CVE-2025-23354 in Megatron-LM
Summary
by MITRE • 09/24/2025
NVIDIA Megatron-LM for all platforms contains a vulnerability in the ensemble_classifer script where malicious data created by an attacker may cause an injection. 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 • 09/24/2025
The vulnerability identified as CVE-2025-23354 resides within NVIDIA Megatron-LM's ensemble_classifer script across all supported platforms, representing a critical security flaw that undermines the integrity of machine learning workflows. This vulnerability stems from insufficient input validation mechanisms within the ensemble classification processing pipeline, where attacker-controlled data can potentially manipulate the script's execution flow through code injection techniques. The affected component processes ensemble predictions from multiple models and aggregates them into final classifications, creating a potential attack surface where malicious inputs can be interpreted as executable code rather than mere data.
The technical exploitation of this vulnerability follows a classic injection attack pattern that aligns with CWE-94, which describes the execution of arbitrary code due to inadequate input sanitization. When the ensemble_classifer script processes data containing malicious payloads, the lack of proper validation allows attacker-controlled code to be executed within the context of the running process. This flaw particularly affects the script's handling of configuration parameters, model weights, or prediction results that are not adequately sanitized before being processed. The vulnerability's impact extends beyond simple code execution to encompass privilege escalation opportunities, as the compromised process may operate with elevated permissions necessary for system-level operations.
Operational consequences of this vulnerability are severe and multifaceted, potentially enabling attackers to gain full control over affected systems running NVIDIA Megatron-LM implementations. The information disclosure aspect allows adversaries to access sensitive model parameters, training data, or prediction results that could compromise intellectual property or confidential business information. Data tampering capabilities further amplify the threat by enabling attackers to corrupt model outputs, manipulate training datasets, or introduce backdoors into machine learning pipelines. This vulnerability particularly impacts organizations using Megatron-LM for sensitive applications such as financial fraud detection, healthcare diagnostics, or autonomous systems where manipulated model outputs could have catastrophic real-world consequences.
Mitigation strategies should prioritize immediate input validation and sanitization measures within the ensemble_classifer script, implementing strict parameter validation and sanitization routines that align with industry best practices for secure coding. Organizations should consider adopting defensive programming techniques including input whitelisting, proper data type checking, and comprehensive error handling to prevent injection attacks. The implementation of principle of least privilege access controls and process isolation mechanisms can limit the potential damage from successful exploitation attempts. Additionally, regular security assessments and code reviews focusing on the ensemble_classifer component should be conducted to identify similar vulnerabilities in other parts of the Megatron-LM framework. Organizations utilizing NVIDIA Megatron-LM should also monitor for official patches and updates from NVIDIA while implementing network segmentation and monitoring to detect potential exploitation attempts. This vulnerability demonstrates the critical importance of secure input handling in machine learning environments and aligns with ATT&CK technique T1059 for command and scripting interpreter, highlighting the need for comprehensive security measures throughout the entire machine learning pipeline lifecycle.