CVE-2025-33240 in Megatron-Bridge
Summary
by MITRE • 02/18/2026
NVIDIA Megatron Bridge contains a vulnerability in a data shuffling tutorial, where malicious input could cause a code injection. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure, and data tampering.
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Analysis
by VulDB Data Team • 02/18/2026
The vulnerability identified as CVE-2025-33240 resides within NVIDIA Megatron Bridge's data shuffling tutorial component, representing a critical security flaw that could enable remote code execution through malicious input manipulation. This issue specifically affects the educational and demonstration materials provided by NVIDIA for their Megatron Bridge framework, which is designed to facilitate large-scale language model training and deployment. The vulnerability stems from insufficient input validation and sanitization mechanisms within the tutorial code that processes user-provided data during the shuffling operations. The affected system components are particularly concerning as they are intended for educational purposes and may be accessed by developers, researchers, and practitioners who could inadvertently execute malicious payloads through seemingly benign tutorial interactions.
The technical root cause of this vulnerability aligns with CWE-94, which describes improper control of generation of code, and specifically manifests as a code injection flaw within the data processing pipeline. The vulnerability occurs when user-supplied parameters are directly incorporated into executable code without proper sanitization or validation, allowing attackers to inject malicious code that gets executed within the tutorial environment. This type of vulnerability is particularly dangerous because it can be exploited through legitimate tutorial usage patterns, making detection and prevention challenging. The data shuffling operations in Megatron Bridge typically involve complex data transformations and parameter handling that create multiple potential injection points where attacker-controlled input can be seamlessly integrated into the execution flow.
The operational impact of this vulnerability extends beyond simple code execution to encompass privilege escalation, information disclosure, and data tampering capabilities. An attacker who successfully exploits this vulnerability could gain elevated privileges within the tutorial environment, potentially allowing access to sensitive system resources or data. The information disclosure aspect poses significant risks as the vulnerability could expose internal system configurations, training data, or other confidential information that might be processed during the data shuffling operations. Data tampering capabilities mean that attackers could modify or corrupt training datasets, potentially compromising the integrity of machine learning models that rely on the affected framework. The attack surface is particularly concerning given that the tutorial environment is often used in research and development settings where sensitive data and intellectual property may be present.
Mitigation strategies for this vulnerability should include immediate input validation and sanitization of all user-provided parameters within the tutorial code, implementing proper code execution boundaries, and employing secure coding practices that prevent dynamic code generation from external inputs. Organizations should ensure that all tutorial environments are isolated and restricted from accessing sensitive systems or data, and that regular security assessments are conducted on educational materials. The remediation process should involve comprehensive code review focusing on input handling, parameter validation, and code generation practices. Additionally, implementing runtime protection mechanisms such as sandboxing and privilege separation can significantly reduce the potential impact of successful exploitation attempts. Security teams should also consider the ATT&CK framework's T1059.001 technique related to command and script injection when planning defensive measures, as this vulnerability directly enables such attack vectors through the code injection mechanism.