CVE-2025-23264: Nvidia Megatron-lm RCE Vulnerability

CVE-2025-23264 Overview

CVE-2025-23264 is a code injection vulnerability in NVIDIA Megatron-LM, a large-scale distributed training framework for transformer-based models. The vulnerability exists 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.

Critical Impact

This vulnerability allows attackers with local access to inject and execute arbitrary code through malicious file processing, potentially compromising AI/ML training pipelines and sensitive model data.

Affected Products

• NVIDIA Megatron-LM (all platforms)
• AI/ML training environments utilizing Megatron-LM
• Distributed training infrastructure with Megatron-LM deployments

Discovery Timeline

• June 24, 2025 - CVE-2025-23264 published to NVD
• October 1, 2025 - Last updated in NVD database

Technical Details for CVE-2025-23264

Vulnerability Analysis

This vulnerability is classified as CWE-94 (Improper Control of Generation of Code), commonly known as code injection. The flaw resides in a Python component within Megatron-LM that processes files without adequate input validation or sanitization. When a user processes a specially crafted malicious file, the vulnerable component may interpret portions of the file content as executable code, allowing an attacker to inject and execute arbitrary Python code within the context of the running application.

The local attack vector means an attacker requires some level of access to the target system or the ability to place malicious files where they will be processed by Megatron-LM. Once the malicious file is processed, the injected code executes with the privileges of the user running the Megatron-LM process, potentially allowing full compromise of the training environment.

Root Cause

The root cause of this vulnerability is improper control of code generation in a Python component. The vulnerable code path fails to properly validate and sanitize file contents before processing, allowing specially crafted input to be interpreted and executed as code. This type of vulnerability often occurs when:

• Dynamic code evaluation functions (such as eval(), exec(), or similar constructs) are used with untrusted input
• File deserialization mechanisms (such as pickle.load()) process untrusted data
• Template engines or code generation utilities lack proper input escaping

Attack Vector

The attack requires local access to the system where Megatron-LM is running. An attacker must be able to provide a malicious file that will be processed by the vulnerable Python component. This could occur through:

• Direct file system access to place malicious files in directories processed by Megatron-LM
• Social engineering to convince a user to process a malicious file
• Compromise of upstream data sources or model checkpoints
• Supply chain attacks through shared training datasets or configurations

The vulnerability does not require user interaction beyond normal file processing operations, and can be exploited by users with low privileges on the system.

Detection Methods for CVE-2025-23264

Indicators of Compromise

• Unexpected Python subprocess spawning from Megatron-LM processes
• Unusual file access patterns or attempts to read sensitive system files
• Network connections initiated by training processes to unexpected destinations
• Modification of training scripts, configurations, or model checkpoints
• Anomalous process behavior or privilege escalation attempts from ML workloads

Detection Strategies

• Monitor file integrity for Megatron-LM installation directories and configuration files
• Implement application-level logging to track file processing operations within Megatron-LM
• Deploy endpoint detection to identify suspicious Python code execution patterns
• Use behavioral analysis to detect anomalous activity from training processes
• Audit access to training data directories and model checkpoint storage

Monitoring Recommendations

• Enable comprehensive logging for all file processing operations in Megatron-LM environments
• Implement real-time monitoring of process execution chains from Python interpreters
• Configure alerts for unauthorized file modifications in training infrastructure
• Monitor for unusual outbound network traffic from ML training nodes

How to Mitigate CVE-2025-23264

Immediate Actions Required

• Review and apply the latest security updates from NVIDIA for Megatron-LM
• Audit all file sources processed by Megatron-LM deployments for potential tampering
• Restrict file system permissions to limit which files can be processed by Megatron-LM
• Implement network segmentation to isolate ML training environments
• Review access controls for users and processes that interact with Megatron-LM

Patch Information

NVIDIA has released a security advisory addressing this vulnerability. Organizations should consult the NVIDIA Support Article for detailed patch information and remediation guidance. Apply the recommended updates as soon as possible to mitigate the risk of exploitation.

Workarounds

• Implement strict input validation for all files processed by Megatron-LM
• Run Megatron-LM processes with minimal required privileges using principle of least privilege
• Use containerization or sandboxing to isolate training workloads from sensitive systems
• Verify the integrity of all input files using cryptographic checksums before processing
• Consider implementing application-level security controls to prevent dynamic code execution on untrusted input

# Example security configuration recommendations
# Restrict file permissions for Megatron-LM directories
chmod -R 750 /path/to/megatron-lm
chown -R mluser:mlgroup /path/to/megatron-lm

# Run training processes with reduced privileges
sudo -u restricted_ml_user python train.py

# Enable Python warnings for dangerous operations
export PYTHONWARNINGS="default"