CVE-2025-33244: NVIDIA APEX RCE Vulnerability

CVE-2025-33244 Overview

CVE-2025-33244 is a critical insecure deserialization vulnerability affecting NVIDIA APEX for Linux. The vulnerability allows an unauthorized attacker to exploit deserialization of untrusted data in environments running PyTorch versions earlier than 2.6. A successful exploit could result in code execution, denial of service, privilege escalation, data tampering, and information disclosure.

Critical Impact

This vulnerability enables attackers on an adjacent network to achieve code execution with potential for complete system compromise, including privilege escalation and data manipulation.

Affected Products

• NVIDIA APEX for Linux
• Environments using PyTorch versions earlier than 2.6

Discovery Timeline

• 2026-03-24 - CVE-2025-33244 published to NVD
• 2026-03-25 - Last updated in NVD database

Technical Details for CVE-2025-33244

Vulnerability Analysis

This vulnerability falls under CWE-502 (Deserialization of Untrusted Data), a well-documented class of security flaws that occurs when an application deserializes data from untrusted sources without adequate validation. In the context of NVIDIA APEX for Linux, the vulnerability specifically affects environments utilizing PyTorch versions prior to 2.6.

NVIDIA APEX is a PyTorch extension designed to provide mixed precision training and distributed training utilities for deep learning workflows. The deserialization flaw allows an attacker with adjacent network access and low privileges to inject malicious serialized objects that are then processed by the vulnerable component. When these malicious objects are deserialized, arbitrary code can be executed within the context of the application.

The scope of this vulnerability is changed (as indicated by the security metrics), meaning a successful exploit can affect resources beyond the vulnerable component's security scope. This makes the vulnerability particularly dangerous in multi-tenant or shared computing environments commonly used for machine learning workloads.

Root Cause

The root cause is improper handling of serialized data within NVIDIA APEX when operating with vulnerable versions of PyTorch (prior to version 2.6). The application fails to adequately validate or sanitize serialized objects before deserialization, allowing attackers to craft malicious payloads that execute arbitrary code when processed.

PyTorch's serialization mechanisms, like Python's pickle module, are inherently unsafe when handling untrusted data, as they can instantiate arbitrary Python objects. APEX's reliance on these mechanisms without proper input validation creates the attack surface.

Attack Vector

The attack requires adjacent network access, meaning the attacker must be on the same network segment as the vulnerable system. With low-privilege access, an attacker can deliver malicious serialized payloads to the APEX component. When these payloads are deserialized, the attacker can achieve:

• Remote code execution within the application context
• Privilege escalation to gain higher-level access
• Data tampering to modify training data or model parameters
• Information disclosure to exfiltrate sensitive data
• Denial of service to disrupt machine learning operations

The vulnerability is particularly concerning in GPU cluster environments where APEX is commonly deployed for distributed training operations.

Detection Methods for CVE-2025-33244

Indicators of Compromise

• Unexpected network connections from APEX processes to adjacent network hosts
• Anomalous deserialization activity or unusual object instantiation in PyTorch/APEX logs
• Suspicious process spawning from Python processes running APEX workloads
• Unauthorized file system modifications in machine learning environment directories
• Unexpected privilege changes or new user account creation in affected systems

Detection Strategies

• Monitor for suspicious pickle or serialization-related operations in APEX and PyTorch processes
• Implement network segmentation monitoring to detect lateral movement attempts from adjacent network positions
• Deploy endpoint detection rules targeting unusual Python deserialization patterns
• Use application-level logging to track serialized data sources and validate against expected origins

Monitoring Recommendations

• Enable verbose logging for APEX and PyTorch operations in production environments
• Implement network traffic analysis for machine learning infrastructure to identify anomalous data transfers
• Deploy file integrity monitoring on critical ML model files and training data directories
• Configure alerts for unexpected process execution chains originating from Python interpreters

How to Mitigate CVE-2025-33244

Immediate Actions Required

• Upgrade PyTorch to version 2.6 or later, which addresses the underlying deserialization handling
• Restrict network access to APEX-enabled systems to trusted hosts only
• Implement network segmentation to isolate machine learning workloads from untrusted network segments
• Review and audit current APEX deployments to identify potentially affected systems

Patch Information

NVIDIA has released guidance addressing this vulnerability. Organizations should consult the NVIDIA Support Article for official remediation instructions. The primary mitigation involves upgrading PyTorch to version 2.6 or later, which provides improved handling of serialized data.

For detailed vulnerability information, refer to the NVD CVE-2025-33244 Entry.

Workarounds

• Implement strict network access controls to prevent untrusted adjacent network access to APEX systems
• Use application-level firewalls to filter and validate incoming serialized data before processing
• Isolate machine learning environments in network segments with restricted access from other systems
• Consider using alternative serialization methods that do not execute arbitrary code during deserialization

# Network isolation configuration example
# Restrict adjacent network access to APEX systems
iptables -A INPUT -s 10.0.0.0/8 -p tcp --dport 29500 -j DROP
iptables -A INPUT -s 172.16.0.0/12 -p tcp --dport 29500 -j DROP

# Allow only trusted hosts for distributed training
iptables -A INPUT -s 192.168.1.100 -p tcp --dport 29500 -j ACCEPT
iptables -A INPUT -s 192.168.1.101 -p tcp --dport 29500 -j ACCEPT