusted or third-party code on shared GPU training infrastructure.

What systems are affected by CVE-2025-3136?

This vulnerability affects the following AI/ML architecture patterns: training pipelines, model serving, GPU inference, multi-tenant ML platforms.

What is the CVSS score for CVE-2025-3136?

CVE-2025-3136 has a CVSS v3.1 base score of 3.3 (LOW). The EPSS exploitation probability is 0.15%.

Technical Details

NVD Description

A vulnerability, which was classified as problematic, has been found in PyTorch 2.6.0. This issue affects the function torch.cuda.memory.caching_allocator_delete of the file c10/cuda/CUDACachingAllocator.cpp. The manipulation leads to memory corruption. An attack has to be approached locally. The exploit has been disclosed to the public and may be used.

Exploitation Scenario

A malicious insider or attacker with a compromised developer account on a shared ML training cluster calls torch.cuda.memory.caching_allocator_delete with a crafted invalid or already-freed pointer. The out-of-bounds write in CUDACachingAllocator.cpp corrupts CUDA memory allocator state, crashing the PyTorch process. In a multi-tenant environment, this could be used to repeatedly terminate a co-located training job, disrupt a GPU-based inference endpoint serving production traffic, or force costly GPU memory resets—effectively sabotaging AI workloads without needing elevated privileges.