Raw vector
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2025-33253 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Nvidia Nemo. Its CVSS base score is 7.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 9th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — 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.
CVE-2025-33253 is a vulnerability in the NVIDIA NeMo Framework, stemming from CWE-502 (Deserialization of Untrusted Data). It allows an attacker to potentially achieve remote code execution by convincing a user to load a maliciously crafted file. The issue carries a CVSS v3.1 base score of 7.8 (High), with vector AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H, indicating high impacts on confidentiality, integrity, and availability.
An attacker with local access and low privileges can exploit this vulnerability after tricking a user into loading the malicious file, leading to arbitrary code execution, denial of service, information disclosure, or data tampering. The low attack complexity and lack of required user interaction beyond the initial file load make it feasible for targeted exploitation in environments using the affected framework.
For mitigation details, security practitioners should consult official advisories, including the NVIDIA security bulletin at https://nvidia.custhelp.com/app/answers/detail/a_id/5762, the NVD entry at https://nvd.nist.gov/vuln/detail/CVE-2025-33253, and the CVE record at https://www.cve.org/CVERecord?id=CVE-2025-33253, which provide patch information and workarounds.
As part of NVIDIA's NeMo Framework for developing generative AI models, this vulnerability holds relevance for AI/ML practitioners securing training and inference pipelines against deserialization attacks. No public evidence of real-world exploitation has been reported.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-207819
Vulnerability Data
NVIDIA NeMo Framework contains a vulnerability where an attacker could cause remote code execution by convincing a user to load a maliciously crafted file. A successful exploit of this vulnerability might lead to code execution, denial of service, information disclosure,…
more
and data tampering.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
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-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
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 includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Requiring vetted libraries, regular updates and SAST before release reduces the likelihood that deserialization logic will accept and act on attacker-controlled serialized objects.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.
Mandatory malware scanning of data received over networks or storage media intercepts malicious serialized payloads before they are deserialized by the target application.