CVE-2025-23303: Nvidia Nemo Framework RCE Vulnerability

CVE-2025-23303 Overview

NVIDIA NeMo Framework for all platforms contains a critical insecure deserialization vulnerability that enables remote code execution. The vulnerability exists in the framework's handling of serialized data, where untrusted input is deserialized without proper validation. An attacker exploiting this vulnerability could achieve arbitrary code execution and data tampering on affected systems running the NeMo Framework.

Critical Impact

This vulnerability allows unauthenticated remote attackers to execute arbitrary code on systems running NVIDIA NeMo Framework, potentially leading to complete system compromise, data exfiltration, and persistent access across all supported platforms (Windows, Linux, and macOS).

Affected Products

• NVIDIA NeMo Framework (all versions prior to patched release)
• Microsoft Windows (all supported versions running NeMo)
• Linux Kernel (all distributions running NeMo)
• Apple macOS (all versions running NeMo)

Discovery Timeline

• August 13, 2025 - CVE-2025-23303 published to NVD
• September 23, 2025 - Last updated in NVD database

Technical Details for CVE-2025-23303

Vulnerability Analysis

This vulnerability is classified as CWE-502 (Deserialization of Untrusted Data). The NVIDIA NeMo Framework, a toolkit for building and training AI models, contains a flaw in how it processes serialized objects. When the framework deserializes data from untrusted sources, it fails to properly validate the integrity and authenticity of the serialized content before reconstructing objects in memory.

The vulnerability is particularly concerning because NeMo is widely used in AI/ML workflows and may process data from various external sources including model checkpoints, configuration files, and inter-process communications. An attacker can craft a malicious serialized payload that, when deserialized by the framework, instantiates arbitrary objects and triggers code execution.

Root Cause

The root cause stems from the framework's use of unsafe deserialization methods without implementing proper input validation or object type restrictions. Python-based frameworks like NeMo commonly use serialization libraries such as pickle or torch.load() for model persistence, which are known to be vulnerable to arbitrary code execution if fed untrusted data. The absence of cryptographic verification or allowlist-based type filtering allows attackers to inject malicious object graphs that execute code upon deserialization.

Attack Vector

The attack vector is network-based and requires no authentication or user interaction, making it highly exploitable in exposed environments. An attacker can deliver a malicious serialized payload through several vectors:

1. Model Checkpoint Poisoning: Crafting malicious model files that execute code when loaded
2. API Exploitation: Sending crafted serialized data to NeMo-based services accepting external input
3. Supply Chain Attacks: Distributing poisoned pre-trained models through model repositories

The vulnerability exploitation does not require any privileges on the target system, and successful exploitation grants the attacker the same privileges as the NeMo process, which often runs with elevated permissions for GPU access.

Detection Methods for CVE-2025-23303

Indicators of Compromise

• Unexpected child processes spawned by Python/NeMo processes, particularly shell interpreters or network utilities
• Anomalous network connections originating from AI/ML framework processes to unknown external hosts
• Unusual file system activity including creation of new executables or scripts in NeMo working directories
• Memory anomalies or crashes in NeMo processes following deserialization operations
• Log entries indicating deserialization errors or unexpected object instantiation

Detection Strategies

• Monitor for pickle.loads(), torch.load(), or similar deserialization calls processing data from external sources
• Implement runtime application self-protection (RASP) to detect suspicious object instantiation during deserialization
• Deploy network intrusion detection rules to identify serialized Python objects (\\x80\\x03 or \\x80\\x04 pickle headers) in network traffic
• Audit model loading operations and flag any models from untrusted or unverified sources
• Use SentinelOne Singularity to detect behavioral anomalies and post-exploitation activities

Monitoring Recommendations

• Enable comprehensive logging for all NeMo Framework operations, especially model loading and data processing functions
• Configure security monitoring to alert on new process execution from NeMo process trees
• Implement file integrity monitoring on directories containing model checkpoints and configuration files
• Monitor outbound network connections from systems running NeMo for data exfiltration attempts

How to Mitigate CVE-2025-23303

Immediate Actions Required

• Review and update NVIDIA NeMo Framework to the latest patched version as specified in the vendor advisory
• Audit all model files and serialized data sources for integrity; only load models from verified, trusted origins
• Implement network segmentation to isolate systems running NeMo from untrusted network segments
• Restrict deserialization functionality using safe loading methods such as torch.load(weights_only=True) where supported
• Apply principle of least privilege to NeMo processes to limit post-exploitation impact

Patch Information

NVIDIA has released a security update addressing this vulnerability. Administrators should consult the NVIDIA Support Advisory for specific patch information, affected version details, and update instructions. Priority should be given to internet-facing systems and those processing external data.

Workarounds

• Implement input validation and cryptographic verification for all serialized data before deserialization
• Use torch.load() with weights_only=True parameter to prevent arbitrary object instantiation when loading PyTorch models
• Deploy a Web Application Firewall (WAF) or API gateway to filter malicious serialized payloads in network traffic
• Sandbox NeMo processes using containerization or virtualization to contain potential compromises
• Disable or restrict external model loading capabilities until the patch can be applied

# Configuration example - Secure model loading with weights_only parameter
# For PyTorch/NeMo model loading, use safe deserialization:

# Instead of unsafe loading:
# model = torch.load('model.pt')

# Use safe weights-only loading:
# model = torch.load('model.pt', weights_only=True)

# Verify model checksums before loading
sha256sum -c model.pt.sha256