CVE-2026-2492 in TensorFlow
Summary
by MITRE • 02/21/2026
TensorFlow HDF5 Library Uncontrolled Search Path Element Local Privilege Escalation Vulnerability. This vulnerability allows local attackers to escalate privileges on affected installations of TensorFlow. An attacker must first obtain the ability to execute low-privileged code on the target system in order to exploit this vulnerability.
The specific flaw exists within the handling of plugins. The application loads plugins from an unsecured location. An attacker can leverage this vulnerability to escalate privileges and execute arbitrary code in the context of a target user. Was ZDI-CAN-25480.
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Analysis
by VulDB Data Team • 02/22/2026
This vulnerability resides within the TensorFlow HDF5 library's plugin loading mechanism, representing a classic uncontrolled search path element issue that enables local privilege escalation. The flaw stems from the application's improper handling of plugin directories, where it loads plugins from locations that are not properly secured or validated. This creates an opportunity for attackers to manipulate the plugin loading process by placing malicious plugins in directories that are searched before secure locations, effectively allowing code execution with elevated privileges.
The technical implementation of this vulnerability follows CWE-427 Uncontrolled Search Path Element, where the application's plugin loader does not properly sanitize or validate the search paths used to locate plugin files. When TensorFlow processes HDF5 files containing plugin references, it traverses directories in a predictable order without sufficient path validation, allowing attackers to place malicious shared libraries or executable code in directories that are searched before the intended plugin locations. This behavior directly aligns with ATT&CK technique T1068, which describes the use of privilege escalation techniques through the manipulation of dynamic link libraries.
The operational impact of this vulnerability is significant as it requires only local execution privileges to exploit, making it particularly dangerous in environments where users can execute code on target systems. Attackers can leverage this vulnerability to escalate from standard user privileges to elevated system privileges, potentially gaining full control over the affected system. The exploitation process involves placing malicious plugins in the search path before legitimate ones, causing the system to execute attacker-controlled code during the normal plugin loading process. This vulnerability affects installations where TensorFlow is used to process HDF5 files containing plugin references, particularly in environments where users have the ability to create or modify files in directories that are part of the plugin search path.
Mitigation strategies should focus on implementing secure plugin loading practices that avoid the use of untrusted search paths. System administrators should ensure that plugin directories are properly secured and that the search path is validated to prevent loading of plugins from untrusted locations. The recommended approach includes implementing proper path validation, using absolute paths for plugin loading, and ensuring that the plugin loading mechanism does not search directories that are writable by unprivileged users. Additionally, implementing privilege separation and restricting write access to plugin directories can significantly reduce the attack surface. Organizations should also consider applying the latest TensorFlow security updates and patches that address this specific vulnerability, while monitoring for any unauthorized modifications to plugin directories or unexpected privilege escalation attempts in their systems.