CVE-2022-23568 is a medium-severity vulnerability affecting TensorFlow, an open-source machine learning framework widely used for developing and deploying machine learning models. The vulnerability arises from an integer overflow in the implementation of the function AddManySparseToTensorsMap. Specifically, this function improperly handles the construction of TensorShape objects by failing to validate the dimensions of input tensors adequately. When user-provided dimensions are used to build a large TensorShape, the integer overflow leads to a CHECK-fail, which is an assert failure within TensorFlow's internal checks. This failure results in a denial of service (DoS) condition, as the process crashes or terminates unexpectedly when attempting to build these TensorShape objects. The vulnerability does not affect confidentiality or integrity but impacts availability by causing service disruption. The affected TensorFlow versions include 2.5.3, 2.6.3, 2.7.1, and the fix is included in version 2.8.0. The vulnerability has a CVSS v3.1 base score of 6.5, reflecting a network attack vector with low attack complexity, requiring privileges but no user interaction, and causing high impact on availability only. There are no known exploits in the wild at this time. The root cause is classified under CWE-190 (Integer Overflow or Wraparound).

For European organizations, the impact of CVE-2022-23568 primarily concerns the availability of machine learning services and applications that rely on vulnerable TensorFlow versions. Organizations using TensorFlow in production environments for critical applications—such as financial services, healthcare, manufacturing, or public sector AI initiatives—may experience service interruptions if an attacker with appropriate privileges exploits this vulnerability. Although exploitation requires some level of privilege (e.g., the ability to submit or manipulate machine learning jobs), the resulting denial of service could disrupt automated decision-making systems, data processing pipelines, or AI-driven services. This disruption could lead to operational delays, loss of productivity, or degraded service quality. Since TensorFlow is widely used across industries in Europe, the vulnerability poses a moderate risk, especially in environments where TensorFlow is exposed to multiple users or integrated into multi-tenant platforms. However, the lack of confidentiality or integrity impact limits the severity to availability concerns only.

European organizations should take the following specific mitigation steps: 1) Identify all instances of TensorFlow in their environments, including development, testing, and production systems. 2) Prioritize upgrading TensorFlow to version 2.8.0 or later, or apply backported patches available for versions 2.5.3, 2.6.3, and 2.7.1, to remediate the vulnerability. 3) Implement strict input validation and sanitization for any user-provided tensor dimensions or data that interact with TensorFlow APIs, reducing the risk of triggering the integer overflow. 4) Restrict privileges for users or processes that can submit or modify machine learning jobs to minimize the attack surface. 5) Monitor TensorFlow application logs and system stability to detect unusual crashes or denial of service symptoms potentially linked to this vulnerability. 6) For multi-tenant or cloud environments, isolate TensorFlow workloads per tenant to prevent cross-tenant impact. 7) Incorporate vulnerability scanning and patch management processes specifically for machine learning frameworks to ensure timely updates.