CVE-2025-33244
Published: 24 March 2026
Description
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,…
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denial of service, escalation of privileges, data tampering, and information disclosure.
Mitigating Controls (NIST 800-53 r5)AI
Timely flaw remediation through patching PyTorch to version 2.6 or later directly eliminates the deserialization vulnerability in NVIDIA APEX.
Information input validation prevents deserialization of untrusted data by ensuring serialized inputs are verified before processing in PyTorch environments.
Memory protection mechanisms like ASLR and DEP mitigate arbitrary code execution resulting from successful deserialization exploits.
Security SummaryAI
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.
Details
- CWE(s)
AI Security AnalysisAI
- AI Category
- Deep Learning Frameworks
- Risk Domain
- N/A
- OWASP Top 10 for LLMs 2025
- None mapped
- MITRE ATLAS Techniques
- None mapped
- Classification Reason
- Matched keywords: pytorch
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Deserialization vulnerability enables remote exploitation over adjacent network (AV:A) with low privileges (PR:L) leading to scope change, arbitrary code execution, and privilege escalation.