Raw vector
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2025-23304 is a high-severity Path Traversal (CWE-22) vulnerability in Nvidia Nemo. Its CVSS base score is 7.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked in the top 39% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and SA-11 (Developer Testing and Evaluation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
The vulnerability CVE-2025-23304 resides in the model loading component of the NVIDIA NeMo library on all platforms. It stems from insufficient validation of metadata in .nemo files, enabling code injection that can lead to remote code execution and data tampering. The flaw maps to CWE-22 and CWE-94 and carries a CVSS 3.1 base score of 7.8 reflecting local attack vector, low complexity, and high impact on confidentiality, integrity, and availability.
An attacker with local access and the ability to cause a victim to load a malicious .nemo file can exploit the issue to inject and execute arbitrary code or alter data. No elevated privileges beyond standard user rights are required.
NVIDIA has published an advisory that addresses the vulnerability. Security practitioners should consult the vendor guidance at the referenced support portal for patch availability and recommended actions.
The associated EPSS score has remained low, reaching a peak of only 0.0147. The affected component is widely used in AI/ML workflows for loading trained models.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-24608
Vulnerability Data
NVIDIA NeMo library for all platforms contains a vulnerability in the model loading component, where an attacker could cause code injection by loading .nemo files with maliciously crafted metadata. A successful exploit of this vulnerability may lead to remote code…
more
execution and data tampering.
- CWE(s)
Related Threats
Threat-Actor AttributionAI
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V5.3.2V1.3.1
Mitigating Controls (NIST 800-53 r5) AI
Enforces the intended directory access authorizations that path traversal would otherwise bypass.
Developer testing and evaluation finds code paths that accept and execute externally influenced strings.
Input validation directly neutralizes special path elements before pathname construction occurs.
Least privilege reduces the impact of any unauthorized file access obtained via traversal.
Requiring documented secure development standards and tools enforces use of safe code-generation APIs and escaping.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
PR.PS-06's SDLC practices directly target injection flaws via secure coding and testing (mostly), yet as a single broad outcome it leaves many code-generation specifics unaddressed (partial).
Patching/maintenance can remediate known path-traversal flaws in deployed software (partial prevention of exploitability) but does nothing to stop the coding defect from being introduced in the first place.
PR.AA-05 defines and reviews access policies but does not address code-level pathname neutralization, so neither direction prevents CWE-22.
PR.DS-10 protects runtime data confidentiality/integrity but has no bearing on neutralizing externally influenced input during code generation, so neither direction shows any preventive effect.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing in development catches path traversal via static/dynamic analysis.
Secure SDLC mandates input validation and path sanitization that directly prevent path traversal.
Application security requirements include rules for safe file handling and canonicalization.
Secure architecture principles require least-privilege file access and directory isolation.
Secure coding standards explicitly forbid unsafe path construction and mandate safe APIs.
Information access restriction limits which files an application may read or write.