kibana

Improper Control of Generation of Code ('Code Injection') (CWE-94) in Fleet Server can lead to the execution of attacker-supplied script content via Code Injection (CAPEC-242). Kibana accepted an identifier for an output configuration without restricting it to safe characters. That identifier is later placed into a server-side script that Fleet Server builds as part of routine agent policy processing, so script syntax embedded in the identifier became part of the script that was executed rather than being treated as data.

Allocation of Resources Without Limits or Throttling (CWE-770) in Kibana can lead to a denial of service via Excessive Allocation (CAPEC-130). A user-supplied list of document fields accepted by the Kibana Playground for RAG feature was neither bounded in length nor de-duplicated before it was used to assemble the response for each matching document. A single crafted request could therefore make Kibana build a response far larger than the data it was derived from, and the resulting processing and memory pressure exhausts the resources of the Kibana instance.

Incorrect Authorization (CWE-863) in Kibana can lead to unauthorized deletion of Synthetics private locations via Accessing Functionality Not Properly Constrained by ACLs (CAPEC-1). Synthetics private locations can be shared with more than one space, and deleting one removes it from every space it is shared with. The safeguard that prevented the deletion of a private location still in use evaluated only the monitors visible in the requesting user's own space, so monitors that depend on the private location in other spaces were not taken into account. As a result, an authenticated Kibana user holding the Synthetics write privilege in a single space could delete a private location that other spaces still depend on, even where the user has no access to those spaces. Deleting the private location removes the shared configuration and stops the monitors in the other spaces from running, which suppresses the availability monitoring those spaces rely on.

A Kibana Machine Learning capability that removes a saved object from the current space accepts machine learning trained models as a target, but it verifies only the privileges that apply to anomaly detection jobs and data frame analytics jobs. A user whose role grants create anomaly detection jobs and data frame analytics jobs without the trained model privilege can therefore remove a trained model from a space. The model itself is not deleted and remains available in its other spaces, and the change can be reversed by a suitably privileged user.

Allocation of Resources Without Limits or Throttling (CWE-770) in Kibana can lead to a denial of service via Excessive Allocation (CAPEC-130). A specially crafted request submitted by an authenticated user with minimal privileges to a validation capability of the Observability log analysis feature causes Kibana to perform an unbounded amount of concurrent work. This can exhaust the memory available to the Kibana process and make Kibana unavailable to all users until it is restarted. The severity of the outcome depends on the resources allocated to the deployment; on well-provisioned deployments a single request may cause degraded performance and elevated memory pressure rather than a full outage, but the request is inexpensive to repeat.