Three high-severity vulnerabilities have been discovered in the NVIDIA NeMo Framework CVE-2025-23249, CVE-2025-23250, and CVE-2025-23251 posing serious risks of remote code execution and data tampering across Windows, Linux, and macOS systems. Each flaw has been signifying a high-risk threat landscape for users of this generative AI platform. NVIDIA addressed these issues with a patch released on April 22, 2025, strongly advising all users to upgrade to version 25.02 immediately to prevent exploitation.

The first vulnerability, CVE-2025-23249, is linked to unsafe deserialization (CWE-502), allowing remote attackers to manipulate serialized objects and execute arbitrary code by injecting malicious content during data processing. This creates a critical entry point for unauthorized access, potentially resulting in altered or malicious outputs from AI models. The second vulnerability, CVE-2025-23250, involves improper path validation (CWE-22), which can enable path traversal attacks leading to unauthorized file writes. Exploiting this could allow adversaries to overwrite system files or corrupt training data and model configurations, leading to possible poisoning of AI workflows.

The third flaw, CVE-2025-23251, stems from improper control over dynamic code generation (CWE-94). This vulnerability is particularly alarming due to the NeMo Framework’s focus on generative AI—making it susceptible to the injection and execution of untrusted code. All three issues share the same attack vector profile (AV:N/AC:L/PR:N/UI:R/S:U/C:L/I:H/A:L), which denotes that the flaws are exploitable remotely with low complexity and no privilege requirements, though user interaction is necessary.

Given NeMo's popularity in training large language models, speech recognition systems, and multimodal AI, the implications of these flaws are significant. Organizations are advised to not only patch their systems but also conduct audits to detect any signs of compromise. Additional protective measures, including enhanced controls over the AI development lifecycle and vigilant monitoring for anomalies, are strongly recommended. These vulnerabilities emphasize the urgent need for robust security in AI frameworks as they become critical to both commercial innovation and academic research globally.