Allocation of Resources Without Limits or Throttling (CWE-770) in Kibana can lead to a denial of service via Excessive Allocation (CAPEC-130). An authenticated user with low-level permissions could submit a specially crafted request that causes excessive resource consumption, which may render Kibana unavailable.
Authorization Bypass Through User-Controlled Key (CWE-639) in Kibana can lead to unauthorized data modification via Accessing Functionality Not Properly Constrained by ACLs (CAPEC-1). Under certain conditions, an authenticated user could reference another user's AI Assistant conversation identifier to access or modify a conversation they do not own. Successful exploitation requires knowledge of a hard-to-guess identifier.
Missing Authorization (CWE-862) in Kibana can lead to cross-space information disclosure and unauthorized data modification via Privilege Abuse (CAPEC-122). Kibana Machine Learning carries out its Elasticsearch operations with elevated internal permissions and relies on a per-request space filter to keep the machine learning data of one space separated from another. Part of the Machine Learning functionality did not apply that filter, so operations issued from one space were carried out against the machine learning data of every space in the deployment.
Relative Path Traversal (CWE-23) in Kibana can lead to the unauthorized deletion of Kibana resources via Relative Path Traversal (CAPEC-139). Kibana Fleet accepted a user-supplied identifier for a Fleet Server host configuration without rejecting relative traversal sequences. The identifier is stored as provided and is later incorporated into the request that Kibana issues when that configuration is removed.
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.
A lower privileged user who holds only the privilege to read agent policies can read the entire configuration of a configured Fleet proxy. This would normally require the Fleet privilege to read settings.The proxy configuration possibly contains proxy authentication credentials and private key material that they should not be authorized to view.
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.
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.
Cross-Site Request Forgery (CWE-352) in Kibana can lead to privilege escalation via Cross Site Request Forgery (CAPEC-62). A user who is permitted to create visualizations can save a specially crafted Vega visualization that, when it is opened by another user, causes authenticated requests to be issued to Kibana in the context of the viewing user's session.
Allocation of Resources Without Limits or Throttling (CWE-770) in Kibana can lead to denial of service via Excessive Allocation (CAPEC-130). A specially crafted, malformed payload submitted to a Kibana visualization feature by an authenticated user holding only low-privileged access is not correctly validated before use. Processing the request causes unbounded memory growth in the Kibana process, which is terminated by the host once available memory is exhausted. Kibana then becomes unavailable to all users until the service is restarted.