Raw vector
CVSS:3.1/AV:A/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:HSummary
CVE-2025-33244 is a critical-severity Deserialization of Untrusted Data (CWE-502) vulnerability. Its CVSS base score is 9.0 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 45th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Deep Learning Frameworks; in the Data-Related Vulnerabilities risk domain.
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-2025-33244 is a deserialization of untrusted data vulnerability (CWE-502) in NVIDIA APEX for Linux. This issue affects environments using PyTorch versions earlier than 2.6, where an unauthorized attacker could trigger the deserialization of untrusted data. The vulnerability carries a CVSS v3.1 base score of 9.0 (AV:A/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H) and was published on 2026-03-24.
An adjacent attacker with low privileges can exploit this vulnerability over the network with low complexity and no user interaction required. Scope changes to a higher scope upon successful exploitation, potentially allowing arbitrary code execution, denial of service, privilege escalation, data tampering, and information disclosure.
Mitigation details are available in official advisories, including NVIDIA's security bulletin at https://nvidia.custhelp.com/app/answers/detail/a_id/5782, the NVD entry at https://nvd.nist.gov/vuln/detail/CVE-2025-33244, and the CVE record at https://www.cve.org/CVERecord?id=CVE-2025-33244.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-208972
Vulnerability Data
NVIDIA APEX for Linux contains a vulnerability where an unauthorized attacker could cause a deserialization of untrusted data. This vulnerability affects environments that use PyTorch versions earlier than 2.6. A successful exploit of this vulnerability might lead to code execution,…
more
denial of service, escalation of privileges, data tampering, and information disclosure.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Deep Learning Frameworks
- Risk Domain
- Data-Related Vulnerabilities
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: pytorch
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.