CVE-2022-41910 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 an out-of-bounds read condition in the function MakeGrapplerFunctionItem. This function processes arguments that specify the sizes of inputs and outputs for computational graphs. If the input sizes provided are greater than or equal to the output sizes, the function attempts to read memory beyond the allocated bounds, leading to an out-of-bounds memory read or potentially causing the application to crash. This type of vulnerability is classified under CWE-125 (Out-of-bounds Read), which can lead to information disclosure or denial of service. The issue affects TensorFlow versions prior to 2.8.4, versions between 2.9.0 and before 2.9.3, and versions between 2.10.0 and before 2.10.1. The vulnerability has been patched in TensorFlow 2.11.0 and backported to versions 2.8.4, 2.9.3, and 2.10.1. There are no known exploits in the wild at this time. The vulnerability requires crafted inputs to trigger the out-of-bounds read, and it does not require authentication but does require user interaction in the form of providing or processing maliciously crafted machine learning models or graphs. The impact primarily involves potential application crashes and possible exposure of sensitive memory contents, which could lead to information leakage or denial of service in environments where TensorFlow is used for model training or inference.

For European organizations, the impact of CVE-2022-41910 depends on the extent to which TensorFlow is integrated into their machine learning workflows. Organizations in sectors such as finance, healthcare, automotive, and manufacturing that rely heavily on AI and machine learning could face disruptions if vulnerable TensorFlow versions are exploited. The out-of-bounds read could cause denial of service by crashing critical AI services, potentially interrupting automated decision-making processes or data analysis pipelines. Additionally, although no direct code execution is indicated, the out-of-bounds read could lead to information disclosure, exposing sensitive data processed by TensorFlow models. This is particularly concerning for organizations handling personal data under GDPR regulations, as any data leakage could result in compliance violations and reputational damage. The absence of known exploits reduces immediate risk, but the widespread use of TensorFlow in research institutions and enterprises across Europe means that unpatched systems remain vulnerable to targeted attacks or accidental crashes caused by malformed inputs.

European organizations should prioritize updating TensorFlow installations to versions 2.11.0 or later, or apply the backported patches available in versions 2.8.4, 2.9.3, and 2.10.1. Beyond patching, organizations should implement strict input validation and sanitization for any machine learning models or graphs processed by TensorFlow to prevent malformed inputs from triggering the vulnerability. Deploying runtime monitoring and anomaly detection on TensorFlow services can help identify unusual crashes or memory access patterns indicative of exploitation attempts. For environments where upgrading TensorFlow is not immediately feasible, isolating TensorFlow workloads within containerized or sandboxed environments can limit the impact of crashes or memory leaks. Additionally, organizations should conduct regular security audits of their machine learning pipelines and ensure that access to model submission or training interfaces is restricted to trusted users to reduce the risk of malicious input injection. Finally, integrating TensorFlow usage logs with centralized security information and event management (SIEM) systems can facilitate rapid detection and response to potential exploitation attempts.