Comparison Overview
Spiez Laboratory

Spiez Laboratory
Austrasse, Spiez, 3700, CH
Last Update: 25/12/2025
The Swiss Federal Institute for NBC-Protection Twitter: @Spiezlab Spiez Laboratory carries out NBC protection research and provides international organisations, national authorities and the Swiss population with a range of services in the fields of arms control, prote...

BAE Systems
BAE Systems, London, SW1Y 5AD, GB
Last Update: 01/04/2026
At BAE Systems, we help our customers to stay a step ahead when protecting people and national security, critical infrastructure and vital information. We provide some of the world’s most advanced, technology-led defence, aerospace and security solutions and employ a sk...
Compliance Ranges Comparison

Spiez Laboratory







BAE Systems






Benchmark & Cyber Underwriting Signals
Incidents vs Defense and Space Manufacturing Industry Avg (This Year)
No incidents recorded for Spiez Laboratory in 2026.
Incidents vs Defense and Space Manufacturing Industry Avg (This Year)
No incidents recorded for BAE Systems in 2026.
Incident History - Spiez Laboratory (X = Date, Y = Severity)
Spiez Laboratory cyber incidents detection timeline including parent company and subsidiaries.
Incident History - BAE Systems (X = Date, Y = Severity)
BAE Systems cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

Spiez Laboratory

BAE Systems
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.