Vulnerabilidades do Elastic Kibana

Missing Authorization (CWE-862) in the Kibana Entity Store feature can lead to unauthorized credential creation via Accessing Functionality Not Properly Constrained by ACLs (CAPEC-1). An authenticated user holding only low-privilege Security feature access could invoke an administrative operation that creates and persists Elasticsearch API keys under the caller's identity, bypassing the elevated cluster and Kibana privileges that the documented Entity Store setup flow requires.

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 holding only low, read-level Agent Builder privileges could submit a specially crafted request that causes Kibana to consume an unbounded amount of memory, terminating the process and denying service to all users of the instance.

Execution with Unnecessary Privileges (CWE-250) in the Kibana machine learning feature can lead to information disclosure via Privilege Abuse (CAPEC-122). An operation available to users holding only read access to the machine learning feature was performed with an internal service identity rather than the identity of the requesting user. Such a user could therefore receive data from Elasticsearch indices they are not authorized to read. No Elasticsearch cluster or index privileges are required.

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 can submit a specially crafted request that causes excessive resource consumption, which may render Kibana unavailable.

Uncaught Exception (CWE-248) in Kibana can lead to a denial of service via Input Data Manipulation (CAPEC-153). An authenticated user holding only the low-privileged feature access required to use the Observability AI Assistant can submit a specially crafted request that produces an unhandled error condition, terminating the Kibana process and denying service to all users and spaces on that instance until it is restarted.

Incorrect Authorization (CWE-863) in Kibana can lead to unauthorized modification of data via Accessing Functionality Not Properly Constrained by ACLs (CAPEC-1). An authenticated user holding only Security Solution read access in a Kibana space could enumerate and change the state of Entity Store maintainer tasks, silently disabling Entity Analytics maintenance for that space.

Incorrect Authorization (CWE-863) in Kibana Entity Analytics can lead to a loss of security monitoring via Accessing Functionality Not Properly Constrained by ACLs (CAPEC-1). An authenticated user holding only read-level Security feature access, and no Elasticsearch privileges, could stop the recurring Privilege Monitoring engine task for a Kibana space. Privileged user monitoring then stops producing data for that space while the engine continues to report a healthy state to operators.

Improper Handling of Highly Compressed Data (CWE-409) in Kibana can lead to a denial of service via Excessive Allocation (CAPEC-130). An authenticated user holding Streams management privileges could supply specially crafted content that expands to a far larger volume of data during processing, exhausting the memory available to Kibana. The Kibana process is terminated by the host and remains unavailable to all users until the service is restarted.

Improper Neutralization of Special Elements in Data Query Logic (CWE-943) in Kibana can lead to information disclosure via NoSQL Injection (CAPEC-676). An authenticated user with access to the affected query functionality could submit specially crafted input that alters the intended query logic, returning data the user is not authorized to read.

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.

Kibana Agent Builder A2A JSON-RPC API endpoint derives the identifier of a stored conversation from a user-supplied input, and the ownership check on that identifier does not distinguish between a conversation that does not exist and one that exists but belongs to another user. As a result, an authenticated user holding only the Agent Builder read privilege can supply an identifier already in use by another user in the same space and cause that user's conversation to be replaced and reassigned to the requesting account. The original owner permanently loses access to the conversation and its history. The impact is limited to loss of integrity and availability of the affected conversation; the attacker does not read the overwritten content.

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.

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.

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.