Comparison Overview
NIHR Imperial Biomedical Research Centre

NIHR Imperial Biomedical Research Centre
The Bays, Entrance 2, 2nd Floor, South Wharf Road , London, W2 1NY, GB
Last Update: 04/03/2026
The NIHR Imperial Biomedical Research Centre (BRC) is a partnership between Imperial College Healthcare NHS Trust and Imperial College London, one of only 20 in the country. Our BRC is structured into 8 Research Themes and 4 Cross-Cutting Themes, reflecting the breadt...

Los Alamos National Laboratory
P.O. Box 1663, Los Alamos, NM, US, 87545
Last Update: 29/03/2026
Los Alamos National Laboratory is one of the world’s most innovative multidisciplinary research institutions. We're engaged in strategic science on behalf of national security to ensure the safety and reliability of the U.S. nuclear stockpile. Our workforce specializes ...
Compliance Ranges Comparison

NIHR Imperial Biomedical Research Centre







Los Alamos National Laboratory






Benchmark & Cyber Underwriting Signals
Incidents vs Research Services Industry Avg (This Year)
No incidents recorded for NIHR Imperial Biomedical Research Centre in 2026.
Incidents vs Research Services Industry Avg (This Year)
No incidents recorded for Los Alamos National Laboratory in 2026.
Incident History - NIHR Imperial Biomedical Research Centre (X = Date, Y = Severity)
NIHR Imperial Biomedical Research Centre cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Los Alamos National Laboratory (X = Date, Y = Severity)
Los Alamos National Laboratory cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

NIHR Imperial Biomedical Research Centre

Los Alamos National Laboratory
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.