Double Injection Risk in NVIDIA Megatron-LM: Code Execution Flaws Patched in v0.12.1
Ddos 
NVIDIA has released a security bulletin addressing two newly discovered vulnerabilities—CVE-2025-23264 and CVE-2025-23265—affecting Megatron-LM, its open-source large language model (LLM) framework designed for training transformer-based neural networks. These flaws, both rated 7.8 (High) on the CVSS scale, stem from insecure handling of input in a Python component and could allow remote code execution, privilege escalation, and data tampering across any platform.
“A successful exploit of this vulnerability may lead to Code Execution, Escalation of Privileges, Information Disclosure and Data Tampering,” NVIDIA warned in the advisory.
According to the bulletin, both vulnerabilities arise from insecure code injection pathways in a Python component of Megatron-LM. While the bulletin doesn’t specify whether these flaws exist in a particular function or configuration, it highlights that the issue can be triggered by simply providing a malicious file to the system—an especially alarming attack vector for environments that support automated model loading or dynamic pipeline configuration.
These vulnerabilities affect all Megatron-LM versions prior to 0.12.0 and are resolved in version 0.12.1. NVIDIA’s security team has issued a unified fix for both CVEs in this update.
As LLMs are increasingly integrated into enterprise AI applications, their frameworks and training infrastructure have become critical parts of the software supply chain. Megatron-LM is often deployed in high-performance computing (HPC) and research environments, where data integrity, model confidentiality, and secure infrastructure are paramount.
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