CVE-2025-14936 is a stack-based buffer overflow vulnerability classified under CWE-121 found in NSF Unidata NetCDF-C, a widely used library for array-oriented scientific data. The vulnerability specifically exists in the parsing of attribute names, where the software fails to properly validate the length of user-supplied attribute name data before copying it into a fixed-length stack buffer. This lack of bounds checking allows an attacker to overflow the buffer, potentially overwriting the stack and enabling arbitrary code execution. Exploitation requires user interaction, such as opening a maliciously crafted NetCDF file or visiting a web page that triggers the vulnerable parsing code. The vulnerability has a CVSS 3.0 base score of 7.8, reflecting high impact on confidentiality, integrity, and availability, with low attack complexity and no privileges required but user interaction necessary. The flaw allows remote attackers to execute code with the privileges of the current user, which could lead to full system compromise if the user has elevated rights. No public exploits have been reported yet, but the vulnerability is serious due to the widespread use of NetCDF-C in scientific, meteorological, and geospatial data processing environments. The vulnerability was assigned ZDI-CAN-27269 and published on December 23, 2025. The absence of patches at the time of reporting means organizations must rely on mitigations until updates are released.

For European organizations, particularly research institutions, universities, meteorological agencies, and companies involved in geospatial and scientific data analysis, this vulnerability poses a significant risk. Successful exploitation could lead to unauthorized code execution, data breaches, manipulation of scientific data, disruption of critical research processes, and potential lateral movement within networks. The confidentiality of sensitive research data and intellectual property could be compromised. Integrity of datasets could be undermined, affecting research outcomes and decision-making. Availability of systems processing large scientific datasets could be impacted by crashes or malicious payloads. Given the reliance on NetCDF-C in many European scientific communities, the threat could disrupt collaborative projects and data sharing initiatives. The requirement for user interaction somewhat limits remote exploitation but does not eliminate risk, especially in environments where users frequently handle external data files or web resources.

Organizations should prioritize monitoring for updates from NSF Unidata and apply security patches as soon as they become available. Until patches are released, restrict the opening of NetCDF files from untrusted or unknown sources. Implement strict file validation and scanning procedures for incoming data files. Employ runtime protections such as stack canaries, ASLR, and DEP (Data Execution Prevention) to reduce exploitation likelihood. Educate users about the risks of opening files from untrusted sources and visiting suspicious web pages. Network segmentation can limit the impact of a compromised host. Use application whitelisting and endpoint detection and response (EDR) tools to detect anomalous behavior indicative of exploitation attempts. For environments with high security requirements, consider sandboxing applications that process NetCDF files to contain potential exploits. Regularly audit and update software dependencies to minimize exposure to known vulnerabilities.