EUVD-2025-19045CVSS Vector
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
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3Description
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.
Analysis
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 Context
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.
Affected Products
NVIDIA Megatron-LM (all versions, all platforms). The vulnerability affects: (1) the core Megatron-LM Python package across Linux, Windows, and other supported operating systems, (2) any deployment using Megatron-LM for large language model training or inference, (3) distributed training setups where multiple nodes process Megatron-LM files. Specific affected components include model loading routines, checkpoint deserialization functions, and configuration file parsing. Users running Megatron-LM versions prior to the patch release should assume they are vulnerable. Cloud platforms (AWS, Azure, GCP) offering Megatron-LM through container images or integrated ML services may have propagated this vulnerability.
Remediation
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.
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