EUVD-2025-19044CVSS VectorNVD
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
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3DescriptionNVD
NVIDIA Megatron-LM for all platforms contains a vulnerability in a python component where an attacker may cause a code injection issue by providing a malicious file. A successful exploit of this vulnerability may lead to Code Execution, Escalation of Privileges, Information Disclosure and Data Tampering.
AnalysisAI
CVE-2025-23265 is a code injection vulnerability in NVIDIA Megatron-LM's Python component that allows local attackers with low privileges to execute arbitrary code by providing a malicious file. Successful exploitation enables code execution, privilege escalation, information disclosure, and data tampering. This vulnerability affects all platforms running Megatron-LM and poses significant risk to machine learning infrastructure, particularly in multi-tenant or shared compute environments.
Technical ContextAI
NVIDIA Megatron-LM is a large-scale language model training framework that processes Python files and configurations during model initialization and execution. The vulnerability resides in a Python component (likely related to configuration loading, model serialization, or dynamic code execution) and is classified as CWE-94 (Improper Control of Generation of Code), indicating unsafe deserialization, eval-like functions, or dynamic code generation from untrusted input. The vulnerability stems from insufficient validation of file contents before processing, allowing attackers to inject arbitrary Python code through crafted files that are subsequently executed within the Megatron-LM process context. This affects all platform variants (Linux, cloud deployments, on-premises installations) where Megatron-LM is deployed.
RemediationAI
Immediate actions: (1) Consult NVIDIA security advisory (referenced in CVE documentation) for patched versions—apply patches prioritizing production ML training clusters; (2) Implement file access controls restricting which users can provide configuration/model files to Megatron-LM processes; (3) Run Megatron-LM processes with minimal required privileges (non-root service account with restricted file system access); (4) Validate and sanitize all external file inputs before processing—implement file format verification and signatures; (5) Use containerization (Docker/Kubernetes) with read-only file systems and network policies to limit blast radius. Longer-term: (1) Update to patched Megatron-LM versions once available from NVIDIA; (2) Implement input validation in upstream code for all file handling operations; (3) Replace dynamic code execution patterns (if applicable) with safe alternatives; (4) Deploy runtime monitoring to detect suspicious Python code execution patterns.
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