Comparison Overview
Cookeville Regional Medical Center

Cookeville Regional Medical Center
1 Medical Center Blvd, Cookeville, 38501, US
Last Update: 17/04/2026
At Cookeville Regional Medical Center, we are dedicated to providing the highest quality care to our patients and making a positive impact on our community. With a rich history dating back to 1921, our state-of-the-art medical center has grown into a leading facility wi...

RHÖN-KLINIKUM AG
1 Schlossplatz, Bad Neustadt an der Saale, DE, 97616
Last Update: 19/07/2026
Die RHÖN‐KLINIKUM AG ist einer der größten Gesundheitsdienstleister in Deutschland. Die Kliniken bieten exzellente Medizin mit direkter Anbindung zu Universitäten und Forschungseinrichtungen. An den fünf Standorten Campus Bad Neustadt, Klinikum Frankfurt (Oder), Univers...
Compliance Ranges Comparison

Cookeville Regional Medical Center







RHÖN-KLINIKUM AG






Benchmark & Cyber Underwriting Signals
Incidents vs Hospitals and Health Care Industry Avg (This Year)
Cookeville Regional Medical Center has 28.57% fewer incidents than the average of same-industry companies with at least one recorded incident.
Incidents vs Hospitals and Health Care Industry Avg (This Year)
No incidents recorded for RHÖN-KLINIKUM AG in 2026.
Incident History - Cookeville Regional Medical Center (X = Date, Y = Severity)
Cookeville Regional Medical Center cyber incidents detection timeline including parent company and subsidiaries.
Incident History - RHÖN-KLINIKUM AG (X = Date, Y = Severity)
RHÖN-KLINIKUM AG cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

Cookeville Regional Medical Center

RHÖN-KLINIKUM AG
FAQ
Latest Global CVEs
Uncontrolled Resource Consumption (CWE-400) in Kibana can lead to denial of service via Excessive Allocation (CAPEC-130). A low-privileged authenticated user can send a specially crafted request to a Kibana machine learning feature, causing the server to exhaust available memory and become unavailable to all users.
Uncontrolled Resource Consumption (CWE-400) in Kibana can lead to denial of service via Excessive Allocation (CAPEC-130). An authenticated attacker with low-privilege access can trigger a denial of service condition in Kibana by sending a specially crafted, oversized request payload. Processing this user-supplied input requires resource-intensive memory allocation that can exhaust the available heap memory in the Kibana process, causing it to crash and become unavailable to all users.
Authorization Bypass Through User-Controlled Key (CWE-639) in Kibana can lead to information disclosure via user-supplied identifiers that reference scheduled query result data from Kibana Spaces the requester is not authorized to access.
Incorrect Authorization (CWE-863) in Kibana can lead to integrity compromise of Machine Learning audit and notification records via Accessing Functionality Not Properly Constrained by ACLs (CAPEC-1). A vulnerability exists in Kibana's Machine Learning functionality where a Machine Learning management endpoint performs an insufficient authorization check. The endpoint validates only a coarse privilege level but does not verify that the requesting user has access to the specific Machine Learning job or notification resources provided in the request. As a result, a low-privileged user with Machine Learning access in any Kibana space can manipulate Machine Learning audit and notification records for arbitrary jobs—including jobs in other spaces or belonging to other users—by leveraging Kibana's internally elevated credentials to write to restricted Machine Learning system indices that the user cannot access directly.
Uncontrolled Recursion (CWE-674) in Elasticsearch can lead to denial of service via a specially crafted search request submitted by a low-privileged authenticated user. A user with read-level index access can submit a request that triggers unbounded recursive processing within the Elasticsearch query evaluation component, causing a fatal error that terminates the affected node. In single-node deployments, this results in complete service outage; in multi-node clusters, it causes repeated node restarts and sustained availability degradation.