Comparison Overview
De Beers Group
De Beers Group
N/A
Last Update: 23/01/2026
The only luxury diamond brand present at source. Discovering nature’s brilliant treasures since 1888 - each creating opportunities for people and the planet. #Building Forever
Tiffany & Co.
727 5th Ave, New York, 10022, US
Last Update: 02/04/2026
In 1837 Charles Lewis Tiffany founded his company in New York City where his store was soon acclaimed as the palace of jewels for its exceptional gemstones. Since then TIFFANY & CO. has become synonymous with elegance, innovative design, fine craftsmanship and creative ...
Compliance Ranges Comparison
De Beers Group
Tiffany & Co.
Benchmark & Cyber Underwriting Signals
Incidents vs Retail Luxury Goods and Jewelry Industry Avg (This Year)
No incidents recorded for De Beers Group in 2026.
Incidents vs Retail Luxury Goods and Jewelry Industry Avg (This Year)
Tiffany & Co. has 3.85% fewer incidents than the average of all companies with at least one recorded incident.
Incident History - De Beers Group (X = Date, Y = Severity)
De Beers Group cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Tiffany & Co. (X = Date, Y = Severity)
Tiffany & Co. cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents
De Beers Group
Tiffany & Co.
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.