CVE-2025-33248: Nvidia Megatron-lm RCE Vulnerability

CVE-2025-33248 Overview

CVE-2025-33248 is an insecure deserialization vulnerability affecting NVIDIA Megatron-LM, a powerful framework for training large language models. The vulnerability exists in the hybrid conversion script where an attacker can achieve remote code execution by convincing a user to load a maliciously crafted file. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data tampering.

Critical Impact

Successful exploitation enables arbitrary code execution with the privileges of the user running Megatron-LM, potentially compromising AI/ML training infrastructure and sensitive model data.

Affected Products

• NVIDIA Megatron-LM (all versions prior to patch)

Discovery Timeline

• 2026-03-24 - CVE-2025-33248 published to NVD
• 2026-03-25 - Last updated in NVD database

Technical Details for CVE-2025-33248

Vulnerability Analysis

This vulnerability is classified under CWE-502 (Deserialization of Untrusted Data). The hybrid conversion script in NVIDIA Megatron-LM improperly deserializes user-supplied data from files without adequate validation. When a user loads a specially crafted malicious file, the deserialization process can instantiate arbitrary objects and execute attacker-controlled code within the context of the application.

The vulnerability requires local access and user interaction—specifically, the victim must be convinced to open or process a malicious file. Despite this requirement, the potential impact is severe, as successful exploitation grants the attacker the ability to execute arbitrary code, escalate privileges, access sensitive information, and tamper with data. In AI/ML environments where Megatron-LM is deployed for training large language models, this could compromise valuable training data, model weights, and underlying infrastructure.

Root Cause

The root cause is insecure deserialization in the hybrid conversion script component of Megatron-LM. The application deserializes data from external files without properly validating or sanitizing the input. Python's pickle module or similar serialization mechanisms, commonly used in ML frameworks for saving and loading model checkpoints, are inherently unsafe when processing untrusted data. Attackers can craft malicious serialized objects that execute arbitrary code during the deserialization process.

Attack Vector

The attack requires local access to the target system where Megatron-LM is installed. The attacker must craft a malicious file containing serialized payload objects designed to execute code when deserialized. The attacker then uses social engineering techniques to convince the victim to load this file using the hybrid conversion script. Common attack scenarios include:

1. Sharing a "model checkpoint" or "conversion file" via email, messaging, or file sharing platforms
2. Hosting malicious files on seemingly legitimate repositories or websites
3. Compromising shared storage locations where ML teams store model artifacts

When the victim executes the hybrid conversion script with the malicious file, the deserialization process triggers code execution, potentially allowing the attacker to establish persistence, exfiltrate data, or pivot to other systems in the ML infrastructure.

Detection Methods for CVE-2025-33248

Indicators of Compromise

• Unexpected processes spawned by Megatron-LM or Python processes running conversion scripts
• Unusual file access patterns or network connections originating from ML training environments
• Presence of unfamiliar serialized files (.pkl, .pt, .ckpt) in model directories
• Anomalous user activity involving the hybrid conversion script with files from untrusted sources

Detection Strategies

• Monitor execution of the Megatron-LM hybrid conversion script for unusual command-line arguments or file inputs
• Implement file integrity monitoring on model checkpoint directories to detect unauthorized modifications
• Deploy endpoint detection to identify suspicious child process creation from Python/Megatron-LM processes
• Analyze network traffic from ML training systems for unexpected outbound connections following file processing operations

Monitoring Recommendations

• Enable verbose logging for Megatron-LM operations, particularly around file loading and conversion activities
• Implement alerting for any execution of conversion scripts with files sourced from external or untrusted locations
• Utilize SentinelOne's behavioral AI to detect and block suspicious code execution patterns during deserialization operations
• Monitor for signs of privilege escalation or lateral movement following any file processing activity in ML environments

How to Mitigate CVE-2025-33248

Immediate Actions Required

• Update NVIDIA Megatron-LM to the latest patched version as indicated in the official NVIDIA security advisory
• Audit recent usage of the hybrid conversion script and review any files that have been processed from external sources
• Restrict execution of conversion scripts to trusted users and implement strict file source validation policies
• Isolate ML training environments from sensitive production systems to limit blast radius of potential compromise

Patch Information

NVIDIA has released a security patch addressing this vulnerability. Users should refer to the NVIDIA Support Article for detailed patching instructions and the latest secure version of Megatron-LM. Apply the patch immediately to all systems running affected versions of the software.

Workarounds

• Only process files from verified and trusted sources; never load model checkpoints or conversion files from unknown origins
• Implement strict access controls limiting who can execute conversion scripts in your environment
• Use containerized or sandboxed environments when processing any external files to contain potential exploitation
• Validate file integrity using cryptographic checksums before processing any model artifacts or conversion files

# Example: Restrict conversion script execution and validate files
# 1. Verify file hash before processing
sha256sum model_checkpoint.pkl
# Compare against known-good checksum from trusted source

# 2. Run conversion in isolated environment (example using Docker)
docker run --rm --network=none -v /path/to/file:/data:ro nvidia/megatron-lm python convert_script.py /data/model_checkpoint.pkl