Incorrect Authorization (CWE-863) in Kibana Fleet can lead to privilege escalation via Privilege Abuse (CAPEC-122). Fleet restricts some callers to managing integration policies for one specific integration. When an existing integration policy was updated, that restriction was evaluated against the integration recorded on the stored policy rather than against the replacement integration supplied with the update. An authenticated user holding only the Elastic Defend endpoint policy management privilege was therefore able to convert an endpoint policy they administer into a policy for a different integration, and to supply that integration's configuration at the same time.

Authorization Bypass Through User-Controlled Key (CWE-639) in Kibana can lead to unauthorized cross-space access via Accessing Functionality Not Properly Constrained by ACLs (CAPEC-1). The result is disclosure of inference output from a trained model in a different space that the user is not authorized to list, read, or use, which exposes the behavior of a model. The same pattern also reached the deployment stop and deployment update operations, allowing an active trained model deployment in another space to be stopped or to have its allocated resources altered.

Kibana Agent Builder determines whether a caller owns a private agent by comparing a stable user identifier when one is recorded, and falling back to a comparison of the username when it is not. A username is not unique across Elasticsearch authentication realms, so two distinct principals that share a username in different realms are treated as the same owner. This discloses the configuration and instructions of an agent the caller does not own, and allows that agent to be altered or removed.

Improperly Controlled Modification of Dynamically-Determined Object Attributes (CWE-915) in the case management functionality of Elastic Security in Kibana can lead to unauthorized modification of case data by an authenticated user who has not been granted case editing privileges, via Manipulating User-Controlled Variables (CAPEC-77). Object attributes accepted by the case management API were not subject to the same authorization enforcement applied in the user interface, so a low-privileged user could alter case records they were only entitled to view.

Allocation of Resources Without Limits or Throttling (CWE-770) in Kibana can lead to denial of service via Excessive Allocation (CAPEC-130). An authenticated user who is authorized to manage maintenance windows could submit a specially crafted, malformed payload that causes the Kibana process to consume excessive resources. Kibana becomes unresponsive for all users and does not recover without manual intervention.

Inefficient Algorithmic Complexity (CWE-407) in Kibana can lead to denial of service via Input Data Manipulation (CAPEC-153). A specially crafted, deeply nested expression submitted to a Kibana TSVB visualization is evaluated with a worst-case cost that grows disproportionately with the size of the input. Because the evaluation runs synchronously, a single request consumes the Kibana request-processing thread indefinitely, and Kibana stops responding to all further requests until the service is restarted.

Missing Authorization (CWE-862) in Kibana can lead to information disclosure via Accessing Functionality Not Properly Constrained by ACLs (CAPEC-1). An internal Kibana data retrieval capability used by Elastic Defend endpoint response actions did not enforce the Security Solution and endpoint privileges that its user-facing equivalents require, and it retrieved data with elevated internal permissions rather than the permissions of the requesting user. As a result, an authenticated low-privileged Kibana user with no Security Solution privileges, endpoint privileges and no Elasticsearch privileges on the underlying data, could read endpoint response action records and the corresponding response content returned by managed hosts.

Authorization Bypass Through User-Controlled Key (CWE-639) in Kibana can lead to unauthorized query execution against Elastic Agents that are assigned to a Kibana space the requesting user has no access to, via Accessing Functionality Not Properly Constrained by ACLs (CAPEC-1). A user who is authorized to run Osquery live queries in one space can have a query carried out on hosts belonging to another space, resulting in disclosure of information from those hosts to the Osquery results data stream.

Missing Authorization (CWE-862) in Kibana can lead to unauthorized execution of Osquery and Elastic Defend response actions on managed hosts via Accessing Functionality Not Properly Constrained by ACLs (CAPEC-1). A Kibana user who is able to author and evaluate Elastic Security detection rules can cause response actions to be carried out against enrolled agents without holding the Osquery live query privileges or the Elastic Defend response action privileges that normally govern those capabilities. Depending on the response action involved, this can result in disclosure of information from the affected hosts or in unauthorized changes to their state.

Missing Authorization (CWE-862) in Kibana can lead to unauthorized execution of Elastic Defend response actions on managed hosts via Accessing Functionality Not Properly Constrained by ACLs (CAPEC-1). A Kibana user who holds only detection rule authoring privileges for the Elastic Security solution can associate automated endpoint response actions with a detection rule, even though the dedicated Endpoint response action privileges that govern those capabilities (host isolation, process operations, and execute operations) have not been granted to that user. When such a rule generates alerts, the associated response actions are carried out against the matching hosts.

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.

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.

The Elastic Security capability that suggests existing field values while a user authors endpoint policy artifacts queries Elastic Defend event data with Kibana's internal Elasticsearch account instead of the account of the requesting user. Only Kibana feature privileges are verified, and the caller's Elasticsearch index privileges are not. An authenticated user who holds Elastic Security feature privileges but no read access to the Elastic Defend event indices can therefore retrieve field values from that data, including process command line arguments, which commonly contain tokens, credentials, connection strings, and other sensitive operational detail from protected hosts.

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.

The state that Kibana stores for an Observability Onboarding flow is not bound to the user who created the flow, and the routes that read and update that state do not verify ownership. An authenticated user who holds only generic read access to the space can therefore discover the onboarding flows of other users, read their onboarding state, and write arbitrary progress data into them. A tampered flow can also cause the owner's onboarding view to fail with a server error.

Kibana Agent Builder does not correctly verify that the requesting user holds the privileges required by a separate Kibana feature before it creates and runs a tool that invokes that feature's functionality. This allows privilege escalation and could lead to disclosure of sensitive information that the user is not authorized to read.

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.

Elasticsearch does not apply its configurable input length restriction to a user-supplied pattern accepted by an intervals query. Compiling a deeply nested pattern drives unbounded recursion that exhausts the thread stack and raises a fatal error, terminating the Elasticsearch node process and causing a denial of service for that node. An authenticated user holding only read-only privileges on a single searchable index can trigger the condition with one small search request.

A flaw in Elasticsearch allows an authenticated user with the privileges required to invoke the simulate pipeline API endpoint (https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ingest-simulate) to submit a request that causes a self-referential data structure to be created. When a specific internal component later processes that structure, the operation recurses without bound and raises a fatal error that is not handled by the surrounding execution path, terminating the affected node process and resulting in a denial of service.

Latest version: 9.5.1

Latest version: 9.5.1

Latest version: 9.5.1

Latest version: 9.5.1

Uncontrolled Resource Consumption (CWE-400) in Elasticsearch can lead to denial of service via Exponential Data Expansion (CAPEC-197). An authenticated user may submit a specially crafted query to the ES|QL engine that causes exponential CPU consumption during query evaluation. Because the resource exhaustion persists beyond query completion, repeated requests can fully exhaust the available query worker resources, rendering ES|QL queries unavailable until the node is restarted.

Missing Authorization (CWE-862) in Kibana can lead to unauthorized cross-space information disclosure via user-supplied input that circumvents space-level access control.

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.