CVE-2025-23304: Nvidia Nemo RCE Vulnerability

CVE-2025-23304 Overview

CVE-2025-23304 is a critical code injection vulnerability affecting the NVIDIA NeMo library across all platforms. The vulnerability exists in the model loading component, where an attacker can inject arbitrary code by loading .nemo files containing maliciously crafted metadata. A successful exploit of this vulnerability may lead to remote code execution and data tampering, making it particularly dangerous in AI/ML development and production environments where model files are frequently shared and loaded.

Critical Impact
This vulnerability enables remote code execution through malicious .nemo model files, potentially compromising entire AI/ML pipelines and allowing attackers to tamper with training data or inference results.

Affected Products

NVIDIA NeMo (all versions prior to patched release)
Apple macOS (when running vulnerable NeMo versions)
Linux Kernel-based systems (when running vulnerable NeMo versions)
Microsoft Windows (when running vulnerable NeMo versions)

Discovery Timeline

August 13, 2025 - CVE-2025-23304 published to NVD
September 24, 2025 - Last updated in NVD database

Technical Details for CVE-2025-23304

Vulnerability Analysis

This vulnerability combines two weakness categories: CWE-22 (Path Traversal) and CWE-94 (Code Injection). The NVIDIA NeMo library's model loading component fails to properly sanitize metadata embedded within .nemo files before processing. When a user loads a maliciously crafted model file, the embedded metadata can be interpreted and executed as code, enabling arbitrary code execution in the context of the application loading the model.

The attack requires no privileges and can be executed remotely over the network. No user interaction beyond loading the malicious file is required, making this vulnerability particularly severe. The potential impact spans complete compromise of confidentiality, integrity, and availability of the affected system.

Root Cause

The root cause of CVE-2025-23304 stems from insufficient validation and sanitization of metadata fields within .nemo files during the model loading process. The NeMo library's deserialization routine processes embedded metadata without adequately checking for malicious content or path traversal sequences, allowing attackers to inject executable code or escape intended directory boundaries. This represents a fundamental failure in input validation within the model loading pipeline.

Attack Vector

The attack vector for this vulnerability is network-based, meaning an attacker can exploit it remotely. The attack scenario typically involves:

An attacker crafts a malicious .nemo file with injected code in the metadata fields
The malicious file is distributed through model repositories, shared drives, or direct transfer
A victim loads the compromised model file using the NeMo library
The injected code executes with the privileges of the application, potentially leading to full system compromise

The combination of path traversal (CWE-22) and code injection (CWE-94) weaknesses means attackers can both escape sandboxed directories and execute arbitrary code, maximizing the potential damage from a successful exploit.

Detection Methods for CVE-2025-23304

Indicators of Compromise

Unexpected code execution or processes spawned when loading .nemo model files
Suspicious network connections originating from NeMo-based applications
Modified or corrupted model files with unusual metadata structures
Anomalous file system access patterns, particularly path traversal attempts outside expected model directories

Detection Strategies

Monitor NeMo library function calls for unusual metadata parsing behavior
Implement file integrity monitoring on model file repositories and storage locations
Deploy runtime application security monitoring to detect code injection attempts
Analyze .nemo file metadata for embedded executable code or suspicious path sequences before loading

Monitoring Recommendations

Enable verbose logging for NeMo model loading operations to capture metadata processing details
Configure endpoint detection to alert on unexpected child processes spawned by Python/NeMo applications
Implement network monitoring for AI/ML infrastructure to detect unusual outbound connections
Establish baseline behavior for model loading operations and alert on deviations

How to Mitigate CVE-2025-23304

Immediate Actions Required

Update NVIDIA NeMo to the latest patched version as specified in the vendor advisory
Audit all .nemo model files in your environment for integrity and origin verification
Restrict model file loading to trusted, validated sources only
Implement application-level sandboxing for NeMo-based applications to limit blast radius of potential exploitation

Patch Information

NVIDIA has released a security update to address this vulnerability. Consult the NVIDIA Support Article for detailed patch information and download instructions. Organizations should prioritize applying this patch immediately given the critical severity and remote code execution potential of this vulnerability.

Workarounds

Validate the source and integrity of all .nemo files before loading using cryptographic signatures
Run NeMo applications in isolated containers or sandboxed environments with restricted permissions
Implement network segmentation to limit the impact of potential compromise
Disable or restrict model loading from untrusted or external sources until patching is complete

bash

# Example: Verify .nemo file checksum before loading
# Ensure model files are from trusted sources with verified integrity
sha256sum model_file.nemo
# Compare against known-good checksum from trusted source

# Run NeMo applications in restricted container
docker run --read-only --network=none --user nobody nvidia-nemo-app