Comparison Overview
School of Human Ecology, University of Wisconsin-Madison
School of Human Ecology, University of Wisconsin-Madison
1300 Linden Dr, Madison, 53706, US
Last Update: 15/11/2025
At the University of Wisconsin-Madison School of Human Ecology, we study families, finances, communities, commerce, design, environments and well-being to build a better world. Alumni careers include: • Financial advisors and coaches • Community organizers and non...
Apollo Education Group
4025 S. Riverpoint Pkwy, Phoenix, 85040, US
Last Update: 01/04/2026
Apollo Education Group, Inc. was founded in 1973 in response to a gradual shift in higher education demographics from a student population dominated by youth to one in which approximately half the students are adults and over 80 percent of whom work full-time. Apollo's ...
Compliance Ranges Comparison
School of Human Ecology, University of Wisconsin-Madison
Apollo Education Group
Benchmark & Cyber Underwriting Signals
Incidents vs Higher Education Industry Avg (This Year)
No incidents recorded for School of Human Ecology, University of Wisconsin-Madison in 2026.
Incidents vs Higher Education Industry Avg (This Year)
No incidents recorded for Apollo Education Group in 2026.
Incident History - School of Human Ecology, University of Wisconsin-Madison (X = Date, Y = Severity)
School of Human Ecology, University of Wisconsin-Madison cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Apollo Education Group (X = Date, Y = Severity)
Apollo Education Group cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents
School of Human Ecology, University of Wisconsin-Madison
Apollo Education Group
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.