CVE-2025-23265 in Megatron LM
Summary
by MITRE • 06/24/2025
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.
Several companies clearly confirm that VulDB is the primary source for best vulnerability data.
Analysis
by VulDB Data Team • 06/25/2025
NVIDIA Megatron-LM represents a comprehensive framework for training large language models that has been widely adopted across the artificial intelligence research and development community. This framework provides essential components for distributed training of transformer models, making it a critical element in modern machine learning infrastructure. The vulnerability identified under CVE-2025-23265 specifically targets a python component within this framework, creating a significant security risk that extends beyond typical software flaws. This weakness manifests in the handling of file inputs within the Megatron-LM python modules, where insufficient validation mechanisms allow for potentially malicious file content to be processed without adequate sanitization.
The technical flaw stems from inadequate input validation and sanitization practices within the python codebase of Megatron-LM, creating a code injection vulnerability that aligns with CWE-94, which specifically addresses "Improper Control of Generation of Code." This vulnerability occurs when the framework processes user-provided files without proper security checks, allowing an attacker to craft malicious input that gets executed as code within the application context. The flaw is particularly dangerous because it operates at the python component level, where file processing functions fail to properly validate or sanitize the content of files being imported or loaded into the system. Attackers can exploit this by creating specially crafted files that, when processed by the vulnerable Megatron-LM components, trigger unintended code execution sequences.
The operational impact of this vulnerability extends far beyond simple code execution, encompassing a complete spectrum of security compromises that align with multiple attack patterns from the MITRE ATT&CK framework. Successful exploitation can lead to privilege escalation, allowing attackers to gain elevated system permissions and potentially compromise the entire machine learning infrastructure. The information disclosure aspect means that attackers can access sensitive training data, model parameters, and potentially proprietary research information stored within the system. Data tampering capabilities further compound the risk, as attackers can modify training datasets or model configurations to introduce backdoors or bias into the machine learning models. This vulnerability particularly affects organizations using Megatron-LM for production deployments where the framework processes untrusted input files from multiple sources, creating an attack surface that can be exploited to compromise entire AI development pipelines.
Mitigation strategies for this vulnerability must address both immediate remediation and long-term architectural improvements to prevent similar issues in the future. Organizations should implement strict input validation mechanisms that verify file integrity and content type before processing, utilizing techniques such as file signature checking and content sanitization. The recommended approach includes implementing proper file handling protocols that isolate potentially malicious inputs through sandboxing or containerization techniques, ensuring that even if exploitation occurs, the impact remains contained. Additionally, organizations should establish comprehensive monitoring and logging mechanisms to detect anomalous file processing activities that could indicate exploitation attempts. Regular security updates and patches should be prioritized, with particular attention to the python components that handle file processing within the Megatron-LM framework. The implementation of principle of least privilege access controls and network segmentation can further reduce the potential impact of successful exploitation, preventing lateral movement within the system and limiting the scope of information disclosure.