Comparison Overview
AXIMUM (Groupe COLAS)

AXIMUM (Groupe COLAS)
8 rue Jean Mermoz, MAGNY-LES-HAMEAUX, 78772, FR
Last Update: 16/03/2026
Aximum sécurise, signale et régule les flux, agissant ainsi en tant qu'opérateur global de la mobilité sûre. Nos agences et leurs établissements élaborent des solutions complètes, en veillant à prendre en compte toutes les mobilités, et afin d'assurer l'installation, l'...

International Brotherhood of Electrical Workers (IBEW)
900 7th Street, NW, Washington, 20001, US
Last Update: 02/04/2026
The IBEW represents 860,000 active. and retired who work in a wide variety of fields, including utilities, construction, telecommunications, broadcasting, manufacturing, railroads and government. The IBEW has members in both the United States and Canada and stands out ...
Compliance Ranges Comparison

AXIMUM (Groupe COLAS)







International Brotherhood of Electrical Workers (IBEW)






Benchmark & Cyber Underwriting Signals
Incidents vs Construction Industry Avg (This Year)
No incidents recorded for AXIMUM (Groupe COLAS) in 2026.
Incidents vs Construction Industry Avg (This Year)
No incidents recorded for International Brotherhood of Electrical Workers (IBEW) in 2026.
Incident History - AXIMUM (Groupe COLAS) (X = Date, Y = Severity)
AXIMUM (Groupe COLAS) cyber incidents detection timeline including parent company and subsidiaries.
Incident History - International Brotherhood of Electrical Workers (IBEW) (X = Date, Y = Severity)
International Brotherhood of Electrical Workers (IBEW) cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

AXIMUM (Groupe COLAS)

International Brotherhood of Electrical Workers (IBEW)
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.