CVE-2025-33253: Critical Remote Code Execution Flaw Discovered in NVIDIA NeMo Framework — Malicious AI Models Could Trigger System Takeover

CVE-2025-33253 — NVIDIA NeMo Framework Remote Code Execution

CVE: CVE-2025-33253
Product: NVIDIA NeMo Framework (all supported platforms)
Severity: High
CVSS v3.1 Score: 7.8 (High)
Impact: Remote Code Execution (RCE), potential denial of service, information disclosure, data tampering
Exploitability: Low complexity once a malicious model artifact is loaded
Exploit Availability: No widely trusted public exploit code is confirmed for this specific CVE at the time of writing
Official Patch / Upgrade: https://nvidia.custhelp.com/app/answers/detail/a_id/5762

This vulnerability involves how the NeMo Framework handles certain model files or their metadata. When a maliciously crafted file is loaded by a vulnerable NeMo installation, insufficient checks on the embedded data can let attacker-controlled content trigger code paths that were not intended to be executed, effectively letting that content run arbitrary logic inside the host environment.

Overview of the Issue

The NVIDIA NeMo Framework is used by developers and researchers to train and serve machine learning models for language, speech, and multimodal tasks. In CVE-2025-33253, unsafe handling of model files or model metadata can allow an attacker to construct a model artifact that embeds malicious instructions. When unsuspecting users or automated processes load such files into the framework, the unsafe path in model deserialization or metadata parsing can execute unintended code inside the context of the process using NeMo. This could allow commands to run with the permissions of the user or service loading the model, which can lead to complete control of the host, modification of data, denial of service, and potential disclosure of sensitive information.

The underlying weakness is similar to insecure deserialization (classification CWE-502) — untrusted inputs are converted into live program objects without strict validation. In ML frameworks, this often happens when metadata or model structures are implicitly used to construct classes or inject behavior at runtime.

Exploitation Path

To successfully take advantage of this issue:

Crafting a malicious model artifact: An attacker prepares a model file or model metadata that leverages the unsafe deserialization path. This file embeds specially structured data designed to invoke unexpected logic when parsed.
Delivery of the malicious file: The crafted model must reach a system that will load it using the NeMo Framework. This might happen through shared repositories, email attachments, public model hubs, or automation pipelines.
Loading the file on a vulnerable instance: When a developer, tester, or automated job loads this malicious file with the affected NeMo version, the framework processes the embedded data without proper safety checks.
Triggering arbitrary code execution: Once loaded, the malicious content can cause the framework to execute code that performs destructive actions, executes shell commands, installs backdoors, or steals data.

Successful exploitation does not require high privileges on the host; even low-privileged access can be enough if the user or automation pipeline loads the malicious model.

What Happens When an Exploit Succeeds

If an attacker successfully exploits this vulnerability:

Arbitrary code could run as the user or process that loaded the model.
Existing system files and configurations could be altered.
Sensitive data accessible to the process could be read or exfiltrated.
Additional malware could be installed and persist on the device.
Services or hosts could be disrupted or taken offline.

Because GPUs are often used for these workloads, attackers may try to misuse GPU cycles for mining or other resource-heavy tasks that go unnoticed by operators.

Detection Strategies

To detect potential exploitation or attempts to abuse this vulnerability, security teams should consider multiple data sources and correlation logic.

Application and Model Serving Logs

Look for unusual model loading events, such as:

Models loaded from untrusted or unexpected directories.
Sudden loads of multiple large model files by the same user or process.
Model load operations occurring outside of expected workflows or times.

Process Behavior Monitoring

Monitor for suspicious process activity following model loads:

A Python/NeMo process spawning shell interpreters (sh, bash, powershell).
Unexpected downloads initiated by the model loading process.
New processes created immediately after a model load that don’t fit normal patterns.

These can be collected from host audit logs, endpoint detection and response (EDR) telemetry, and system process logs.

System and Security Logs

Correlate system logs with network and application events:

Unusual outbound connections shortly after model loading events.
File system changes in unexpected locations (tmp directories, user profiles).
Alerts from security tools tied to suspicious process trees.

Detection Queries

Below are detection rules that can help identify suspicious activity tied to exploitation attempts. These are generic examples and should be refined to fit your environment.

Endpoint / Process Query (ELK / Splunk style)

index=processes
(process_name="python" OR process_name="python3")
(parent_process="nemo" OR cmdline="*nemo*")
| transaction pid startswith="model_load_start" endswith="model_load_end"
| search (child_process="bash" OR child_process="sh" OR child_process="powershell.exe" OR child_process="curl" OR child_process="wget")
| stats count by host, user, child_process

This finds where a python/NeMo process that loaded a model spawned unexpected child processes like shells or network tools.

File Access + Network Activity Correlation

index=fsaccess OR index=network
(
  (filename="*.nemo" AND action="opened")
  OR
  (process="python" AND dest_port=443 AND user_agent="*python-requests*")
)
| stats earliest(_time) as firstTime latest(_time) as lastTime by host, process
| where lastTime - firstTime < 60

This groups file access to .nemo files with network activity within a short time window, which could signal exploitation.

How to Confirm Exploit Attempts

Review the exact model file that triggered the alerts. Calculate a checksum and store the artifact for offline analysis.
Reproduce the load in an isolated analysis environment to observe behavior.
Capture full process trees, command lines, and spawned child processes immediately surrounding the event.

Preserve logs, artifacts, and memory snapshots because these can be crucial for investigation.

Prevention and Hardening

To reduce risk even beyond patching:

Only accept model artifacts from trusted sources.
Use artifact signing and verification for all model files.
Run model loaders in sandboxed or least-privilege environments (containers with restricted capabilities).
Enforce strict directory and file permission controls to limit where model files can be stored and loaded.
Monitor and log all model loads with rich metadata (user, path, timestamp, model name).

Remediation

The definitive mitigation step is to apply the official patch or upgrade provided by NVIDIA. Fixed versions of the NeMo Framework that address CVE-2025-33253 are detailed in the official security bulletin:

🔗 Official Patch / Advisory: https://nvidia.custhelp.com/app/answers/detail/a_id/5762

Install the updated NeMo release as soon as possible in all environments — development, testing, CI/CD, and production. Keeping framework versions current closes this and many related issues that arise in complex ML toolchains.