Comparison Overview
Penn Dental Medicine

Penn Dental Medicine
240 S 40th St, Philadelphia, Pennsylvania, US, 19104
Last Update: 19/02/2026
Penn Dental Medicine is a private, ivy-league institution with a history deeply rooted in forging precedents in dental education, research, and patient care. Since its founding, dentistry has been taught in a scientific environment as a specialty of medicine and under t...

Nanyang Technological University Singapore
50 Nanyang Avenue, Singapore, Singapore, 639798, SG
Last Update: 02/04/2026
A research-intensive public university, Nanyang Technological University, Singapore (NTU Singapore) has 33,000 undergraduate and postgraduate students in the Engineering, Business, Science, Medicine, Humanities, Arts, & Social Sciences, and Graduate colleges. NTU is ...
Compliance Ranges Comparison

Penn Dental Medicine







Nanyang Technological University Singapore






Benchmark & Cyber Underwriting Signals
Incidents vs Higher Education Industry Avg (This Year)
No incidents recorded for Penn Dental Medicine in 2026.
Incidents vs Higher Education Industry Avg (This Year)
No incidents recorded for Nanyang Technological University Singapore in 2026.
Incident History - Penn Dental Medicine (X = Date, Y = Severity)
Penn Dental Medicine cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Nanyang Technological University Singapore (X = Date, Y = Severity)
Nanyang Technological University Singapore cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

Penn Dental Medicine

Nanyang Technological University Singapore
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.