CVE-2026-24149 is a vulnerability classified under CWE-94 (Improper Control of Generation of Code), specifically a code injection flaw found in NVIDIA's Megatron-LM, a large language model training framework. The vulnerability exists in a script component of Megatron-LM across all platforms, where insufficient validation or sanitization of attacker-controlled input allows malicious data to be interpreted as executable code. This flaw enables an attacker with local access and low privileges to execute arbitrary code within the context of the Megatron-LM process. The vulnerability does not require user interaction and can lead to a chain of severe consequences including escalation of privileges, unauthorized disclosure of sensitive information, and tampering with data integrity. The CVSS v3.1 score of 7.8 reflects a high severity, with attack vector being local (AV:L), low attack complexity (AC:L), requiring privileges (PR:L), no user interaction (UI:N), and impacting confidentiality, integrity, and availability to a high degree (C:H/I:H/A:H). Although no exploits have been reported in the wild, the potential impact on AI workloads and data confidentiality is significant. The lack of available patches at the time of publication necessitates immediate risk mitigation and monitoring by affected organizations.

For European organizations, the impact of this vulnerability is substantial, especially those involved in AI research, development, and deployment using NVIDIA Megatron-LM. Successful exploitation could lead to unauthorized code execution, allowing attackers to manipulate AI training processes or exfiltrate sensitive datasets, potentially compromising intellectual property and personal data under GDPR. The escalation of privileges could enable attackers to gain broader system access, threatening enterprise infrastructure stability and data integrity. Disruption of AI workloads could also affect operational availability, impacting services reliant on AI models. Given the strategic importance of AI technologies in Europe’s digital economy and innovation landscape, this vulnerability poses risks to both private sector companies and public research institutions. The confidentiality breach could lead to regulatory penalties and reputational damage, while integrity and availability impacts could hinder AI-driven decision-making and services.

Immediate mitigation should focus on restricting access to the vulnerable Megatron-LM scripts to trusted users only, enforcing strict access controls and monitoring local user activities for suspicious behavior. Organizations should implement rigorous input validation and sanitization within any custom scripts or data pipelines interacting with Megatron-LM. Network segmentation and endpoint protection can reduce the risk of lateral movement if exploitation occurs. Since no official patches are available yet, organizations should prepare to apply updates promptly once released by NVIDIA. Additionally, conducting thorough audits of AI infrastructure and applying principle of least privilege for all users and processes interacting with Megatron-LM will reduce attack surface. Employing runtime application self-protection (RASP) or behavior-based anomaly detection tools can help detect exploitation attempts. Finally, organizations should maintain incident response readiness specific to AI platform compromises.