CVE-2025-33253: Nvidia Nemo Framework RCE Vulnerability

CVE-2025-33253 Overview

CVE-2025-33253 is a deserialization vulnerability [CWE-502] affecting the NVIDIA NeMo Framework. An attacker can achieve remote code execution by convincing a user to load a maliciously crafted file. Successful exploitation can lead to code execution, denial of service, information disclosure, and data tampering on the affected host.

The issue requires user interaction and local access with low privileges, making it relevant to data scientists and machine learning engineers who routinely load third-party model files, checkpoints, or configuration artifacts. NVIDIA has published a security advisory addressing the issue.

Critical Impact
Loading an attacker-supplied file in NVIDIA NeMo can result in arbitrary code execution, full compromise of confidentiality and integrity, and disruption of AI/ML workloads.

Affected Products

NVIDIA NeMo Framework (all versions prior to the fixed release referenced in NVIDIA advisory ID 5762)
Deployments on Linux hosts running NeMo training or inference workflows
Workstations and servers used to load third-party NeMo checkpoints or model artifacts

Discovery Timeline

2026-02-18 - CVE-2025-33253 published to NVD
2026-02-20 - Last updated in NVD database

Technical Details for CVE-2025-33253

Vulnerability Analysis

The vulnerability falls under CWE-502: Deserialization of Untrusted Data. NVIDIA NeMo processes user-supplied files during normal model and data loading operations. When the framework deserializes a maliciously crafted file, embedded payloads are executed in the context of the user running NeMo.

The attack vector is Local and requires user interaction. An attacker does not need network access to the target. Instead, the attacker stages a malicious artifact (for example, a model checkpoint or configuration file) and convinces a victim to load it inside NeMo. Successful exploitation impacts confidentiality, integrity, and availability of the host.

The issue carries an EPSS probability of 0.126%, reflecting limited observed exploitation activity at the time of publication. No public proof-of-concept exploit is currently listed.

Root Cause

The root cause is unsafe deserialization of untrusted input within the NeMo Framework. Python-based ML frameworks commonly rely on serialization formats that can invoke arbitrary callables during object reconstruction. When NeMo deserializes an attacker-controlled file without strict validation, malicious objects can trigger code execution as a side effect of loading.

Attack Vector

Exploitation follows a social-engineering-driven workflow typical of supply chain attacks on AI/ML pipelines. The attacker publishes or shares a weaponized NeMo-compatible file through a model hub, a shared storage location, or a phishing message. The victim loads the file using standard NeMo loading routines, and the embedded payload executes with the victim's privileges.

No verified public exploit code is available. Technical details should be consulted in the NVIDIA Support Answer and the NVD CVE-2025-33253 Details.

Detection Methods for CVE-2025-33253

Indicators of Compromise

Unexpected child processes spawned by Python interpreters running NeMo workloads, such as shells, curl, wget, or compilers
NeMo processes initiating outbound network connections to unknown hosts shortly after a model load operation
Newly created files, cron jobs, or systemd units in user home directories following the load of an externally sourced checkpoint
Modifications to shell startup scripts (.bashrc, .profile) by NeMo or Python processes

Detection Strategies

Monitor process lineage where the parent is a Python interpreter executing NeMo modules and the child is an interactive shell or scripting tool.
Alert on file reads of .nemo, .ckpt, or pickle-based artifacts originating from non-trusted directories such as /tmp, downloads folders, or removable media.
Correlate model loading activity with subsequent privilege-sensitive operations, including credential file access and SSH key reads.

Monitoring Recommendations

Enable command-line auditing on hosts running NeMo and forward telemetry to a centralized SIEM for retrospective hunting.
Inventory all NeMo installations and the source of every model artifact loaded by data science teams.
Track outbound network traffic from ML workstations and training nodes to detect post-exploitation command-and-control activity.

How to Mitigate CVE-2025-33253

Immediate Actions Required

Upgrade NVIDIA NeMo Framework to the fixed version listed in the NVIDIA Support Answer.
Audit all NeMo checkpoints and model files currently in use, and remove artifacts obtained from untrusted sources.
Restrict the user accounts permitted to load arbitrary model files on shared training infrastructure.
Educate ML engineers and data scientists about the risk of loading untrusted serialized files.

Patch Information

NVIDIA has issued a security bulletin addressing CVE-2025-33253. Refer to the NVIDIA Support Answer for the specific fixed version of NeMo and upgrade instructions. Validate the upgrade in a non-production environment before rolling it out to GPU clusters and training nodes.

Workarounds

Load NeMo files only from cryptographically verified or internally produced sources, and reject artifacts whose provenance cannot be established.
Run NeMo workloads under a dedicated low-privilege service account with no access to credentials, secrets, or production data shares.
Execute model evaluation of third-party artifacts inside isolated containers or virtual machines with no outbound network access.
Apply mandatory access controls (AppArmor or SELinux profiles) to restrict the file system and network capabilities of Python processes running NeMo.

bash

# Configuration example: upgrade NeMo and verify the installed version
pip install --upgrade nemo_toolkit
python -c "import nemo; print(nemo.__version__)"

# Run NeMo inside an isolated, network-restricted container
docker run --rm -it \
  --network=none \
  --read-only \
  --tmpfs /tmp \
  --user 1000:1000 \
  -v /srv/models/trusted:/models:ro \
  nvcr.io/nvidia/nemo:<fixed-version> \
  python validate_model.py /models/checkpoint.nemo