CVE-2025-23295: Nvidia Apex RCE Vulnerability

CVE-2025-23295 Overview

CVE-2025-23295 is a code injection vulnerability in NVIDIA Apex, a PyTorch extension providing mixed-precision and distributed training utilities for GPU-accelerated workloads. The flaw resides in a Python component that processes attacker-controlled files without sufficient validation. A local authenticated user can supply a malicious file to trigger arbitrary code execution in the context of the Apex process. Successful exploitation can lead to code execution, privilege escalation, information disclosure, and data tampering. The vulnerability is tracked under CWE-94: Improper Control of Generation of Code and affects NVIDIA Apex across all supported platforms.

Critical Impact
A local attacker with low privileges can achieve arbitrary code execution, escalate privileges, and tamper with data on any host running a vulnerable NVIDIA Apex installation.

Affected Products

NVIDIA Apex (all platforms) prior to the fixed release
Machine learning and training environments using Apex mixed-precision utilities
GPU workstations and servers with NVIDIA Apex deployed as a Python dependency

Discovery Timeline

2025-08-13 - CVE-2025-23295 published to the National Vulnerability Database
2026-06-17 - Last updated in the NVD database

Technical Details for CVE-2025-23295

Vulnerability Analysis

CVE-2025-23295 is a code injection weakness classified under CWE-94. NVIDIA Apex includes Python code that processes external files during model, checkpoint, or configuration loading. When the affected component parses attacker-supplied data, it evaluates or deserializes content that ultimately reaches a Python execution primitive.

The attack requires local access and low-level privileges on the host running Apex. No user interaction is needed once the malicious file is placed in a path the Apex workflow reads. Because Apex is commonly used in shared training environments and multi-tenant GPU clusters, one low-privileged user can pivot to the identity of any other user or service invoking Apex on the same host.

Once triggered, the injected code runs with the permissions of the Python process. In data-science pipelines this frequently includes access to model artifacts, training datasets, cloud credentials mounted into the environment, and orchestration tokens.

Root Cause

The root cause is improper neutralization of directives embedded in a file that Apex loads through a Python component. Instead of restricting parsing to a safe schema, the component allows constructs that resolve to executable Python objects, permitting the attacker to influence code generation at load time.

Attack Vector

Exploitation follows a local file-based path. An attacker with an account on the target system places a crafted file in a location consumed by an Apex workflow, such as a checkpoint directory, configuration path, or model artifact store. When a user or scheduled job invokes Apex, the malicious file is parsed and injected code executes under that principal. Refer to the NVIDIA Security Bulletin for vendor-specific technical details.

// No verified public proof-of-concept is available for CVE-2025-23295.
// See the NVIDIA advisory referenced above for technical specifics.

Detection Methods for CVE-2025-23295

Indicators of Compromise

Unexpected child processes spawned from Python interpreters that have imported the apex module
Modification of checkpoint, configuration, or serialized model files by accounts that do not normally write to those paths
Outbound network connections initiated from training jobs to non-approved destinations
New or altered files under Apex working directories with mismatched ownership or timestamps

Detection Strategies

Inventory hosts running NVIDIA Apex by querying installed Python packages (pip show apex) across managed endpoints and GPU nodes
Alert on Python processes executing shells, package managers, or credential-access binaries such as curl, wget, bash, or aws
Monitor file integrity on directories that hold model checkpoints, YAML configs, and pickle artifacts consumed by Apex
Correlate anomalous GPU job launches with recent writes to shared training storage

Monitoring Recommendations

Enable process-lineage telemetry on ML training hosts and forward it to a centralized analytics platform for retention and correlation
Track imports of apex alongside subsequent os.system, subprocess, or eval invocations in Python audit logs
Baseline outbound network egress from training clusters and alert on deviations coinciding with Apex job execution

How to Mitigate CVE-2025-23295

Immediate Actions Required

Upgrade NVIDIA Apex to the fixed version identified in the NVIDIA Security Bulletin ID 5680
Restrict write permissions on directories containing Apex configuration files, checkpoints, and model artifacts to trusted service accounts only
Audit shared GPU hosts for untrusted files placed under paths consumed by Apex workflows
Rotate credentials and tokens accessible to any training job that ran on a potentially exposed host

Patch Information

NVIDIA has released a security update addressing CVE-2025-23295. Administrators should follow the remediation guidance published in the NVIDIA Support advisory and update all Apex installations across development workstations, CI runners, and GPU training clusters. Verify the installed version after upgrade using pip show apex.

Workarounds

Isolate multi-user GPU hosts using separate OS accounts, filesystem namespaces, or containers so one user cannot write into another user's Apex paths
Load only Apex artifacts sourced from signed, integrity-verified repositories and reject files from untrusted origins
Disable or refactor code paths that deserialize Python objects from user-controlled inputs until the upgrade is applied

bash

# Verify installed Apex version and upgrade to the patched release
pip show apex
pip install --upgrade apex

# Restrict permissions on Apex artifact directories
chown -R mluser:mlgroup /opt/ml/apex
chmod -R 750 /opt/ml/apex