CVE-2025-33245 in NeMo Framework
Summary
by MITRE • 02/18/2026
NVIDIA NeMo Framework contains a vulnerability where malicious data 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 • 02/21/2026
The NVIDIA NeMo Framework vulnerability identified as CVE-2025-33245 represents a critical remote code execution flaw that exposes organizations utilizing this machine learning toolkit to significant security risks. This framework serves as a comprehensive platform for developing and deploying conversational AI models, making it a prime target for adversaries seeking to compromise AI infrastructure. The vulnerability stems from insufficient input validation mechanisms within the framework's data processing pipelines, particularly when handling user-supplied data streams. Attackers can craft malicious inputs that exploit memory corruption vulnerabilities during data deserialization processes, potentially leading to arbitrary code execution on systems running affected versions of the NeMo Framework.
The technical implementation of this vulnerability aligns with CWE-121, which describes stack-based buffer overflow conditions, and CWE-787, which addresses out-of-bounds write vulnerabilities. The flaw manifests when the framework processes structured data formats such as json or yaml configurations that contain crafted malicious payloads. These payloads can manipulate memory layout during parsing operations, allowing attackers to overwrite critical program variables or function pointers. The vulnerability's remote exploitability means that attackers do not require physical access to target systems, as they can leverage network-based attack vectors through API endpoints or configuration interfaces that process external data inputs. This characteristic places the vulnerability squarely within the ATT&CK matrix domain of TA0040 (TA0040: Execution) and TA0002 (TA0002: Execution) where adversaries can establish persistent access through remote code execution capabilities.
The operational impact of CVE-2025-33245 extends beyond immediate code execution privileges to encompass broader system compromise capabilities. Successful exploitation can lead to privilege escalation attacks that allow attackers to gain administrative access to underlying infrastructure hosting the NeMo Framework. This escalation capability significantly amplifies the potential damage, as attackers can manipulate data flows, exfiltrate sensitive training datasets, or corrupt model parameters that could affect downstream applications. Organizations relying on NeMo Framework for enterprise AI deployments face substantial risks including intellectual property theft, data integrity compromise, and service disruption. The vulnerability's presence in conversational AI systems also raises concerns about potential manipulation of chatbots and virtual assistants, which could be used for social engineering attacks or information gathering operations.
Mitigation strategies for this vulnerability require immediate patching of affected NeMo Framework versions, with security teams implementing comprehensive network segmentation to isolate AI infrastructure from critical business systems. Organizations should deploy input validation controls at multiple layers, including API gateways and data processing pipelines, to filter malicious payloads before they reach the framework's core components. The implementation of runtime protection mechanisms such as address space layout randomization and stack canaries can help detect and prevent exploitation attempts. Security monitoring should focus on anomalous API access patterns, unusual data processing activities, and unexpected privilege escalation events. Additionally, organizations should conduct thorough vulnerability assessments of their AI infrastructure to identify similar vulnerabilities in related frameworks and dependencies, as the attack surface extends beyond the primary framework to include associated libraries and deployment environments. Regular security updates and adherence to secure coding practices in AI development processes are essential for preventing similar vulnerabilities in future implementations.