Comparison Overview
Next Level Apparel

Next Level Apparel
588 Crenshaw Boulevard, Los Angeles, CA, US, 90503
Last Update: 02/04/2026
At Next Level Apparel, we aspire to not only elevate the way you dress, but support your self-expression and imagination. If you can envision it, we can make it—and in a fashion beyond your stylish dreams. Founded in 2003 and based in Los Angeles, our company has a dec...

Tommy Hilfiger
Danzigerkade 165, Amsterdam, North Holland, NL, 1013
Last Update: 01/04/2026
TOMMY HILFIGER is one of the world’s leading designer lifestyle brands creating a platform that inspires the modern American spirit, while committing to wasting nothing and welcoming all. Founded in 1985, Tommy Hilfiger delivers premium styling, quality and value to c...
Compliance Ranges Comparison

Next Level Apparel







Tommy Hilfiger






Benchmark & Cyber Underwriting Signals
Incidents vs Retail Apparel and Fashion Industry Avg (This Year)
No incidents recorded for Next Level Apparel in 2026.
Incidents vs Retail Apparel and Fashion Industry Avg (This Year)
No incidents recorded for Tommy Hilfiger in 2026.
Incident History - Next Level Apparel (X = Date, Y = Severity)
Next Level Apparel cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Tommy Hilfiger (X = Date, Y = Severity)
Tommy Hilfiger cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

Next Level Apparel

Tommy Hilfiger
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.