CVE-2025-33241: NVIDIA NeMo Framework RCE Vulnerability

CVE-2025-33241 Overview

NVIDIA NeMo Framework contains a vulnerability where an attacker could cause remote code execution by loading a maliciously crafted file. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure, and data tampering. This vulnerability is classified as CWE-502 (Desecure Deserialization), indicating that the framework improperly handles deserialization of untrusted data when loading files.

Critical Impact

Successful exploitation enables arbitrary code execution with the privileges of the affected application, potentially allowing attackers to escalate privileges, access sensitive information, or tamper with data on affected systems.

Affected Products

• NVIDIA NeMo Framework (specific versions not disclosed in advisory)

Discovery Timeline

• 2026-02-18 - CVE-2025-33241 published to NVD
• 2026-02-18 - Last updated in NVD database

Technical Details for CVE-2025-33241

Vulnerability Analysis

This vulnerability stems from insecure deserialization within the NVIDIA NeMo Framework, a popular toolkit used for building, training, and fine-tuning GPU-accelerated conversational AI and speech AI models. When the framework processes and loads certain file types, it fails to properly validate and sanitize the serialized data before deserializing it. This allows an attacker to craft a malicious file containing embedded code or object structures that, when loaded by the framework, execute arbitrary commands on the target system.

The local attack vector requires an attacker to either have access to the target system or convince a user to load a maliciously crafted file. Given that NeMo Framework is commonly used in machine learning pipelines where model files and datasets are frequently shared and loaded, this creates significant risk in collaborative environments.

Root Cause

The root cause is CWE-502: Deserialization of Untrusted Data. The NeMo Framework does not implement adequate validation when loading serialized objects from files. Python-based ML frameworks often utilize serialization libraries such as pickle or similar mechanisms for model persistence, which can execute arbitrary code during the deserialization process if the serialized data is crafted maliciously. The framework fails to implement proper integrity checks or use safe deserialization practices before processing potentially untrusted file content.

Attack Vector

The attack requires local access to the system or the ability to deliver a malicious file to a user who will load it using the NeMo Framework. An attacker would:

1. Craft a malicious file containing serialized Python objects with embedded executable code
2. Deliver the file to the target environment through file sharing, compromised repositories, or social engineering
3. Wait for or trigger the victim to load the file using NeMo Framework functions
4. Upon loading, the malicious code executes with the privileges of the running process

The vulnerability does not require user interaction beyond loading the file, and exploitation requires low privileges on the target system. The attack can result in complete compromise of confidentiality, integrity, and availability of the affected system.

Detection Methods for CVE-2025-33241

Indicators of Compromise

• Unexpected Python processes spawning child processes or network connections during model loading operations
• Unusual file access patterns in directories containing NeMo model files or datasets
• Suspicious serialized Python objects (.pkl, .pt, .nemo files) from untrusted sources appearing in model directories
• Anomalous system calls or process behavior during framework operations

Detection Strategies

• Monitor file integrity of model files and datasets used with NeMo Framework
• Implement file source validation and cryptographic signing for all model artifacts
• Deploy endpoint detection to identify suspicious process behavior during model loading operations
• Analyze network traffic for unexpected outbound connections from ML training environments

Monitoring Recommendations

• Enable detailed logging for NeMo Framework file loading operations
• Implement SentinelOne endpoint protection to detect and block exploitation attempts
• Monitor for unusual process genealogy where Python processes spawn unexpected child processes
• Track file system access to serialized model files from untrusted sources

How to Mitigate CVE-2025-33241

Immediate Actions Required

• Update NVIDIA NeMo Framework to the latest patched version as recommended by NVIDIA
• Audit all model files and datasets currently in use for integrity and trusted provenance
• Restrict file loading operations to only signed and verified model artifacts
• Implement network segmentation for ML training environments to limit post-exploitation impact

Patch Information

NVIDIA has released a security update addressing this vulnerability. For detailed patch information and affected version specifics, refer to the NVIDIA Security Support Answer. Administrators should apply the latest NeMo Framework update available through official NVIDIA channels and verify the integrity of their installation.

Workarounds

• Implement strict file provenance controls, only loading models from trusted and verified sources
• Use isolated container environments or sandboxed execution for processing untrusted model files
• Deploy application allowlisting to restrict which processes can be spawned by Python/NeMo Framework
• Consider using safer serialization formats where supported, avoiding pickle-based deserialization of untrusted data
• Enable SentinelOne behavioral AI detection to identify and block malicious code execution attempts