Comparison Overview
University of Pittsburgh Office of Human Resources

University of Pittsburgh Office of Human Resources
200 S Craig St, Pittsburgh, 15260, US
Last Update: 08/03/2026
Working in collaboration with the University community, the Office of Human Resources, through the departments of Administration, Benefits, Compensation, Employee and Labor Relations, HCM Solutions, Organization Development, Shared Services, Talent Acquisition, and the ...

New York University
70 Washington Sq South, New York, NY, US, 10012-1091
Last Update: 01/04/2026
Founded in 1831, NYU is one of the world’s foremost research universities and is a member of the selective Association of American Universities. The first Global Network University, NYU has degree-granting university campuses in New York and Abu Dhabi, and has announced...
Compliance Ranges Comparison

University of Pittsburgh Office of Human Resources







New York University






Benchmark & Cyber Underwriting Signals
Incidents vs Higher Education Industry Avg (This Year)
No incidents recorded for University of Pittsburgh Office of Human Resources in 2026.
Incidents vs Higher Education Industry Avg (This Year)
No incidents recorded for New York University in 2026.
Incident History - University of Pittsburgh Office of Human Resources (X = Date, Y = Severity)
University of Pittsburgh Office of Human Resources cyber incidents detection timeline including parent company and subsidiaries.
Incident History - New York University (X = Date, Y = Severity)
New York University cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

University of Pittsburgh Office of Human Resources

New York University
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.