CVE-2025-33252: NVIDIA NeMo Framework RCE Vulnerability

CVE-2025-33252 Overview

NVIDIA NeMo Framework contains a vulnerability where an attacker could cause remote code execution through insecure deserialization. A successful exploit of this vulnerability might lead to code execution, denial of service, information disclosure, and data tampering. The vulnerability requires local access and low privileges to exploit, making it particularly dangerous in shared computing environments where NeMo Framework is deployed for AI/ML workloads.

Critical Impact

Successful exploitation enables arbitrary code execution with potential for complete system compromise, data theft, and service disruption in AI/ML development environments.

Affected Products

• NVIDIA NeMo Framework (specific versions not disclosed)

Discovery Timeline

• 2026-02-18 - CVE-2025-33252 published to NVD
• 2026-02-18 - Last updated in NVD database

Technical Details for CVE-2025-33252

Vulnerability Analysis

This vulnerability is classified under CWE-502 (Deserialization of Untrusted Data), indicating that the NVIDIA NeMo Framework improperly handles deserialization of user-controlled data. In machine learning frameworks like NeMo, serialization is commonly used for model checkpoints, configuration files, and inter-process communication. When deserialization occurs without proper validation, an attacker can craft malicious serialized objects that execute arbitrary code upon being deserialized.

The local attack vector suggests that exploitation requires the attacker to have some level of access to the target system or the ability to influence files that the NeMo Framework processes. This could include uploading malicious model files, manipulating configuration data, or poisoning training datasets that get serialized and deserialized during ML workflows.

Root Cause

The root cause is insecure deserialization (CWE-502) within the NVIDIA NeMo Framework. The framework likely uses Python's pickle module or similar serialization mechanisms to save and load model checkpoints, configurations, or other data structures. Without proper input validation and sanitization before deserialization, malicious payloads embedded in serialized data can trigger arbitrary code execution when the data is loaded.

Python's pickle module is notorious for deserialization vulnerabilities because it can execute arbitrary Python code during the unpickling process through the __reduce__ method. AI/ML frameworks frequently use pickle for convenience, creating significant attack surface.

Attack Vector

The attack requires local access to the system running NVIDIA NeMo Framework. An attacker with low-level privileges can exploit this vulnerability by:

1. Crafting a malicious serialized object containing embedded code execution payloads
2. Placing or substituting this malicious payload where NeMo Framework will deserialize it (e.g., model checkpoints, configuration files)
3. Triggering the deserialization process through normal framework operations
4. Achieving code execution with the privileges of the NeMo Framework process

The attack does not require user interaction, making it suitable for automated exploitation in environments where multiple users share access to ML infrastructure.

Detection Methods for CVE-2025-33252

Indicators of Compromise

• Unexpected modifications to model checkpoint files or configuration files in NeMo project directories
• Presence of unusual or obfuscated serialized data in .pkl, .pt, or .nemo files
• Suspicious process spawning from Python processes running NeMo Framework
• Unauthorized network connections or reverse shells initiated from ML training environments

Detection Strategies

• Monitor file integrity of model checkpoints and configuration files using cryptographic hashing
• Implement application-level logging for deserialization operations within NeMo workflows
• Deploy endpoint detection and response (EDR) solutions to detect anomalous behavior from Python processes
• Use SentinelOne Singularity to detect post-exploitation behaviors such as privilege escalation or lateral movement

Monitoring Recommendations

• Enable detailed audit logging for file access in directories containing NeMo models and configurations
• Monitor for unusual process trees originating from Python interpreters running ML workloads
• Implement network segmentation and monitor egress traffic from ML infrastructure for command-and-control indicators
• Review and validate provenance of any externally-sourced model files before loading them into NeMo Framework

How to Mitigate CVE-2025-33252

Immediate Actions Required

• Review the NVIDIA Knowledge Base Article for official patch information and apply available updates
• Audit all model checkpoint and configuration files currently in use for signs of tampering
• Restrict access to NeMo Framework installations using principle of least privilege
• Implement file integrity monitoring on directories containing serialized model data

Patch Information

NVIDIA has published security guidance for this vulnerability. Administrators should consult the NVIDIA Knowledge Base Article for specific patch versions and update instructions. Apply the latest security updates to NVIDIA NeMo Framework as soon as they become available.

Workarounds

• Only load model checkpoints and serialized data from trusted, verified sources
• Implement strict access controls to prevent unauthorized users from modifying serialized files
• Consider using safer serialization formats like JSON or Protocol Buffers for configuration data where possible
• Run NeMo Framework in isolated environments (containers, sandboxes) to limit the impact of potential exploitation

# Example: Verify model checkpoint integrity before loading
# Calculate and verify SHA256 hash of model files
sha256sum -c model_checksums.txt

# Restrict permissions on model directories
chmod 755 /path/to/nemo/models
chown -R ml-service:ml-service /path/to/nemo/models