Comparison Overview
CPAM de Roubaix-Tourcoing (Assurance Maladie)
CPAM de Roubaix-Tourcoing (Assurance Maladie)
2, Place Sébastopol, Tourcoing, 59200, FR
Last Update: 20/04/2026
Travailler à l'Assurance Maladie, c'est s'engager au sein d'un collectif animé par la culture du résultat, où chacun met ses compétences au service de nombreux projets pour protéger la santé de plus de 60 millions d'assurés. La CPAM de Roubaix-Tourcoing protège plus de...
Texas Health and Human Services
4601 W Guadalupe St. , Austin, 78751, US
Last Update: 02/04/2026
Overview The Texas Health and Human Services Commission (HHSC) is an agency within the Texas Health and Human Services System. In September 2016, Texas began transforming how it delivers health and human services to qualified Texans, with a goal of making the Health and...
Compliance Ranges Comparison
CPAM de Roubaix-Tourcoing (Assurance Maladie)
Texas Health and Human Services
Benchmark & Cyber Underwriting Signals
Incidents vs Government Administration Industry Avg (This Year)
No incidents recorded for CPAM de Roubaix-Tourcoing (Assurance Maladie) in 2026.
Incidents vs Government Administration Industry Avg (This Year)
No incidents recorded for Texas Health and Human Services in 2026.
Incident History - CPAM de Roubaix-Tourcoing (Assurance Maladie) (X = Date, Y = Severity)
CPAM de Roubaix-Tourcoing (Assurance Maladie) cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Texas Health and Human Services (X = Date, Y = Severity)
Texas Health and Human Services cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents
CPAM de Roubaix-Tourcoing (Assurance Maladie)
Texas Health and Human Services
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.