Comparison Overview
The Peabody Institute of The Johns Hopkins University
The Peabody Institute of The Johns Hopkins University
1 East Mount Vernon Place, Baltimore, MD, 21202, US
Last Update: 07/12/2025
Founded in 1857 as the first conservatory in the United States, the Peabody Institute has remained a leader at the intersection of art and education. Building on a rich history of professional music training at the highest level and focused on the five pillars of excel...
Tsinghua University
北京市海淀区双清路1号, 北京, beijing, CN, 100084
Last Update: 02/04/2026
Tsinghua University is a university in Beijing, People's Republic of China. Tsinghua University was established in 1911, originally under the name “Tsinghua Xuetang”. The school was renamed the "Tsinghua School" in 1912. The university section was founded in 1925 and t...
Compliance Ranges Comparison
The Peabody Institute of The Johns Hopkins University
Tsinghua University
Benchmark & Cyber Underwriting Signals
Incidents vs Higher Education Industry Avg (This Year)
No incidents recorded for The Peabody Institute of The Johns Hopkins University in 2026.
Incidents vs Higher Education Industry Avg (This Year)
No incidents recorded for Tsinghua University in 2026.
Incident History - The Peabody Institute of The Johns Hopkins University (X = Date, Y = Severity)
The Peabody Institute of The Johns Hopkins University cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Tsinghua University (X = Date, Y = Severity)
Tsinghua University cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents
The Peabody Institute of The Johns Hopkins University
Tsinghua 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.