Comparison Overview
Trenitalia

Trenitalia
Piazza della Croce Rossa n. 1, ROMA, IT, 00161
Last Update: 30/06/2026
Trenitalia, the Ferrovie dello Stato Group's company for the transportation of passengers and the provision of logistics services for goods, is one of Europe’s largest rail operators. Trenitalia's mission is to provide services, in a domestic and European context, able...

Network Rail
The Quadrant MK, Milton Keynes, MK9 1EN, GB
Last Update: 01/04/2026
We’re at the heart of revitalising Britain’s railway, getting people and goods where they need to be and supporting the economy. Investment and modernisation are essential. So we’re building the railway of the future, running a safe, reliable and efficient railway, and...
Compliance Ranges Comparison

Trenitalia







Network Rail






Benchmark & Cyber Underwriting Signals
Incidents vs Rail Transportation Industry Avg (This Year)
Trenitalia has 20.0% fewer incidents than the average of same-industry companies with at least one recorded incident.
Incidents vs Rail Transportation Industry Avg (This Year)
No incidents recorded for Network Rail in 2026.
Incident History - Trenitalia (X = Date, Y = Severity)
Trenitalia cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Network Rail (X = Date, Y = Severity)
Network Rail cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

Trenitalia

Network Rail
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.