CVE-2026-24157: NVIDIA NeMo Framework RCE Vulnerability

CVE-2026-24157 Overview

CVE-2026-24157 is an insecure deserialization vulnerability in the NVIDIA NeMo Framework that affects the checkpoint loading functionality. An attacker who can supply a malicious checkpoint file could achieve remote code execution on the target system. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure, and data tampering.

Critical Impact
Attackers can leverage malicious checkpoint files to execute arbitrary code, escalate privileges, and compromise system integrity through the NeMo Framework's checkpoint loading mechanism.

Affected Products

NVIDIA NeMo Framework (checkpoint loading functionality)

Discovery Timeline

2026-03-24 - CVE-2026-24157 published to NVD
2026-03-25 - Last updated in NVD database

Technical Details for CVE-2026-24157

Vulnerability Analysis

This vulnerability stems from insecure deserialization (CWE-502) in the NVIDIA NeMo Framework's checkpoint loading mechanism. NeMo is a conversational AI toolkit commonly used for training and deploying large language models, speech recognition systems, and other deep learning applications.

The checkpoint loading functionality processes serialized model state files without adequate validation of the serialized data. When a user or automated process loads a checkpoint file, the deserialization routine can instantiate arbitrary objects and execute malicious code embedded within the crafted checkpoint.

The vulnerability requires local access, meaning an attacker must be able to place a malicious checkpoint file on the target system or convince a user to load a checkpoint from an untrusted source. This could occur through supply chain attacks, compromised model repositories, or social engineering tactics targeting machine learning practitioners.

Root Cause

The root cause is improper handling of serialized data during checkpoint loading operations. Python-based machine learning frameworks commonly use pickle or similar serialization formats for saving model checkpoints. These serialization mechanisms can execute arbitrary code during the deserialization process if the input data is not properly validated. The NeMo Framework does not adequately sanitize or restrict the types of objects that can be instantiated when loading checkpoint files, allowing attackers to inject malicious payloads.

Attack Vector

The attack requires local access to the system where the NeMo Framework is installed. An attacker must craft a malicious checkpoint file containing embedded code and either place it on the target system or trick a user into downloading and loading it. Common attack scenarios include:

The vulnerability is exploited through the deserialization of untrusted checkpoint data. When a malicious checkpoint file is loaded by the NeMo Framework, the serialized payload is deserialized and executed with the privileges of the user running the framework. This can result in complete system compromise, data exfiltration, or lateral movement within the network.

For technical details on this vulnerability, refer to the NVIDIA Support Answer and the NVD CVE-2026-24157 Details.

Detection Methods for CVE-2026-24157

Indicators of Compromise

Unexpected checkpoint files appearing in NeMo model directories or download locations
Unusual process spawning or network connections originating from Python processes running NeMo
Suspicious file access patterns during model loading operations
Unexpected privilege escalation or lateral movement following checkpoint loading activities

Detection Strategies

Monitor file integrity for checkpoint directories and flag newly introduced or modified .ckpt files
Implement application whitelisting to detect unauthorized code execution during model loading
Deploy endpoint detection and response (EDR) solutions to identify anomalous behavior from machine learning framework processes
Use SentinelOne's behavioral AI to detect suspicious process chains originating from Python or NeMo-related processes

Monitoring Recommendations

Enable audit logging for file system access to model checkpoint directories
Monitor for unusual outbound network connections from systems running NeMo Framework
Implement anomaly detection for resource usage spikes during checkpoint loading operations
Configure alerts for any unexpected child process creation by NeMo-related processes

How to Mitigate CVE-2026-24157

Immediate Actions Required

Only load checkpoint files from trusted and verified sources
Implement strict access controls on directories where checkpoint files are stored
Review and audit all checkpoint files before loading them in production environments
Consider running NeMo Framework in isolated or sandboxed environments

Patch Information

NVIDIA has released a security update addressing this vulnerability. Refer to the NVIDIA Support Answer for specific patch information and updated versions of the NeMo Framework. Organizations should update to the latest patched version as soon as possible.

Workarounds

Restrict checkpoint loading to files from verified and trusted sources only
Implement network segmentation to isolate systems running machine learning workloads
Use file integrity monitoring to detect unauthorized modifications to checkpoint files
Consider implementing a checkpoint validation pipeline that scans files before loading

bash

# Configuration example: Restrict file permissions on checkpoint directories
chmod 750 /path/to/nemo/checkpoints
chown root:ml-team /path/to/nemo/checkpoints
# Enable audit logging for checkpoint directory access
auditctl -w /path/to/nemo/checkpoints -p rwxa -k nemo_checkpoint_access