Comparison Overview
The Craneware Group

The Craneware Group
600 W Hillsboro Blvd, Deerfield Beach, 33441, US
Last Update: 20/07/2026
For over 25 years, The Craneware Group has led healthcare financial and operational transformation with innovative technologies that drive measurable results. Our Trisus® cloud ecosystem integrates data, value cycle intelligence, and advanced analytics, helping healthca...

Norton Healthcare
234 East Gray Street, Louisville, 40202, US
Last Update: 01/04/2026
Norton Healthcare is a leader in serving adult and pediatric patients from throughout Greater Louisville, Southern Indiana, the commonwealth of Kentucky and beyond. The not-for-profit hospital and health care system is Louisville’s second largest employer, with more th...
Compliance Ranges Comparison

The Craneware Group







Norton Healthcare






Benchmark & Cyber Underwriting Signals
Incidents vs Hospitals and Health Care Industry Avg (This Year)
The Craneware Group has 42.86% more incidents than the average of same-industry companies with at least one recorded incident.
Incidents vs Hospitals and Health Care Industry Avg (This Year)
Norton Healthcare has 3.85% fewer incidents than the average of all companies with at least one recorded incident.
Incident History - The Craneware Group (X = Date, Y = Severity)
The Craneware Group cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Norton Healthcare (X = Date, Y = Severity)
Norton Healthcare cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

The Craneware Group

Norton Healthcare
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.