Comparison Overview
ATC Healthcare

ATC Healthcare
1983 Marcus Ave, None, New Hyde Park, New York, US, 11042
Last Update: 01/04/2026
Since 1985, ATC Healthcare has been growing and providing “staffing wherever healthcare is provided” through our network of over 65 Franchised locations. Today we continue to support our nurses, healthcare professionals and facilities with innovative programs and benefi...

Aya Healthcare
5930 Cornerstone Ct #300, San Diego, 92121, US
Last Update: 30/03/2026
Aya Healthcare is the largest healthcare talent software and staffing company in the United States. Aya operates the world’s largest digital staffing platform delivering every component of healthcare-focused labor services, including travel nursing and allied health, pe...
Compliance Ranges Comparison

ATC Healthcare







Aya Healthcare






Benchmark & Cyber Underwriting Signals
Incidents vs Staffing and Recruiting Industry Avg (This Year)
No incidents recorded for ATC Healthcare in 2026.
Incidents vs Staffing and Recruiting Industry Avg (This Year)
No incidents recorded for Aya Healthcare in 2026.
Incident History - ATC Healthcare (X = Date, Y = Severity)
ATC Healthcare cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Aya Healthcare (X = Date, Y = Severity)
Aya Healthcare cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

ATC Healthcare

Aya 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.