CVE-2025-33248 in Megatron LM
Summary
by MITRE • 03/24/2026
NVIDIA Megatron-LM contains a vulnerability in the hybrid conversion script where an Attacker may cause an RCE by convincing a user to load a maliciously crafted file. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data tampering.
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Analysis
by VulDB Data Team • 03/29/2026
The vulnerability identified as CVE-2025-33248 resides within NVIDIA Megatron-LM's hybrid conversion script, representing a critical security flaw that enables remote code execution through social engineering tactics. This issue affects the automated conversion processes used in large language model training and deployment workflows, where the system processes external files without adequate validation mechanisms. The vulnerability stems from insufficient input sanitization and validation within the conversion pipeline, creating an attack surface where maliciously crafted files can trigger unintended code execution. Attackers can exploit this weakness by crafting specially designed input files that, when processed by the hybrid conversion script, execute arbitrary commands with the privileges of the user running the conversion utility.
The technical implementation of this vulnerability aligns with CWE-74 and CWE-94, which address injection flaws and code injection vulnerabilities respectively. The hybrid conversion script likely employs Python's pickle module or similar serialization mechanisms that can execute arbitrary code during deserialization processes. This pattern creates a direct pathway for attackers to inject malicious payloads that bypass normal execution boundaries. The ATT&CK framework categorizes this as a technique involving code injection and privilege escalation, where the initial compromise can lead to full system control. The vulnerability is particularly dangerous because it operates within legitimate system workflows, making detection more challenging and allowing attackers to leverage existing trust relationships within the machine learning infrastructure.
Operational impact of this vulnerability extends beyond simple code execution to encompass complete system compromise and data integrity violations. Successful exploitation allows attackers to escalate privileges from standard user accounts to root or administrative levels, depending on the execution context. The converted model files may become corrupted or manipulated during the attack, leading to data tampering that can compromise the entire machine learning pipeline. Information disclosure becomes possible as attackers can extract sensitive data from memory, configuration files, or model weights. Organizations using NVIDIA Megatron-LM for production deployments face significant risks, particularly in environments where multiple users have access to the conversion utilities or where automated pipelines process untrusted input from external sources.
Mitigation strategies must address both immediate protection and long-term architectural improvements. The primary recommendation involves implementing strict input validation and sanitization within the hybrid conversion script, eliminating the use of dangerous deserialization methods such as pickle.load(). Organizations should deploy principle of least privilege access controls, ensuring that conversion processes run with minimal required permissions and isolated execution environments. Network segmentation and monitoring solutions should detect unusual file processing activities, particularly those involving model conversion workflows. Regular security updates and patch management procedures must be established to address similar vulnerabilities in related components. Additionally, implementing automated code analysis tools and static application security testing can help identify similar injection vulnerabilities in other parts of the machine learning infrastructure. The solution should also incorporate runtime application self-protection mechanisms that can detect and prevent malicious code execution attempts during file processing operations.