CVE-2025-33236 is a vulnerability classified under CWE-94 (Improper Control of Generation of Code) found in the NVIDIA NeMo Framework, a toolkit widely used for building and training conversational AI models. The vulnerability exists because the framework does not adequately validate or control the generation of code from input data, allowing an attacker to craft malicious data that results in code injection. This injected code can be executed within the context of the NeMo environment, leading to unauthorized code execution. The attack vector is local (AV:L), requiring the attacker to have low privileges (PR:L) on the system but no user interaction (UI:N) is needed. The vulnerability impacts confidentiality, integrity, and availability, as it can lead to privilege escalation, information disclosure, and data tampering. All versions of NeMo prior to 2.6.1 are affected, and no patches are currently linked, indicating that organizations must be vigilant until an official fix is released. While no exploits have been observed in the wild, the potential for damage is significant given the framework’s use in sensitive AI workflows. The vulnerability’s CVSS v3.1 score is 7.8, indicating a high severity level due to the combination of high impact and relatively low complexity of exploitation once local access is obtained.

For European organizations, the impact of CVE-2025-33236 can be substantial, particularly for those involved in AI research, development, and deployment using the NVIDIA NeMo Framework. Successful exploitation could allow attackers to execute arbitrary code, escalate privileges, and manipulate or exfiltrate sensitive AI model data or intellectual property. This could lead to compromised AI model integrity, leakage of proprietary datasets, and disruption of AI services. Industries such as automotive (autonomous driving systems), telecommunications, finance, and healthcare that leverage AI models built with NeMo are at risk of operational disruption and reputational damage. Additionally, the breach of AI model confidentiality could have regulatory implications under GDPR if personal data is involved. The local attack vector means that insider threats or attackers who gain initial foothold in the network could leverage this vulnerability to deepen their access and control.

1. Restrict access to systems running NVIDIA NeMo Framework to trusted users only and enforce strict access controls and monitoring. 2. Implement network segmentation and isolate AI development environments to limit lateral movement in case of compromise. 3. Apply strict input validation and sanitization on all data fed into the NeMo Framework to prevent malicious code injection. 4. Monitor logs and system behavior for unusual code execution patterns or privilege escalations within AI environments. 5. Maintain up-to-date backups of AI models and related data to enable recovery in case of tampering. 6. Stay informed about NVIDIA’s security advisories and apply patches or updates promptly once version 2.6.1 or later is released. 7. Employ endpoint detection and response (EDR) solutions capable of detecting suspicious local code execution activities. 8. Conduct regular security audits and penetration testing focused on AI development environments to identify and remediate weaknesses.