Comparison Overview
Healthier Futures at The University of Manchester

Healthier Futures at The University of Manchester
The University of Manchester, Oxford Road, Manchester, M13 9PL, GB
Last Update: 01/02/2026
The University of Manchester’s Healthier Futures Research Platform is bringing together academics, policy makers, campaign groups and businesses to act upon the causes and consequences of health inequalities around the world. The Healthier Futures Research Platform wil...

Politecnico di Milano
Piazza Leonardo da Vinci, 32, Milano, IT
Last Update: 01/04/2026
Politecnico Milano is a scientific-technological university which trains engineers, architects and designers. The University has always focused on the quality and innovation of its teaching and research, developing a fruitful relationship with business and productive w...
Compliance Ranges Comparison

Healthier Futures at The University of Manchester







Politecnico di Milano






Benchmark & Cyber Underwriting Signals
Incidents vs Research Services Industry Avg (This Year)
No incidents recorded for Healthier Futures at The University of Manchester in 2026.
Incidents vs Research Services Industry Avg (This Year)
No incidents recorded for Politecnico di Milano in 2026.
Incident History - Healthier Futures at The University of Manchester (X = Date, Y = Severity)
Healthier Futures at The University of Manchester cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Politecnico di Milano (X = Date, Y = Severity)
Politecnico di Milano cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

Healthier Futures at The University of Manchester

Politecnico di Milano
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.