CVE-2025-33204: Nvidia Nemo Framework RCE Vulnerability

CVE-2025-33204 Overview

CVE-2025-33204 is a code injection vulnerability [CWE-94] affecting the NVIDIA NeMo Framework across all supported platforms. The flaw resides in the Natural Language Processing (NLP) and Large Language Model (LLM) components. An attacker with local, low-privilege access can supply malicious data that the framework processes unsafely, leading to code execution in the context of the running process.

Successful exploitation can result in arbitrary code execution, privilege escalation, information disclosure, and data tampering. NVIDIA published a security bulletin addressing the issue and released fixed versions of the NeMo Framework.

Critical Impact
An attacker who can deliver crafted input to a NeMo NLP or LLM workflow can execute arbitrary code, escalate privileges, and tamper with model data or training pipelines.

Affected Products

NVIDIA NeMo Framework on Linux
NVIDIA NeMo Framework on Windows
NVIDIA NeMo Framework NLP and LLM components (all platforms prior to the fixed release)

Discovery Timeline

2025-11-25 - CVE-2025-33204 published to the National Vulnerability Database (NVD)
2026-06-17 - Last updated in NVD database

Technical Details for CVE-2025-33204

Vulnerability Analysis

The vulnerability is classified as Improper Control of Generation of Code [CWE-94], commonly referred to as code injection. It exists within the NLP and LLM components of the NeMo Framework, which handle model configuration, tokenization, and training or inference workflows.

The attack vector is local and requires low privileges without user interaction. The scope is unchanged, but confidentiality, integrity, and availability are all rated High because successful exploitation grants the attacker the ability to run arbitrary code as the NeMo process user.

In machine learning pipelines, NeMo processes typically run with access to training datasets, model weights, and sometimes credentials for cloud storage or GPU orchestration systems. Code execution in this context enables theft of proprietary models, poisoning of training data, and lateral movement into adjacent ML infrastructure.

Root Cause

The root cause is unsafe handling of attacker-controlled data within NeMo's NLP and LLM components. According to NVIDIA's advisory, malicious data crafted by an attacker is interpreted in a way that causes code injection rather than being treated as inert input. This pattern is common when frameworks deserialize configuration files, load model artifacts, or evaluate expressions derived from user-supplied fields without strict validation.

Attack Vector

Exploitation requires local access to a system running the NeMo Framework and the ability to place or influence input data consumed by NLP or LLM workflows. A representative scenario involves an attacker with a low-privileged account on a shared GPU workstation or training host who stages a malicious configuration file, dataset, or model artifact. When another user or an automated job loads that data through a vulnerable NeMo component, the injected code executes with the privileges of the loading process.

No public proof-of-concept exploit is available at the time of publication, and the vulnerability is not listed in the CISA Known Exploited Vulnerabilities catalog. The EPSS probability is 0.197%, indicating a low predicted likelihood of exploitation in the short term.

No verified exploit code is publicly available. Refer to the NVIDIA Support Answer for authoritative technical details.

Detection Methods for CVE-2025-33204

Indicators of Compromise

Unexpected child processes spawned by Python interpreters running NeMo training or inference scripts, particularly shells, curl, wget, or compilers.
Modifications to NeMo configuration files (.yaml, .json) or checkpoint files by low-privileged users on shared ML hosts.
Outbound network connections from NeMo worker processes to previously unseen destinations.

Detection Strategies

Monitor process lineage for Python or NeMo entry points spawning interactive shells or code compilers, which is atypical for training workloads.
Audit file integrity of NeMo model artifacts, tokenizer files, and pipeline configuration files stored on shared training infrastructure.
Correlate GPU workload execution logs with unexpected privilege changes or new persistence mechanisms on training hosts.

Monitoring Recommendations

Enable command-line and process-creation logging on all hosts running NeMo, and forward events to a centralized analytics platform.
Alert on read access to sensitive files such as ~/.aws/credentials, ~/.ssh/, or cloud provider tokens from within NeMo process trees.
Track version metadata of the installed NeMo Framework across the fleet to identify unpatched hosts.

How to Mitigate CVE-2025-33204

Immediate Actions Required

Apply the patched NeMo Framework release referenced in the NVIDIA Support Answer as soon as possible.
Inventory all systems running NeMo, including researcher workstations, shared GPU servers, and container images in internal registries.
Restrict local access to training hosts to trusted users only, and revoke unnecessary shell accounts on shared ML infrastructure.

Patch Information

NVIDIA has released a fixed version of the NeMo Framework. Upgrade instructions and the list of affected releases are published in the NVIDIA Security Bulletin (Answer ID 5729). Additional metadata is available in the NVD Vulnerability Detail and the CVE Record for CVE-2025-33204.

Workarounds

Do not load NeMo configuration files, datasets, or model checkpoints from untrusted or shared writable locations until the patch is applied.
Run NeMo workloads inside isolated containers or virtual machines with minimal privileges and no access to sensitive credentials.
Enforce least privilege on filesystem paths used for model artifacts, and use file integrity monitoring on those paths.

bash

# Upgrade NeMo Framework to the fixed release published by NVIDIA
pip install --upgrade nemo_toolkit

# Verify installed version
python -c "import nemo; print(nemo.__version__)"