Comparison Overview
College of Natural & Agricultural Sciences (CNAS)

College of Natural & Agricultural Sciences (CNAS)
900 University Avenue, Riverside, 92521, US
Last Update: 02/04/2026
About CNAS The College of Natural and Agricultural Sciences(CNAS) is home to world-renowned scholars pursuing research that deepens our knowledge of the universe we live in and improves the quality of life for inhabitants of the state, the nation, and the world. Central...

Louisiana State University
Louisiana State University, Baton Rouge, la, US, 70803
Last Update: 02/04/2026
LSU is the flagship institution of Louisiana and is one of only 30 universities nationwide holding land-grant, sea-grant and space-grant status. Since 1860, LSU has served its region, the nation, and the world through extensive, multipurpose programs encompassing ins...
Compliance Ranges Comparison

College of Natural & Agricultural Sciences (CNAS)







Louisiana State University






Benchmark & Cyber Underwriting Signals
Incidents vs Higher Education Industry Avg (This Year)
No incidents recorded for College of Natural & Agricultural Sciences (CNAS) in 2026.
Incidents vs Higher Education Industry Avg (This Year)
No incidents recorded for Louisiana State University in 2026.
Incident History - College of Natural & Agricultural Sciences (CNAS) (X = Date, Y = Severity)
College of Natural & Agricultural Sciences (CNAS) cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Louisiana State University (X = Date, Y = Severity)
Louisiana State University cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

College of Natural & Agricultural Sciences (CNAS)

Louisiana State 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.