CVE-2025-23249: Nvidia Nemo Framework RCE Vulnerability

CVE-2025-23249 Overview

CVE-2025-23249 is a critical insecure deserialization vulnerability in the NVIDIA NeMo Framework that enables remote code execution. The vulnerability allows attackers to exploit untrusted data deserialization mechanisms within the framework, potentially leading to arbitrary code execution and data tampering on affected systems. NVIDIA NeMo is a scalable generative AI framework built for researchers and developers working on large language models, multimodal models, and various AI applications, making this vulnerability particularly concerning for AI/ML development environments.

Critical Impact

This vulnerability allows unauthenticated remote attackers to execute arbitrary code on systems running vulnerable versions of NVIDIA NeMo Framework, potentially compromising AI development infrastructure, training data, and model integrity.

Affected Products

• NVIDIA NeMo Framework (all versions prior to patched release)
• Systems running NeMo on Apple macOS
• Systems running NeMo on Linux
• Systems running NeMo on Microsoft Windows

Discovery Timeline

• April 22, 2025 - CVE-2025-23249 published to NVD
• September 24, 2025 - Last updated in NVD database

Technical Details for CVE-2025-23249

Vulnerability Analysis

This vulnerability is classified under CWE-502 (Deserialization of Untrusted Data). The NVIDIA NeMo Framework improperly handles deserialization of user-supplied data, creating an attack surface for remote code execution. In machine learning frameworks like NeMo, serialization is commonly used for model checkpoints, configuration files, and data pipelines. When deserialization occurs without proper validation, attackers can craft malicious serialized objects that execute arbitrary code upon being processed by the framework.

The network-accessible nature of this vulnerability means attackers can exploit it remotely without requiring authentication or user interaction. Successful exploitation leads to complete compromise of confidentiality, integrity, and availability of the affected system.

Root Cause

The root cause of CVE-2025-23249 lies in the framework's handling of serialized data without adequate validation or sanitization. Python-based ML frameworks often utilize serialization libraries such as pickle, torch.load(), or custom serialization mechanisms for model persistence and data transfer. When these operations process untrusted input, malicious payloads embedded within the serialized data can trigger arbitrary code execution during the deserialization process.

The vulnerability exists because the NeMo Framework does not implement sufficient controls to verify the integrity and trustworthiness of serialized objects before processing them, allowing attackers to inject malicious code that executes with the privileges of the application.

Attack Vector

The attack vector for this vulnerability is network-based, requiring no privileges or user interaction. An attacker can exploit this vulnerability by:

1. Identifying a NeMo Framework instance that processes externally-supplied serialized data
2. Crafting a malicious serialized payload containing code execution primitives
3. Submitting the payload to the vulnerable deserialization endpoint
4. The malicious payload executes arbitrary code when deserialized by the framework

Common attack scenarios include submitting malicious model checkpoint files, corrupted configuration data, or exploiting API endpoints that accept serialized inputs. In ML/AI environments, this could lead to model poisoning, data exfiltration, or lateral movement within development infrastructure.

Detection Methods for CVE-2025-23249

Indicators of Compromise

• Unexpected process spawning from Python/NeMo processes, particularly shell commands or network connections
• Anomalous file system activity in NeMo working directories including creation of unknown files
• Unusual network connections originating from systems running NeMo Framework
• Modified or corrupted model checkpoint files with unexpected content
• Error logs indicating deserialization failures or unexpected object types

Detection Strategies

• Monitor for suspicious pickle.loads(), torch.load(), or similar deserialization function calls processing external data
• Implement file integrity monitoring on model checkpoints and configuration files
• Deploy network intrusion detection rules to identify malicious serialized Python objects
• Use application-level logging to track deserialization operations and their sources
• Implement SentinelOne Singularity to detect behavioral anomalies associated with deserialization attacks

Monitoring Recommendations

• Enable comprehensive logging for all NeMo Framework operations, particularly those involving external data
• Monitor process trees for unexpected child processes spawned by NeMo applications
• Track network connections from ML infrastructure systems to detect command and control activity
• Implement alerting on model file modifications outside normal training workflows

How to Mitigate CVE-2025-23249

Immediate Actions Required

• Update NVIDIA NeMo Framework to the latest patched version as specified in the NVIDIA security advisory
• Audit all external data sources that provide serialized input to NeMo applications
• Implement network segmentation to isolate ML development infrastructure
• Review and restrict permissions for NeMo Framework processes
• Temporarily disable any endpoints that accept serialized data from untrusted sources until patches are applied

Patch Information

NVIDIA has released a security update addressing this vulnerability. Organizations should consult the NVIDIA Security Advisory for specific patch versions and update instructions. It is strongly recommended to apply the patch immediately given the critical severity and remote exploitation capability of this vulnerability.

Workarounds

• Implement strict input validation for all serialized data before deserialization
• Use safer serialization formats such as JSON or Protocol Buffers instead of pickle where possible
• Deploy web application firewalls or API gateways to filter potentially malicious serialized payloads
• Restrict network access to NeMo Framework instances, allowing only trusted sources
• Run NeMo Framework in containerized or sandboxed environments with limited system privileges

# Configuration example - Restrict network access to NeMo services
# Add firewall rules to limit access to trusted sources only
iptables -A INPUT -p tcp --dport 5000 -s trusted_network/24 -j ACCEPT
iptables -A INPUT -p tcp --dport 5000 -j DROP

# Run NeMo in a restricted environment
# Use container isolation with limited capabilities
docker run --cap-drop=ALL --cap-add=NET_BIND_SERVICE \
  --read-only --tmpfs /tmp \
  --security-opt=no-new-privileges \
  nvidia/nemo:latest