Comparison Overview
Garda Technologies, LLC

Garda Technologies, LLC
ул. Нартова, Нижний Новгород, 603104, RU
Last Update: 04/04/2026
Garda Technologies is a Russian vendor of innovative antifraud, DLP and network forensics solutions. The company’s products hold leading positions on the information security (IS) market and include: • DDoS protection • database protection • fraud monitoring • DLP syste...

Atos
80 Quai Voltaire, 95877 Bezons, FR
Last Update: 03/04/2026
Atos Group is a global leader in digital transformation with c. 67,000 employees and annual revenue of c. €10 billion, operating in 61 countries under two brands — Atos for services and Eviden for products. European number one in cybersecurity, cloud and high performanc...
Compliance Ranges Comparison

Garda Technologies, LLC







Atos






Benchmark & Cyber Underwriting Signals
Incidents vs IT Services and IT Consulting Industry Avg (This Year)
No incidents recorded for Garda Technologies, LLC in 2026.
Incidents vs IT Services and IT Consulting Industry Avg (This Year)
No incidents recorded for Atos in 2026.
Incident History - Garda Technologies, LLC (X = Date, Y = Severity)
Garda Technologies, LLC cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Atos (X = Date, Y = Severity)
Atos cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

Garda Technologies, LLC

Atos
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.