Comparison Overview
SF Fire Credit Union

SF Fire Credit Union
3201 California Street, San Francisco, California, US, 94118
Last Update: 04/04/2026
Founded in 1951 by firefighters for firefighters, we are a credit union dedicated to building financial success in our community. Today, we continue to serve firefighters while also extending membership to those who live, work, or attend school in San Francisco, San Ma...

Marsh McLennan
1166 Avenue of the Americas, New York, NY, US, 10036
Last Update: 18/07/2026
Marsh (NYSE: MRSH) is a global leader in risk, strategy and people, advising clients in 130 countries across four businesses: Marsh Risk, Guy Carpenter, Mercer and Oliver Wyman. With annual revenue over $24 billion and more than 90,000 ...
Compliance Ranges Comparison

SF Fire Credit Union







Marsh McLennan






Benchmark & Cyber Underwriting Signals
Incidents vs Financial Services Industry Avg (This Year)
No incidents recorded for SF Fire Credit Union in 2026.
Incidents vs Financial Services Industry Avg (This Year)
No incidents recorded for Marsh McLennan in 2026.
Incident History - SF Fire Credit Union (X = Date, Y = Severity)
SF Fire Credit Union cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Marsh McLennan (X = Date, Y = Severity)
Marsh McLennan cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

SF Fire Credit Union

Marsh McLennan
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.