CVE-2025-33243: NVIDIA NeMo Framework RCE Vulnerability

CVE-2025-33243 Overview

NVIDIA NeMo Framework contains a vulnerability where an attacker could cause remote code execution in distributed environments. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure, and data tampering. This insecure deserialization vulnerability (CWE-502) poses a significant risk to organizations deploying NVIDIA NeMo for AI and machine learning workloads.

Critical Impact

Successful exploitation enables remote code execution in distributed AI/ML environments, potentially leading to full system compromise, privilege escalation, sensitive data exposure, and data manipulation across distributed training infrastructure.

Affected Products

• NVIDIA NeMo Framework (vulnerable versions)
• Distributed AI/ML training environments using NeMo
• Systems with NeMo deployed in multi-node configurations

Discovery Timeline

• 2026-02-18 - CVE CVE-2025-33243 published to NVD
• 2026-02-18 - Last updated in NVD database

Technical Details for CVE-2025-33243

Vulnerability Analysis

This vulnerability is classified as CWE-502: Deserialization of Untrusted Data. The NVIDIA NeMo Framework, used extensively for building and training AI models, contains an insecure deserialization flaw that can be exploited in distributed computing environments. When processing serialized objects during distributed training or model operations, the framework fails to properly validate the integrity and safety of deserialized data.

In distributed AI/ML environments, nodes frequently exchange serialized data for model checkpoints, training state, and inter-process communication. An attacker with local access to the distributed environment could craft malicious serialized payloads that, when deserialized by NeMo, execute arbitrary code with the privileges of the NeMo process. This could compromise not only the immediate host but potentially propagate across the entire distributed training cluster.

Root Cause

The root cause of this vulnerability stems from the insecure handling of deserialized objects within the NVIDIA NeMo Framework. The framework does not implement sufficient validation or sanitization of incoming serialized data before processing it. This allows attackers to inject malicious object references or code execution gadgets within serialized payloads that trigger arbitrary code execution during the deserialization process.

Attack Vector

This vulnerability requires local access to exploit, indicating that an attacker must have some level of access to the distributed environment or network where NeMo is deployed. The attack involves injecting crafted serialized payloads into data streams or storage locations that NeMo processes during distributed operations. The malicious payload is then deserialized by the framework, triggering code execution.

Given the distributed nature of NeMo deployments, a successful attack could allow lateral movement across training nodes, access to model weights and training data, manipulation of AI model outputs, and persistent access to the AI infrastructure.

The vulnerability mechanism involves the unsafe deserialization of objects that may contain malicious instructions. When NeMo processes these objects without proper validation, the embedded malicious code is executed in the context of the framework process. For detailed technical analysis, refer to the NVIDIA Support Advisory.

Detection Methods for CVE-2025-33243

Indicators of Compromise

• Unexpected process spawning from NeMo framework processes
• Anomalous network connections originating from distributed training nodes
• Unusual file system access patterns in NeMo working directories
• Unexpected modification of model checkpoints or training state files

Detection Strategies

• Monitor for suspicious deserialization operations within NeMo processes using runtime application monitoring
• Implement file integrity monitoring for model checkpoint directories and training state storage
• Deploy endpoint detection solutions to identify code execution attempts from NeMo process contexts
• Analyze network traffic between distributed training nodes for anomalous serialized data patterns

Monitoring Recommendations

• Enable verbose logging for NVIDIA NeMo Framework operations in distributed environments
• Implement security monitoring on inter-node communication channels used for distributed training
• Configure alerts for unexpected privilege escalation attempts in AI/ML infrastructure
• Monitor for unauthorized access to training data and model artifact storage locations

How to Mitigate CVE-2025-33243

Immediate Actions Required

• Review the NVIDIA Support Advisory for official patch and mitigation guidance
• Inventory all systems running NVIDIA NeMo Framework in your environment
• Restrict local access to distributed training environments to authorized personnel only
• Implement network segmentation to isolate AI/ML training infrastructure

Patch Information

NVIDIA has released security guidance for this vulnerability. Organizations should consult the NVIDIA Support Advisory for the latest patch information and update instructions. Apply vendor-provided updates to all NeMo installations as soon as they become available.

Additional details can be found at the NVD CVE-2025-33243 Details page.

Workarounds

• Implement strict access controls to limit who can submit jobs or data to distributed NeMo environments
• Validate and sanitize all external inputs before processing with the NeMo Framework
• Use application-layer firewalls to inspect and filter serialized data traffic between nodes
• Consider running NeMo processes in isolated containers with restricted capabilities until patches are applied

# Configuration example - Restrict NeMo process permissions
# Limit the capabilities of NeMo processes in containerized deployments
# Example Docker security options:
docker run --security-opt=no-new-privileges \
  --cap-drop=ALL \
  --read-only \
  --network=internal-training \
  nvidia/nemo:latest