CVE-2022-41898 is a medium-severity vulnerability in TensorFlow, an open-source machine learning platform widely used for developing and deploying machine learning models. The vulnerability arises from improper input validation (CWE-20) in the SparseFillEmptyRowsGrad operation. Specifically, if this operation receives empty inputs, TensorFlow crashes, leading to a denial of service (DoS) condition. This issue affects TensorFlow versions 2.8.4 and earlier, 2.9.0 up to but not including 2.9.3, and 2.10.0 up to but not including 2.10.1. The root cause is the lack of validation for empty inputs before processing, which causes the system to fail unexpectedly. The vulnerability has been patched in TensorFlow 2.11 and backported to versions 2.10.1, 2.9.3, and 2.8.4. No known exploits are currently reported in the wild. The vulnerability does not require authentication or user interaction to trigger, but it requires the attacker to have the ability to supply crafted inputs to the TensorFlow operation, which typically implies some level of access to the environment where TensorFlow is running.

For European organizations, the impact of this vulnerability primarily concerns availability. TensorFlow is extensively used in research institutions, technology companies, and industries leveraging AI and machine learning, including finance, healthcare, automotive, and manufacturing sectors. A successful exploitation could cause denial of service by crashing machine learning workloads, potentially disrupting critical AI-driven applications and services. This could lead to operational downtime, loss of productivity, and delayed decision-making processes. While the vulnerability does not directly compromise confidentiality or integrity, the disruption of AI services could indirectly affect business continuity and service reliability. Organizations running vulnerable TensorFlow versions in production or research environments are at risk, especially if these environments process real-time or critical data. The absence of known exploits reduces immediate risk, but the widespread use of TensorFlow and the ease of triggering the crash by providing empty inputs make timely patching essential.

European organizations should prioritize upgrading TensorFlow to version 2.11 or later, or apply the backported patches available in versions 2.10.1, 2.9.3, and 2.8.4. Beyond patching, organizations should implement input validation controls at the application level to ensure that empty inputs are not passed to the SparseFillEmptyRowsGrad operation. Incorporating robust input sanitization and validation in machine learning pipelines can prevent malformed data from causing crashes. Monitoring and alerting on TensorFlow service crashes or abnormal terminations can help detect exploitation attempts early. Additionally, organizations should isolate machine learning workloads in controlled environments with restricted access to reduce the risk of unauthorized input injection. Regularly reviewing and updating machine learning frameworks and dependencies as part of the software supply chain security practices is also recommended to mitigate similar vulnerabilities proactively.