CVE-2022-35981 is a vulnerability identified in TensorFlow, an open-source machine learning platform widely used for developing and deploying machine learning models. The issue lies specifically in the `FractionalMaxPoolGrad` operation, which is part of TensorFlow's gradient computation for fractional max pooling layers. The vulnerability is classified as CWE-617 (Reachable Assertion), meaning that the code contains an assertion that can be triggered by crafted inputs, leading to a program crash. In this case, `FractionalMaxPoolGrad` validates its inputs using `CHECK` statements, which cause the program to abort if the inputs are incorrectly sized, rather than returning a controlled error. This behavior can be exploited to cause a denial of service (DoS) by crashing the TensorFlow process when it receives malformed inputs. The affected versions include TensorFlow versions prior to 2.7.2, versions 2.8.0 up to but not including 2.8.1, and versions 2.9.0 up to but not including 2.9.1. The vulnerability was patched in commit 8741e57d163a079db05a7107a7609af70931def4 and included in TensorFlow 2.10.0, with backports to 2.9.1, 2.8.1, and 2.7.2. There are no known workarounds, and no exploits have been reported in the wild. The vulnerability requires that an attacker can supply inputs to the vulnerable function, which typically implies some level of access to the machine learning pipeline or service using TensorFlow. The impact is primarily denial of service due to process termination caused by assertion failure, rather than remote code execution or data breach.

For European organizations, the impact of CVE-2022-35981 is primarily related to availability disruption of machine learning services that utilize affected TensorFlow versions. Organizations relying on TensorFlow for critical AI workloads, such as financial institutions using ML for fraud detection, healthcare providers using AI for diagnostics, or manufacturing companies employing predictive maintenance, could experience service interruptions if an attacker supplies malformed inputs triggering the assertion failure. While the vulnerability does not lead to data compromise or privilege escalation, denial of service can degrade operational capabilities and potentially cause cascading effects in automated systems. Given the increasing adoption of AI and ML in European industries and public sector services, unpatched systems could face reliability issues. The lack of known exploits reduces immediate risk, but the ease of triggering assertion failures with malformed inputs means that insider threats or attackers with access to ML pipelines could exploit this vulnerability. Additionally, cloud-based ML services hosted in Europe that allow user-supplied models or data could be susceptible to remote DoS attacks. The impact on confidentiality and integrity is minimal, but availability impact could be significant in high-dependence environments.

European organizations should prioritize upgrading TensorFlow installations to versions 2.7.2, 2.8.1, 2.9.1, or later, where the vulnerability has been patched. Since no workarounds exist, patching is the primary mitigation. Organizations should audit their ML pipelines to identify any exposed TensorFlow services that accept external inputs, especially those that process untrusted or user-supplied data. Implement input validation and sanitization at the application layer before data reaches TensorFlow to reduce the risk of malformed inputs triggering the assertion. Employ runtime monitoring and alerting for TensorFlow process crashes to detect potential exploitation attempts early. For cloud deployments, restrict access to ML model training and inference endpoints using network segmentation, authentication, and authorization controls to limit exposure. Additionally, consider containerizing TensorFlow workloads with resource limits and automatic restart policies to mitigate the impact of process crashes. Finally, maintain an inventory of TensorFlow versions in use across the organization to ensure timely patch management and compliance.