What is the CVSS score of CVE-2026-24165?

CVE-2026-24165 has a CVSS 3.1 base score of 7.8 (High). CVSS vector: CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H. EPSS exploitation probability: 0.1%.

Which versions of Nvidia are affected by CVE-2026-24165?

NVIDIA BioNeMo Framework contains a deserialization vulnerability (CVE-2026-24165) that allows local attackers to execute arbitrary code or cause denial of service when users open malicious files,…

How to fix or mitigate CVE-2026-24165?

Within 24 hours: Identify all systems running NVIDIA BioNeMo Framework and disable or restrict user access to file-opening features. Within 7 days: Implement file integrity monitoring on systems where BioNeMo Framework operates, and establish a policy prohibiting opening untrusted project files or data files from external sources. Within 30 days: Contact NVIDIA for patch availability timeline and plan urgent patching upon release; evaluate alternative or isolated deployment architectures if patch delays extend beyond 60 days.

Nvidia CVE-2026-24165

EUVD-2026-17518 HIGH

Deserialization of Untrusted Data (CWE-502)

2026-03-31 nvidia

Deserialization RCE Denial Of Service Information Disclosure Nvidia

7.8

CVSS 3.1 · NVD

Severity by source

NVD PRIMARY

7.8 HIGH

AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Primary rating from NVD · only source for this CVE.

CVSS VectorNVD

CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Attack Vector

Local

Attack Complexity

Low

Privileges Required

None

User Interaction

Required

Scope

Unchanged

Confidentiality

High

Integrity

High

Availability

High

Lifecycle Timeline

EUVD ID Assigned

Mar 31, 2026 - 17:16 euvd

EUVD-2026-17518

Analysis Generated

Mar 31, 2026 - 17:16 vuln.today

CVE Published

Mar 31, 2026 - 16:24 nvd

HIGH 7.8

DescriptionCVE.org

NVIDIA BioNeMo contains a vulnerability where a user could cause a deserialization of untrusted data. A successful exploit of this vulnerability might lead to code execution, denial of service, information disclosure, and data tampering.

AnalysisAI

Deserialization of untrusted data in NVIDIA BioNeMo Framework enables local attackers to execute arbitrary code, cause denial of service, disclose sensitive information, or tamper with data when users open malicious files. CVSS 7.8 (High) reflects local attack vector requiring user interaction. EPSS data not available; no public exploit identified at time of analysis. Affects NVIDIA BioNeMo Framework, a platform for AI-driven drug discovery and biomolecular research.

Technical ContextAI

This vulnerability stems from CWE-502 (Deserialization of Untrusted Data), a class of flaws where applications deserialize externally-controlled data without proper validation. The NVIDIA BioNeMo Framework (CPE: cpe:2.3:a:nvidia:bionemo_framework) likely accepts serialized objects from user-provided files or inputs during machine learning model loading, configuration parsing, or data processing workflows. When Python pickle, YAML, or similar serialization formats are used without sanitization, attackers can embed malicious payloads that execute during deserialization. In scientific computing frameworks like BioNeMo, which process complex biomolecular datasets and pretrained models, deserialization vulnerabilities are particularly concerning as researchers routinely share model files and configuration data across institutions.

RemediationAI

Organizations should immediately consult NVIDIA's official security bulletin at https://nvidia.custhelp.com/app/answers/detail/a_id/5808 for patched versions and upgrade guidance. Patch available per vendor advisory, though specific remediated version numbers are not confirmed in available data at time of analysis. As an interim mitigation, restrict BioNeMo framework access to trusted users only, implement strict input validation for all model files and configuration data before deserialization, and employ sandboxing or containerization to limit blast radius of potential exploitation. Consider disabling or restricting deserialization features for external data sources until patching is complete. Organizations should also audit existing model repositories for potentially malicious serialized objects and establish policies requiring cryptographic signing of trusted model files.