Comparison Overview
CHRISTUS St. Michael Health System

CHRISTUS St. Michael Health System
2600 Saint Michael Dr, Texarkana, Texas, 75503, US
Last Update: 14/11/2025
CHRISTUS St. Michael Health System, recognized as a High Performing Hospital by U.S. News & World Report for 2020-2021 and one of the nation’s 50 Top Cardiovascular Hospitals in the Nation by Fortune and IBM® Watson Health®, is nestled within over 128 acres of oak, pine...

Rochester Regional Health
100 S Kings Hwy, Rochester, New York, US, 14617
Last Update: 16/06/2026
Rochester Regional Health, headquartered in Rochester, NY, is an integrated health services organization serving the people of Western New York, the Finger Lakes, St. Lawrence County, and beyond. We are dedicated to helping our community stay healthy and live fulfilling...
Compliance Ranges Comparison

CHRISTUS St. Michael Health System







Rochester Regional Health






Benchmark & Cyber Underwriting Signals
Incidents vs Hospitals and Health Care Industry Avg (This Year)
No incidents recorded for CHRISTUS St. Michael Health System in 2026.
Incidents vs Hospitals and Health Care Industry Avg (This Year)
Rochester Regional Health has 3.85% fewer incidents than the average of all companies with at least one recorded incident.
Incident History - CHRISTUS St. Michael Health System (X = Date, Y = Severity)
CHRISTUS St. Michael Health System cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Rochester Regional Health (X = Date, Y = Severity)
Rochester Regional Health cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

CHRISTUS St. Michael Health System

Rochester Regional Health
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.