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NVIDIA NeMo Framework CVE-2026-24155

| EUVDEUVD-2026-37129 HIGH
Code Injection (CWE-94)
2026-06-16 nvidia GHSA-xp23-6mrm-wqh3
7.8
CVSS 3.1 · Vendor: nvidia
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Severity by source

Vendor (nvidia) PRIMARY
7.8 HIGH
AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
vuln.today AI
7.8 HIGH

Local attacker with low-privileged shell on a NeMo host supplies a crafted artifact NeMo evaluates, yielding code execution with full CIA impact under the NeMo process; no UI or scope change.

3.1 AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
4.0 AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

Primary rating from Vendor (nvidia).

CVSS VectorVendor: nvidia

CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Attack Vector
Local
Attack Complexity
Low
Privileges Required
Low
User Interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

Lifecycle Timeline

1
Analysis Generated
Jun 16, 2026 - 17:17 vuln.today

DescriptionCVE.org

NVIDIA NeMo Framework for all platforms contains a code injection vulnerability. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure, and data tampering.

AnalysisAI

Code injection in NVIDIA NeMo Framework across all supported platforms allows a local attacker with low privileges to execute arbitrary code, escalate privileges, disclose sensitive information, and tamper with data. The flaw carries a CVSS 3.1 score of 7.8 with high impact across confidentiality, integrity, and availability, though no public exploit identified at time of analysis and the issue is not listed in CISA KEV.

Technical ContextAI

NVIDIA NeMo Framework is an end-to-end, cloud-native toolkit used by researchers and enterprises to build, customize, and deploy generative AI and large language models, often executed in Python-based training and inference pipelines on GPU-equipped hosts. The CPE 'cpe:2.3:a:nvidia:nemo_framework:*' indicates all versions are in scope pending vendor clarification. The root cause is CWE-94 (Improper Control of Generation of Code, 'Code Injection'), meaning attacker-controlled input is incorporated into something the framework subsequently evaluates or executes - typical patterns in ML frameworks include unsafe deserialization of model artifacts, dynamic import/eval of configuration files (e.g., Hydra/OmegaConf, YAML, pickle), or template expansion in training recipes.

RemediationAI

Patch available per vendor advisory - consult the NVIDIA security bulletin at https://nvidia.custhelp.com/app/answers/detail/a_id/5839 for the exact fixed NeMo Framework version and upgrade all training, fine-tuning, and inference environments accordingly; the NVD record at https://nvd.nist.gov/vuln/detail/CVE-2026-24155 should be monitored for additional CPE/version metadata. Until upgraded, treat NeMo as an untrusted code execution surface: restrict who can submit configs, recipes, and model artifacts to NeMo pipelines, run NeMo workloads under dedicated non-privileged service accounts with minimal filesystem and network egress, and avoid loading model checkpoints, YAML/Hydra configs, or pickled artifacts from untrusted sources (the trade-off is operational friction for data scientists who routinely pull community models from hubs). Adding mandatory provenance and integrity checks (signatures or hash allowlists) on ingested model and config files reduces exposure but requires pipeline changes.

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CVE-2026-24155 vulnerability details – vuln.today

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