CVE-2022-23563 is a medium-severity vulnerability in the TensorFlow open-source machine learning framework, specifically a Time-of-Check to Time-of-Use (TOCTOU) race condition categorized under CWE-367. The issue arises from TensorFlow's use of the deprecated and unsafe Python function tempfile.mktemp for creating temporary files in multiple locations within the codebase. tempfile.mktemp generates a temporary filename but does not create the file itself, leading to a window of opportunity between the filename check and the actual file creation. During this window, an attacker or competing process can create or manipulate the file, potentially causing unexpected behavior or privilege escalation. Additionally, in some cases, TensorFlow intended to create temporary directories but mistakenly used mktemp, which only generates filenames, further complicating the logic and increasing the risk of exploitation. The vulnerability affects TensorFlow versions prior to 2.5.3, versions 2.6.0 up to but not including 2.6.3, and versions 2.7.0 up to but not including 2.7.1. The issue has been addressed by replacing mktemp with safer alternatives such as mkstemp and mkdtemp, which atomically create temporary files and directories, eliminating the race condition. Although no known exploits have been reported in the wild, the vulnerability poses a risk in environments where untrusted users or processes can influence temporary file creation or where TensorFlow is used in multi-tenant or shared environments. Exploitation could lead to unauthorized file manipulation, data corruption, or privilege escalation depending on the deployment context.

For European organizations, the impact of this vulnerability depends largely on how TensorFlow is deployed and the environment in which it operates. Organizations using TensorFlow in shared or multi-user environments, such as cloud-based AI platforms, research institutions, or enterprises running machine learning workloads with untrusted inputs, could face risks of data integrity compromise or unauthorized access through exploitation of the race condition. This could lead to corrupted model training data, injection of malicious data, or unauthorized code execution if an attacker can manipulate temporary files. While the vulnerability does not directly lead to remote code execution, the potential for privilege escalation or data tampering could disrupt critical AI-driven services, impacting sectors such as finance, healthcare, manufacturing, and autonomous systems prevalent in Europe. Additionally, organizations relying on TensorFlow for sensitive data processing may face confidentiality risks if temporary files are manipulated to leak information. The absence of known exploits reduces immediate risk, but the widespread use of TensorFlow in European AI and research sectors means that unpatched systems remain vulnerable to future attacks.

European organizations should prioritize upgrading TensorFlow to versions 2.5.3 or later, 2.6.3 or later, or 2.7.1 or later, where the vulnerability has been patched by replacing mktemp with secure alternatives. For environments where immediate upgrade is not feasible, organizations should audit their TensorFlow deployment to identify any usage of temporary file creation that could be influenced by untrusted users or processes. Implementing strict file system permissions and isolating TensorFlow processes can reduce the risk of exploitation. Additionally, organizations should monitor file system activity related to temporary files and directories used by TensorFlow for suspicious behavior indicative of race condition exploitation attempts. Incorporating runtime application self-protection (RASP) or endpoint detection and response (EDR) solutions that can detect unusual file operations may provide additional defense. For developers extending or embedding TensorFlow, reviewing custom code for safe temporary file handling and avoiding mktemp usage is critical. Finally, organizations should maintain an inventory of TensorFlow versions in use across their infrastructure to ensure timely patching and compliance with security policies.