Comparison Overview
Advanced Professional Studies at Rensselaer
Advanced Professional Studies at Rensselaer
275 Windsor Street, Hartford, 06120, US
Last Update: 31/03/2026
Founded in 1824, Rensselaer Polytechnic Institute is the oldest technological research university in the United States. The Institute is especially well known for its success in the transfer of knowledge from the laboratory, classroom, and studio to the markeplace so t...
University of California, San Francisco
530 Parnassus Ave, San Francisco, California, US, 94122
Last Update: 01/04/2026
UC San Francisco is driven by the idea that when the best research, the best education and the best patient care converge, great breakthroughs are achieved. We pursue this integrated excellence with singular focus, fueled by collaboration among our top-ranked profession...
Compliance Ranges Comparison
Advanced Professional Studies at Rensselaer
University of California, San Francisco
Benchmark & Cyber Underwriting Signals
Incidents vs Higher Education Industry Avg (This Year)
No incidents recorded for Advanced Professional Studies at Rensselaer in 2026.
Incidents vs Higher Education Industry Avg (This Year)
No incidents recorded for University of California, San Francisco in 2026.
Incident History - Advanced Professional Studies at Rensselaer (X = Date, Y = Severity)
Advanced Professional Studies at Rensselaer cyber incidents detection timeline including parent company and subsidiaries.
Incident History - University of California, San Francisco (X = Date, Y = Severity)
University of California, San Francisco cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents
Advanced Professional Studies at Rensselaer
University of California, San Francisco
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.