CVE-2022-23580 is a medium-severity vulnerability classified under CWE-400 (Uncontrolled Resource Consumption) affecting multiple versions of TensorFlow, an open-source machine learning framework widely used for developing and deploying machine learning models. The vulnerability arises during the shape inference phase, where TensorFlow allocates memory for vectors based on values derived from user-controlled tensors. Specifically, an attacker can supply crafted tensor inputs that cause TensorFlow to allocate excessively large vectors, leading to uncontrolled consumption of system resources such as memory and CPU. This can degrade system performance or cause denial of service (DoS) conditions by exhausting available resources. The affected versions include TensorFlow 2.5.0 up to but not including 2.5.3, 2.6.0 up to but not including 2.6.3, and 2.7.0 up to but not including 2.7.1. The issue was addressed in TensorFlow 2.8.0 and backported to supported earlier versions (2.5.3, 2.6.3, 2.7.1). There are no known exploits in the wild at the time of reporting. Exploitation does not require authentication but does require the ability to supply crafted tensor inputs to the affected TensorFlow instance, which may be exposed via APIs or integrated applications. The vulnerability impacts the availability of systems running vulnerable TensorFlow versions by enabling resource exhaustion attacks, but does not directly compromise confidentiality or integrity. The scope of affected systems is broad given TensorFlow's widespread adoption in research, enterprise AI applications, and cloud services. However, exploitation feasibility depends on the attacker's ability to interact with TensorFlow's shape inference process with malicious inputs.

For European organizations, the impact of CVE-2022-23580 can be significant in environments where TensorFlow is used for critical AI workloads, including research institutions, technology companies, financial services, healthcare, and manufacturing sectors that rely on machine learning models. Uncontrolled resource consumption can lead to denial of service, causing interruptions in AI-driven services, delays in data processing, and potential cascading failures in dependent systems. This can affect operational continuity, degrade user experience, and increase operational costs due to system downtime and recovery efforts. Organizations deploying TensorFlow in multi-tenant or cloud environments may face increased risk if attackers can trigger resource exhaustion remotely, potentially impacting shared infrastructure. While the vulnerability does not directly expose sensitive data or allow code execution, the availability impact can indirectly affect confidentiality and integrity if fallback or recovery mechanisms are compromised. Given the growing reliance on AI and machine learning in Europe’s digital economy, this vulnerability poses a moderate risk that requires timely mitigation to maintain service reliability and trust.

To mitigate CVE-2022-23580, European organizations should: 1) Upgrade all TensorFlow deployments to version 2.8.0 or later, or apply backported patches available for 2.5.3, 2.6.3, and 2.7.1 to ensure the vulnerability is remediated. 2) Implement strict input validation and sanitization on tensor data received from untrusted or external sources to prevent maliciously crafted inputs from reaching the shape inference stage. 3) Employ resource usage monitoring and limits (e.g., container memory and CPU quotas, cgroups) to detect and contain abnormal resource consumption patterns indicative of exploitation attempts. 4) Restrict access to TensorFlow APIs and services to trusted users and networks, using authentication and network segmentation to reduce exposure. 5) Conduct regular security assessments and fuzz testing focused on tensor input handling to identify and remediate similar resource exhaustion risks proactively. 6) For cloud deployments, leverage cloud provider security features such as rate limiting, anomaly detection, and workload isolation to mitigate potential abuse. These measures go beyond generic patching by emphasizing proactive input control, resource governance, and access restrictions tailored to TensorFlow’s operational context.