What is the CVSS score of CVE-2025-23264?

CVE-2025-23264 has a CVSS 3.1 base score of 7.8 (High). CVSS vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H. EPSS exploitation probability: 0.0%.

Which versions of Python are affected by CVE-2025-23264?

NVIDIA Megatron-LM contains a high-severity code injection vulnerability affecting all platforms that could allow attackers to execute arbitrary code and escalate privileges through malicious file…

How to fix or mitigate CVE-2025-23264?

Within 24 hours: Inventory all systems running Megatron-LM and restrict file upload functionality where possible; notify affected teams to avoid processing untrusted files. Within 7 days: Implement network segmentation to isolate Megatron-LM instances from sensitive data and production systems; deploy input validation and file scanning on all file upload interfaces. Within 30 days: Contact NVIDIA for patch availability; establish code review procedures for any custom file handling; conduct security assessment of affected systems for signs of compromise.

Python CVE-2025-23264

EUVD-2025-19045 HIGH

Code Injection (CWE-94)

2025-06-24 psirt@nvidia.com

RCE Python Information Disclosure Megatron Lm

7.8

CVSS 3.1 · NVD

Severity by source

NVD PRIMARY

7.8 HIGH

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

Primary rating from NVD · only source for this CVE.

CVSS VectorNVD

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

Attack Vector

Local

Attack Complexity

Low

Privileges Required

Low

User Interaction

None

Scope

Unchanged

Confidentiality

High

Integrity

High

Availability

High

Lifecycle Timeline

EUVD ID Assigned

Mar 15, 2026 - 22:36 euvd

EUVD-2025-19045

Analysis Generated

Mar 15, 2026 - 22:36 vuln.today

CVE Published

Jun 24, 2025 - 16:15 nvd

HIGH 7.8

DescriptionCVE.org

NVIDIA Megatron-LM for all platforms contains a vulnerability in a python component where an attacker may cause a code injection issue by providing a malicious file. A successful exploit of this vulnerability may lead to Code Execution, Escalation of Privileges, Information Disclosure and Data Tampering.

AnalysisAI

CVE-2025-23264 is a code injection vulnerability in NVIDIA Megatron-LM's Python component that allows local attackers with limited privileges to execute arbitrary code through malicious file inputs. This vulnerability affects all platforms running Megatron-LM and can lead to complete system compromise including code execution, privilege escalation, information disclosure, and data tampering. The attack requires local access and user interaction is not needed, making it a significant risk for multi-tenant environments and shared compute resources.

Technical ContextAI

NVIDIA Megatron-LM is a large-scale transformer model training framework built on PyTorch. The vulnerability resides in a Python component that processes external files without proper sanitization, falling under CWE-94 (Improper Control of Generation of Code - Code Injection). The root cause is unsafe deserialization or dynamic code evaluation (likely pickle, eval(), exec(), or similar dangerous Python constructs) when loading configuration or model files. This is a common pattern in machine learning frameworks where serialization formats (pickle, YAML, JSON with eval) are used to restore model states or configurations. The vulnerability can be triggered when an attacker provides a crafted malicious file (checkpoint, configuration, or data file) that gets processed by Megatron-LM's loading routines, leading to arbitrary Python code execution in the context of the process owner.

RemediationAI

Immediate mitigation steps: (1) Update NVIDIA Megatron-LM to the patched version released for CVE-2025-23264 (specific version number should be confirmed via NVIDIA's official security advisory). (2) Restrict file write access to directories where Megatron-LM processes configuration and checkpoint files. (3) Implement strict input validation and sanitization for all model files, checkpoints, and configuration files before loading. (4) Avoid using unsafe deserialization methods (pickle, eval, exec); replace with safer alternatives (json, yaml with restricted loaders, or protobuf). (5) Run Megatron-LM in containerized or sandboxed environments with minimal privileges. (6) Monitor file integrity and access logs for unexpected modifications to model files. (7) Conduct a full audit of model repositories and datasets to identify any potentially malicious files already present. Users should consult NVIDIA's official security bulletins and GitHub repository releases for exact patched versions and complete remediation guidance.