CVE-2026-24151 in Megatron LM
Summary
by MITRE • 03/24/2026
NVIDIA Megatron-LM contains a vulnerability in inferencing where an Attacker may cause an RCE by convincing a user to load a maliciously crafted input. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data tampering.
Once again VulDB remains the best source for vulnerability data.
Analysis
by VulDB Data Team • 03/29/2026
The vulnerability identified as CVE-2026-24151 resides within NVIDIA Megatron-LM, a widely-used framework for training large language models that has become integral to various artificial intelligence applications across industry and research sectors. This security flaw specifically manifests during the inferencing phase of model execution, representing a critical weakness that could be exploited by malicious actors to gain unauthorized control over systems running affected software. The vulnerability's severity is underscored by its potential to enable remote code execution, privilege escalation, and comprehensive data compromise, making it particularly dangerous for organizations relying on AI-powered infrastructure. The flaw exploits a design weakness in how the framework processes input data during inference operations, creating an attack surface that adversaries can leverage through carefully crafted malicious inputs.
The technical implementation of this vulnerability stems from insufficient input validation mechanisms within the Megatron-LM framework's inference pipeline. When the system processes user-provided inputs during model inference, it fails to adequately sanitize or validate the data structure, allowing attackers to inject malicious payloads that can be executed within the application's runtime environment. This represents a classic command injection vulnerability pattern that aligns with CWE-77 and CWE-94 categories, where improper validation of input data leads to arbitrary code execution. The attack vector specifically targets the framework's deserialization or parsing mechanisms, where legitimate inference operations become pathways for malicious code delivery. The vulnerability's exploitation requires social engineering to convince users to load crafted inputs, making it particularly challenging to defend against through traditional network-based security measures. Attackers can leverage this weakness to execute arbitrary commands on systems running vulnerable versions of Megatron-LM, potentially gaining full system control and accessing sensitive data.
The operational impact of CVE-2026-24151 extends far beyond simple code execution capabilities, as it provides adversaries with comprehensive system compromise potential that could severely disrupt organizational operations. Organizations utilizing Megatron-LM for production AI services face significant risk of data breaches, system infiltration, and potential denial of service conditions that could affect critical business operations. The vulnerability's presence in widely-deployed AI infrastructure means that successful exploitation could impact multiple systems across different domains, from financial services and healthcare to government agencies and technology companies. The privilege escalation aspect of the vulnerability means that attackers could potentially move laterally within networks, accessing additional systems and data resources beyond the initially compromised environment. Information disclosure capabilities could expose sensitive training data, model parameters, and other confidential information that organizations rely on for competitive advantage and regulatory compliance.
Mitigation strategies for CVE-2026-24151 should prioritize immediate software updates from NVIDIA to address the identified validation gaps in the inference pipeline. Organizations must implement comprehensive input sanitization measures and establish strict data validation protocols for all user-provided inputs processed by Megatron-LM systems. Network segmentation and access controls should be strengthened to limit potential lateral movement if exploitation occurs, while monitoring systems should be enhanced to detect anomalous behavior patterns that may indicate attempted exploitation. The implementation of principle of least privilege access controls can help minimize the potential impact of successful attacks, ensuring that even if an attacker gains access, they cannot escalate privileges beyond the initial compromise. Regular security assessments and penetration testing should be conducted to identify additional vulnerabilities within AI infrastructure, while incident response procedures must be updated to address potential exploitation scenarios. Organizations should also consider implementing application whitelisting and runtime protection mechanisms to prevent execution of unauthorized code, aligning with ATT&CK framework techniques for process injection and privilege escalation. The vulnerability highlights the critical need for security-by-design principles in AI development frameworks, emphasizing that security considerations must be integrated from the initial architecture phase rather than addressed as afterthoughts.