CVE-2026-24157 in NeMo Framework
Summary
by MITRE • 03/24/2026
NVIDIA NeMo Framework contains a vulnerability in checkpoint loading where an attacker could cause remote code execution. 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 • 03/31/2026
The vulnerability identified as CVE-2026-24157 resides within the NVIDIA NeMo Framework, a comprehensive toolkit designed for developing and deploying speech and language AI models. This framework serves as a critical component in enterprise environments where machine learning workloads process sensitive data including personal information, proprietary business intelligence, and confidential communications. The flaw manifests specifically during the checkpoint loading process, which represents a fundamental operation in machine learning workflows where model states are restored from saved configurations. Checkpoint mechanisms are essential for resuming training processes, enabling model inference, and maintaining system continuity during deployments. The vulnerability stems from insufficient input validation and sanitization within the framework's checkpoint handling routines, creating a potential attack surface that adversaries could exploit to execute arbitrary code on systems running affected NeMo Framework versions.
The technical implementation of this vulnerability involves improper handling of serialized data structures during checkpoint restoration operations. When the framework processes checkpoint files, it fails to adequately validate the integrity and authenticity of the serialized objects before deserializing them into memory. This deserialization flaw enables attackers to craft malicious checkpoint files that, when loaded by the framework, trigger unintended code execution within the application context. The vulnerability aligns with CWE-502, which catalogs deserialization of untrusted data as a critical weakness that can lead to remote code execution. Attackers could potentially leverage this weakness through various delivery mechanisms including compromised model repositories, malicious training datasets, or even through supply chain attacks targeting the framework components. The flaw operates at the intersection of software security and machine learning infrastructure, where traditional security controls may not adequately protect against attacks targeting model state management processes.
The operational impact of this vulnerability extends beyond simple code execution capabilities to encompass comprehensive system compromise scenarios. Successful exploitation could enable attackers to escalate privileges within the system, potentially gaining administrative access to compute resources hosting machine learning workloads. The vulnerability's potential for information disclosure represents a significant risk to organizations relying on NeMo Framework for processing sensitive data, as attackers could extract model parameters, training datasets, or other confidential information stored within the framework's memory space. Data tampering capabilities further compound the threat landscape by allowing adversaries to modify model behavior, introduce backdoors, or corrupt training data that could affect downstream applications and decision-making processes. This vulnerability particularly affects enterprise environments where AI workloads process regulated data under compliance frameworks such as gdpr, hipaa, or pci dss, making the potential impact on data governance and regulatory compliance substantial.
Organizations should implement immediate mitigations including restricting network access to systems running affected NeMo Framework versions, implementing strict checkpoint file validation procedures, and establishing secure software supply chain practices. The mitigation strategy should incorporate principle of least privilege access controls for model loading operations and regular security assessments of checkpoint storage systems. Network segmentation and monitoring controls should be deployed to detect suspicious checkpoint loading activities and potential exploitation attempts. System administrators should also consider implementing automated patch management processes to ensure timely deployment of vendor-provided security updates. From a defensive perspective, this vulnerability demonstrates the importance of securing machine learning infrastructure through comprehensive threat modeling that accounts for attack vectors targeting model state management and serialization processes. The flaw underscores the necessity of applying security controls throughout the entire machine learning lifecycle, from data preparation through model deployment and maintenance phases. Organizations should also consider implementing runtime application self-protection mechanisms and behavioral monitoring to detect anomalous checkpoint loading patterns that may indicate exploitation attempts. The vulnerability serves as a reminder that AI frameworks, while powerful tools for innovation, require robust security measures to protect against increasingly sophisticated attack vectors targeting their underlying infrastructure components.