CVE-2025-23314: Nvidia Nemo Framework RCE Vulnerability

CVE-2025-23314 Overview

CVE-2025-23314 is a code injection vulnerability [CWE-94] in the NVIDIA NeMo Framework, affecting the Natural Language Processing (NLP) component across all supported platforms. An attacker with local access and low privileges can craft malicious input data that the framework processes unsafely, resulting in arbitrary code execution. Successful exploitation can lead to code execution in the context of the NeMo process, privilege escalation, information disclosure, and data tampering. NVIDIA has acknowledged the issue and released remediation guidance through its security advisory portal.

Critical Impact
Local attackers can inject and execute arbitrary code within NeMo Framework workloads, compromising confidentiality, integrity, and availability of AI/ML training and inference environments.

Affected Products

NVIDIA NeMo Framework (all platforms) prior to the fixed release
Linux deployments running NeMo NLP workloads
Windows deployments running NeMo NLP workloads

Discovery Timeline

2025-08-26 - CVE-2025-23314 published to the National Vulnerability Database
2026-06-17 - Last updated in NVD database

Technical Details for CVE-2025-23314

Vulnerability Analysis

The vulnerability resides in the NLP component of the NVIDIA NeMo Framework, which handles text, tokenization, and model configuration workflows for large language model training and fine-tuning. The framework processes attacker-supplied data in a way that allows injected content to be interpreted as code rather than data. This mismatch between the expected data context and the actual execution context is characteristic of [CWE-94] Improper Control of Generation of Code.

Exploitation requires local access with low privileges and no user interaction. Once triggered, the injected code executes with the privileges of the NeMo process. In typical machine learning environments, this process often has access to GPU resources, model artifacts, training datasets, and cloud credentials. Successful exploitation compromises confidentiality, integrity, and availability of the host and any downstream ML pipeline components.

Root Cause

The root cause is unsafe handling of attacker-controlled input within the NLP component. Instead of treating untrusted data purely as inert content, the framework passes it through a code-generation or evaluation path where the data influences executable logic. This class of flaw commonly arises when configuration files, model definitions, or serialized objects are parsed with functions such as eval, exec, dynamic import, or unrestricted deserialization routines.

Attack Vector

An attacker must be able to place or supply a crafted artifact that NeMo consumes. Common vectors include supplying a malicious configuration file, model checkpoint, tokenizer definition, or dataset that a legitimate user then loads through the NeMo NLP APIs. When the framework processes the artifact, the embedded payload executes on the victim host.

No verified public exploit or proof-of-concept is currently available. The vulnerability mechanism is described in prose based on the NVIDIA security advisory and the NVD entry for CVE-2025-23314.

Detection Methods for CVE-2025-23314

Indicators of Compromise

Unexpected child processes spawned by Python interpreters running NeMo workloads, particularly shells, curl, wget, or compilers
Outbound network connections from ML training hosts to unknown destinations shortly after loading a model, tokenizer, or config file
New or modified files under NeMo working directories, home directories, or ~/.cache that were not written by the training job
Anomalous GPU host activity such as credential access from ~/.aws, ~/.config/gcloud, or Kubernetes service account tokens

Detection Strategies

Monitor process ancestry for python or nemo parent processes launching interactive shells or system utilities
Alert on writes to shell startup files, cron entries, or systemd unit files originating from ML training processes
Baseline network egress from GPU nodes and flag deviations that follow artifact loads
Inspect .yaml, .json, and .py NeMo config files for embedded Python expressions or serialization tags that trigger code execution

Monitoring Recommendations

Enable command-line and process-creation auditing on all hosts running NeMo
Forward EDR telemetry, GPU node logs, and Kubernetes audit logs to a centralized data lake for correlation
Track integrity of model registries and dataset stores to detect tampered artifacts before they reach training clusters

How to Mitigate CVE-2025-23314

Immediate Actions Required

Upgrade NVIDIA NeMo Framework to the fixed version specified in the NVIDIA security bulletin
Inventory all hosts, containers, and images that include NeMo and prioritize patching of internet-exposed or multi-tenant systems
Restrict which users can supply configuration files, tokenizers, and model checkpoints to NeMo training jobs
Run NeMo workloads under least-privileged service accounts without direct access to cloud credentials or production data stores

Patch Information

NVIDIA has published remediation details in advisory ID 5689. Refer to the NVIDIA Support Response for the fixed version numbers and package guidance. Apply the update to all NeMo installations, including container images used in CI/CD pipelines and Kubernetes clusters. Rebuild any downstream images that pin a vulnerable NeMo release.

Workarounds

Load only NeMo artifacts from trusted, integrity-verified sources such as signed model registries
Execute NeMo NLP jobs inside sandboxed containers with read-only file systems and no outbound network access except to required registries
Disable or restrict dynamic configuration features that evaluate expressions from untrusted YAML or JSON inputs
Enforce code review and provenance checks on any third-party NeMo recipes, configs, or notebooks before execution

bash

# Configuration example: upgrade NeMo and run workloads with reduced privileges
pip install --upgrade nemo_toolkit

# Run training inside a hardened container
docker run --rm \
  --read-only \
  --cap-drop=ALL \
  --network=none \
  --user 1000:1000 \
  -v /trusted/models:/models:ro \
  nvcr.io/nvidia/nemo:<patched-tag> \
  python train.py --config /models/verified_config.yaml