CVE-2025-23304 is a high-severity vulnerability affecting the NVIDIA NeMo Framework, a widely used library for building and deploying AI models across multiple platforms. The vulnerability arises from improper limitation of a pathname to a restricted directory (CWE-22), specifically in the model loading component of the framework. Attackers can exploit this flaw by crafting malicious .nemo files containing specially manipulated metadata that triggers a path traversal condition. This allows the attacker to bypass directory restrictions and inject arbitrary code during the model loading process. Successful exploitation can lead to remote code execution (RCE), enabling attackers to execute arbitrary commands with the privileges of the NeMo process. Additionally, the vulnerability allows data tampering, potentially compromising the integrity of AI models and associated data. The vulnerability affects all versions of NVIDIA NeMo Framework prior to 2.3.2. The CVSS v3.1 base score is 7.8, indicating a high level of severity, with an attack vector classified as local (AV:L), requiring low attack complexity (AC:L) and low privileges (PR:L), but no user interaction (UI:N). The scope is unchanged (S:U), and the impact on confidentiality, integrity, and availability is high (C:H/I:H/A:H). No known exploits are currently reported in the wild, and no official patches or mitigations have been linked yet. However, the vulnerability's nature suggests that attackers with local access or the ability to supply malicious model files can compromise systems running vulnerable versions of the NeMo Framework. This poses a significant risk especially in environments where AI models are dynamically loaded or updated from external sources.

For European organizations, the impact of CVE-2025-23304 can be substantial, particularly for industries relying heavily on AI and machine learning workflows that utilize the NVIDIA NeMo Framework. Sectors such as automotive, healthcare, finance, and research institutions may be affected due to their adoption of AI models for critical operations. Exploitation could lead to unauthorized code execution, resulting in data breaches, manipulation of AI model outputs, and disruption of AI-driven services. This could undermine trust in AI systems, cause financial losses, and potentially violate data protection regulations such as GDPR if personal data is compromised. Furthermore, the ability to tamper with AI models raises concerns about the integrity and reliability of AI decisions, which can have downstream effects on automated processes and decision-making. Since the attack vector is local, organizations that allow third-party model uploads or have multi-tenant environments are at higher risk. The absence of known exploits in the wild provides a window for proactive mitigation, but the high severity score demands urgent attention to prevent potential targeted attacks.

European organizations should implement the following specific mitigation strategies: 1) Upgrade to NVIDIA NeMo Framework version 2.3.2 or later as soon as it becomes available to ensure the vulnerability is patched. 2) Restrict and tightly control the sources of .nemo model files, allowing only trusted and verified models to be loaded. Implement strong validation and integrity checks on model files before loading them into the framework. 3) Employ sandboxing or containerization techniques to isolate the NeMo Framework execution environment, limiting the impact of potential code execution. 4) Monitor file system and process activities related to model loading for anomalous behavior indicative of exploitation attempts. 5) Enforce the principle of least privilege for processes running the NeMo Framework to minimize the potential damage from a successful exploit. 6) Conduct regular security audits and penetration testing focused on AI model pipelines to identify and remediate similar vulnerabilities. 7) Educate developers and AI engineers about secure model handling practices and the risks of loading untrusted models. These measures go beyond generic advice by focusing on the unique aspects of AI model security and the specific attack vector of path traversal in model metadata.