CVE-2025-23313: Nvidia Nemo Framework RCE Vulnerability

CVE-2025-23313 Overview

CVE-2025-23313 affects the NVIDIA NeMo Framework across all supported platforms. The vulnerability resides in the Natural Language Processing (NLP) component, where maliciously crafted data can trigger code injection [CWE-94]. A local attacker with low privileges can exploit the flaw without user interaction. Successful exploitation leads to arbitrary code execution, privilege escalation, information disclosure, and data tampering within the affected environment.

Critical Impact
Attackers who supply malicious input to the NeMo NLP pipeline can execute code in the context of the framework, compromising the confidentiality, integrity, and availability of AI/ML workloads and training data.

Affected Products

NVIDIA NeMo Framework (all platforms)
NVIDIA NeMo NLP component
Deployments using vulnerable NeMo releases prior to the NVIDIA security bulletin fix

Discovery Timeline

2025-08-26 - CVE-2025-23313 published to NVD
2026-06-17 - Last updated in NVD database

Technical Details for CVE-2025-23313

Vulnerability Analysis

The NVIDIA NeMo Framework provides tooling for building, training, and fine-tuning generative AI models, including large language models and speech models. The NLP component processes user-supplied data during model training, evaluation, and inference workflows. CVE-2025-23313 arises when this component handles crafted input that is treated as executable content rather than inert data. An attacker with local, low-privileged access to a system running NeMo can supply malicious payloads that the framework interprets and executes.

The flaw is classified as an Improper Control of Generation of Code vulnerability [CWE-94]. Exploitation results in high impact across confidentiality, integrity, and availability. Because AI/ML pipelines frequently run with elevated access to GPUs, model weights, and training datasets, code execution in this context provides attackers with broad access to sensitive assets.

Root Cause

The root cause is insufficient validation and sanitization of attacker-controlled data consumed by the NLP component. When NeMo parses or evaluates this data, it fails to enforce a boundary between untrusted content and interpreted code paths. This allows adversary-supplied strings, configuration fragments, or serialized artifacts to be treated as code during processing.

Attack Vector

The attack vector is local, requiring the attacker to have access to the system or workflow that feeds data into NeMo. Typical scenarios include shared research environments, multi-tenant GPU clusters, and pipelines that ingest datasets, model artifacts, or configuration files from lower-trust locations. No user interaction is required once malicious data enters the processing pipeline.

No verified public exploit code is available. See the NVIDIA Support Advisory for authoritative technical details.

Detection Methods for CVE-2025-23313

Indicators of Compromise

Unexpected child processes spawned by Python interpreters running NeMo training or inference jobs.
Outbound network connections initiated from NeMo worker processes to unknown hosts.
Modification of model checkpoints, tokenizer files, or configuration artifacts outside of scheduled training runs.

Detection Strategies

Monitor NeMo processes for anomalous system calls, file writes outside expected directories, and shell command execution.
Inspect training and inference input data for embedded code constructs, serialized objects, or template expressions.
Correlate GPU workload activity with process lineage to identify code execution that deviates from legitimate ML pipelines.

Monitoring Recommendations

Enable process auditing on hosts running NeMo, capturing command-line arguments and parent-child relationships.
Log and review access to dataset repositories, model registries, and shared storage that feed NeMo pipelines.
Alert on privilege changes or credential access performed by user accounts associated with ML training jobs.

How to Mitigate CVE-2025-23313

Immediate Actions Required

Apply the NVIDIA-provided security update for NeMo Framework as documented in the NVIDIA Support Advisory.
Inventory all systems and containers running NeMo and prioritize patching hosts that process untrusted or third-party data.
Restrict local access to NeMo systems to authorized ML engineers and service accounts.

Patch Information

NVIDIA has published a security bulletin covering CVE-2025-23313. Refer to the NVIDIA Support Advisory for fixed versions and upgrade guidance. Additional record details are available in the NVD entry for CVE-2025-23313 and the CVE.org record.

Workarounds

Validate and sanitize all datasets, configuration files, and model artifacts before ingesting them into NeMo pipelines.
Run NeMo workloads under least-privilege service accounts and within isolated containers or namespaces.
Segregate multi-tenant GPU environments so that untrusted users cannot stage malicious data into shared NLP workflows.
Disable or restrict NeMo features that evaluate user-supplied expressions where feasible until patches are applied.

bash

# Configuration example: run NeMo in a restricted container with read-only inputs
docker run --rm \
  --gpus all \
  --user 10001:10001 \
  --read-only \
  --tmpfs /tmp:rw,size=1g \
  -v /data/datasets:/workspace/data:ro \
  -v /models:/workspace/models:ro \
  --cap-drop=ALL \
  --security-opt no-new-privileges \
  nvcr.io/nvidia/nemo:<patched-version>