The vulnerabilities within the NVIDIA NeMo Framework allow remote code execution through flaws in deserialization, path validation, and code generation controls. Attackers can exploit these issues to inject malicious code, overwrite critical files, or hijack AI training pipelines, undermining model integrity. Data tampering might corrupt sensitive datasets, introduce backdoors, or degrade AI performance, leading to erroneous outputs. Organizations relying on NeMo for large language models, multimodal deep learning, and speech recognition could face prolonged downtime while investigating breaches, retraining models, and restoring clean datasets. Business-critical applications such as customer support chatbots, autonomous systems, and internal analytics tools risk operational failures, resulting in revenue loss, regulatory scrutiny, and reputational damage. The broad cross-platform exposure on Windows, Linux, and macOS further amplifies the threat surface. Despite immediate patches in version 25.02, any delayed update adoption leaves systems vulnerable to advanced persistent threats. Long-term consequences include erosion of stakeholder trust, increased defense expenditures, and potential legal liabilities if corrupted or poisoned models influence downstream applications. The intertwined nature of AI workflows means a single exploit can cascade across multiple projects, imperiling fundamental research and enterprise deployments.
Source: https://cybersecuritynews.com/nvidia-nemo-framework-vulnerability/
"id": "job741042525",
"linkid": "jobs",
"type": "Vulnerability",
"date": "4/2025",
"severity": "100",
"impact": "5",
"explanation": "Attack threatening the organization’s existence"