Comparison Overview
PetSmart

PetSmart
19601 N. 27th Avenue, Phoenix, 85027, US
Last Update: 29/03/2026
At PetSmart, we’ll do Anything for Pets. ❤️🐾 And the people who love them! Because we’re those people, too. Pets inspire and motivate us to bring our best selves to work each day. Our associates are devoted to ensuring that pets’ lives are happy and healthy. So, natura...

Nordstrom
1600 7th Ave, Seattle, Washington, US, 98101
Last Update: 03/04/2026
At Nordstrom, we empower our employees to set their sights high and blaze their own trails. This is a place where your success and growth are truly a result of your own efforts and achievements. Our teams are made up of motivated people who work hard to become leade...
Compliance Ranges Comparison

PetSmart







Nordstrom






Benchmark & Cyber Underwriting Signals
Incidents vs Retail Industry Avg (This Year)
No incidents recorded for PetSmart in 2026.
Incidents vs Retail Industry Avg (This Year)
Nordstrom has 3.85% fewer incidents than the average of all companies with at least one recorded incident.
Incident History - PetSmart (X = Date, Y = Severity)
PetSmart cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Nordstrom (X = Date, Y = Severity)
Nordstrom cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

PetSmart

Nordstrom
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.