CVE-2025-23313 is a high-severity vulnerability identified in the NVIDIA NeMo Framework, specifically within its Natural Language Processing (NLP) component. The flaw is categorized under CWE-94, which pertains to improper control over the generation of code, commonly known as code injection. This vulnerability affects all versions of the NeMo Framework prior to 2.4.0 across all platforms. The core issue arises when maliciously crafted input data is processed by the NLP component, enabling an attacker to inject and execute arbitrary code within the context of the application using the framework. Exploitation does not require user interaction but does require local access with limited privileges (as indicated by the CVSS vector AV:L/PR:L/UI:N). Successful exploitation can lead to severe consequences including arbitrary code execution, escalation of privileges, unauthorized information disclosure, and tampering with data integrity and availability. The vulnerability has a CVSS v3.1 base score of 7.8, reflecting its high impact on confidentiality, integrity, and availability. Although no known exploits are currently reported in the wild, the potential for exploitation exists given the nature of the vulnerability and the widespread use of the NVIDIA NeMo Framework in AI and NLP applications. The absence of publicly available patches at the time of publication necessitates immediate attention from users of affected versions to mitigate risk.

For European organizations, the impact of CVE-2025-23313 can be significant, especially for those leveraging NVIDIA NeMo Framework in AI-driven NLP applications such as chatbots, automated customer support, document processing, and other language-based services. Exploitation could allow attackers to execute arbitrary code on systems running vulnerable versions, potentially leading to unauthorized access to sensitive data, disruption of critical AI services, and manipulation of outputs that could affect decision-making processes. This is particularly concerning for sectors like finance, healthcare, government, and telecommunications, where NLP applications are increasingly integrated and where data confidentiality and integrity are paramount. Additionally, the escalation of privileges could enable attackers to move laterally within networks, increasing the risk of broader compromise. The vulnerability’s requirement for local access limits remote exploitation but does not eliminate risk, as insider threats or compromised internal systems could be leveraged to trigger the exploit. The lack of user interaction requirement further lowers the barrier for exploitation once local access is obtained.

European organizations should prioritize upgrading the NVIDIA NeMo Framework to version 2.4.0 or later as soon as it becomes available, as this will contain the necessary fixes for CVE-2025-23313. Until patches are released, organizations should implement strict access controls to limit local access to systems running the vulnerable framework, including enforcing the principle of least privilege and monitoring for unusual local activity. Employ application whitelisting and runtime application self-protection (RASP) to detect and block unauthorized code execution attempts within NLP applications. Conduct thorough input validation and sanitization on all data fed into the NLP components to reduce the risk of malicious code injection. Network segmentation should be employed to isolate critical AI infrastructure from general user environments. Additionally, organizations should enhance logging and monitoring to detect early signs of exploitation attempts, focusing on anomalous process executions and privilege escalations. Finally, security teams should prepare incident response plans tailored to potential exploitation scenarios involving AI frameworks.