CVE-2025-23313 in NeMo Framework
Summary
by MITRE • 08/26/2025
NVIDIA NeMo Framework for all platforms contains a vulnerability in the NLP component, where malicious data created by an attacker could cause a code injection issue. 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 • 08/29/2025
The vulnerability identified as CVE-2025-23313 affects the NVIDIA NeMo Framework across all supported platforms and specifically targets the natural language processing component. This framework serves as a comprehensive toolkit for developing and deploying large language models and other NLP applications in enterprise environments. The flaw resides within the data processing mechanisms of the NLP module, where insufficient input validation allows maliciously crafted data to potentially trigger unintended code execution behaviors. The vulnerability represents a critical security risk as it could be exploited by threat actors to gain unauthorized access to systems running the affected framework.
The technical implementation of this vulnerability stems from inadequate sanitization and validation of user-provided data within the NLP processing pipeline. When the framework processes input text or other data elements, the absence of proper boundary checks and input filtering creates opportunities for attackers to inject malicious payloads that can be executed within the application context. This type of vulnerability aligns with CWE-94, which describes "Improper Control of Generation of Code" and falls under the broader category of code injection flaws that have been consistently identified as high-risk security concerns in software development practices. The attack surface is particularly concerning given that NLP frameworks often process untrusted data from multiple sources including user inputs, external APIs, and data feeds.
The operational impact of this vulnerability extends beyond simple code execution capabilities to encompass a comprehensive range of security implications. Successful exploitation could enable attackers to escalate privileges within the affected system, potentially gaining administrative access to underlying infrastructure. Information disclosure represents another serious consequence as attackers might extract sensitive data, model parameters, or other confidential information processed by the framework. Additionally, the vulnerability creates opportunities for data tampering, where malicious actors could modify input data or system outputs to compromise the integrity of NLP models and their generated results. This capability could be particularly damaging in environments where model accuracy and data integrity are paramount, such as financial services, healthcare applications, or security monitoring systems.
Organizations utilizing the NVIDIA NeMo Framework should implement immediate mitigations to address this vulnerability. The primary recommendation involves deploying input validation layers that sanitize all data processed by the NLP components before they are parsed by the framework. This approach aligns with ATT&CK technique T1059.007 for command and scripting interpreter, which emphasizes the importance of preventing code injection through proper input sanitization. System administrators should also consider implementing network segmentation and access controls to limit exposure of affected systems. Regular security updates and patches from NVIDIA should be prioritized, while organizations may need to conduct thorough vulnerability assessments of their NLP applications to identify potential exploitation vectors. Monitoring for anomalous behavior in NLP processing activities and implementing robust logging mechanisms will help detect potential exploitation attempts and support incident response activities. The vulnerability also highlights the importance of secure coding practices and input validation in machine learning frameworks, particularly those handling sensitive data in enterprise environments.