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.

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.

Missing Authorization (CWE-862) in Kibana can lead to unauthorized information disclosure via Privilege Abuse (CAPEC-122). A user with limited feature privileges can access workflow execution outputs in their Kibana space without the authorization required to do so through the documented API. The accessible data may include sensitive information returned by workflow steps, such as results from connected data sources that the caller would not otherwise be authorized to access.

Incomplete List of Disallowed Inputs (CWE-184) in Kibana can allow an authenticated attacker with access to the Reporting feature to bypass outbound request restrictions configured by an administrator, causing the reporting service to send requests to network destinations that should be denied by the configured security policy.

Insertion of Sensitive Information into Log File (CWE-532) in Kibana can lead to information disclosure. When the optional application performance monitoring (APM) instrumentation is enabled, sensitive request header values could be recorded in application logs, where they may be accessible to operators with log access.

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 bulk deletion request that causes excessive resource consumption, which may render Kibana unavailable.

Improper Input Validation (CWE-20) in Kibana can lead to a denial of service via Input Data Manipulation (CAPEC-153). An authenticated user can submit a specially crafted Fleet policy input that is not correctly validated, which can render Fleet agent, server, and policy management functionality unavailable.

Improper Input Validation (CWE-20) in the Kibana Fleet agent policy management feature can lead to privilege escalation. An authenticated user with Fleet management privileges can manipulate agent policy configuration by injecting values into a configuration override mechanism that is not adequately validated. An attacker can cause Elastic Agents to be issued API keys with elevated Elasticsearch privileges, potentially granting unauthorized read and write access to sensitive Elasticsearch security indices beyond what is intended for the Fleet management role.

Server-Side Request Forgery (CWE-918) in Kibana allows authenticated users with connector management privileges to bypass the operator-configured connection allowlist. By configuring a Webhook connector with a crafted target, an attacker can cause Kibana to issue outbound requests to destinations that the egress restriction controls were intended to block.

Uncontrolled Resource Consumption (CWE-400) in Kibana can lead to denial of service via Excessive Allocation (CAPEC-130). An authenticated low-privileged user can cause Kibana to consume exponentially increasing amounts of memory by submitting a specially crafted Timelion visualization expression containing deeply chained function calls. The resulting data structure grows without bound, exhausting available memory and causing the Kibana service to crash 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 user can send a specially crafted compressed request payload that is processed prior to authorization checks, causing excessive memory and CPU resource consumption that can result in a Kibana instance becoming unresponsive or crashing.

Improper Neutralization of Input During Web Page Generation (CWE-79) in Kibana can lead to stored HTML injection. A user with write access to an Elasticsearch index could persist crafted markup which, when subsequently rendered through an affected Kibana view by another user, was not sufficiently sanitized. Successful exploitation could result in unauthorized UI manipulation and outbound network requests issued from the viewing user's browser session.

Operation on a Resource after Expiration or Termination (CWE-672) in Kibana can lead to unauthorized information disclosure. A logic error in how expiration timestamps were validated allowed a time-bounded access token to remain usable beyond its intended validity window, enabling an unauthenticated actor in possession of the token to retrieve the associated content after expiration.

Uncontrolled Resource Consumption (CWE-400) in Kibana can lead to a denial of service via Excessive Allocation (CAPEC-130). An authenticated user holding a low-privileged role can submit a specially crafted, oversized payload to an internal Kibana API, causing the Kibana process to exhaust available resources and become unresponsive to all users until the service recovers or is restarted.

A path traversal vulnerability was identified in Kibana's dashboard management functionality. An authenticated user with limited permissions could create a dashboard with a specially crafted identifier. When an administrator subsequently attempts to delete this dashboard through the Kibana interface, the deletion request is redirected to an unintended internal endpoint, potentially resulting in the unauthorized deletion of user accounts or other resources. Exploitation requires an administrator to perform a delete action on the maliciously crafted dashboard object.

Uncontrolled Resource Consumption (CWE-400) in Kibana can lead to denial of service via Excessive Allocation (CAPEC-130). An authenticated user with access to the automatic import feature can submit specially crafted requests with excessively large input values. When multiple such requests are sent concurrently, the backend services become unstable, resulting in service disruption and deployment unavailability for all users.

Incorrect Authorization (CWE-863) in Kibana can lead to cross-space information disclosure via Privilege Abuse (CAPEC-122). A user with Fleet agent management privileges in one Kibana space can retrieve Fleet Server policy details from other spaces through an internal enrollment endpoint. The endpoint bypasses space-scoped access controls by using an unscoped internal client, returning operational identifiers, policy names, management state, and infrastructure linkage details from spaces the user is not authorized to access.

Incorrect Authorization (CWE-863) in Kibana can lead to information disclosure via Privilege Abuse (CAPEC-122). A user with limited Fleet privileges can exploit an internal API endpoint to retrieve sensitive configuration data, including private keys and authentication tokens, that should only be accessible to users with higher-level settings privileges. The endpoint composes its response by fetching full configuration objects and returning them directly, bypassing the authorization checks enforced by the dedicated settings APIs.

Execution with Unnecessary Privileges (CWE-250) in Kibana’s Fleet plugin debug route handlers can lead reading index data beyond their direct Elasticsearch RBAC scope via Privilege Abuse (CAPEC-122). This requires an authenticated Kibana user with Fleet sub-feature privileges (such as agents, agent policies, and settings management).

Improper Validation of Specified Quantity in Input (CWE-1284) in the Timelion visualization plugin in Kibana can lead Denial of Service via Excessive Allocation (CAPEC-130). The vulnerability allows an authenticated user to send a specially crafted Timelion expression that overwrites internal series data properties with an excessively large quantity value.

Missing Authorization (CWE-862) in Kibana’s server-side Detection Rule Management can lead to Unauthorized Endpoint Response Action Configuration (host isolation, process termination, and process suspension) via CAPEC-1 (Accessing Functionality Not Properly Constrained by ACLs). This requires an authenticated attacker with rule management privileges.

Uncontrolled Resource Consumption (CWE-400) in the Timelion component in Kibana can lead Denial of Service via Input Data Manipulation (CAPEC-153)

Inefficient Regular Expression Complexity (CWE-1333) in the AI Inference Anonymization Engine in Kibana can lead Denial of Service via Regular Expression Exponential Blowup (CAPEC-492).

Improper Input Validation (CWE-20) in the internal Content Connectors search endpoint in Kibana can lead Denial of Service via Input Data Manipulation (CAPEC-153)

Improper Validation of Specified Quantity in Input (CWE-1284) in Kibana can allow an authenticated attacker with view-only privileges to cause a Denial of Service via Input Data Manipulation (CAPEC-153). An attacker can send a specially crafted, malformed payload causing excessive resource consumption and resulting in Kibana becoming unresponsive or crashing.

Improper Input Validation (CWE-20) in Kibana's Email Connector can allow an attacker to cause an Excessive Allocation (CAPEC-130) through a specially crafted email address parameter. This requires an attacker to have authenticated access with view-level privileges sufficient to execute connector actions. The application attempts to process specially crafted email format, resulting in complete service unavailability for all users until manual restart is performed.

Allocation of Resources Without Limits or Throttling (CWE-770) in Kibana Fleet can lead to Excessive Allocation (CAPEC-130) via a specially crafted bulk retrieval request. This requires an attacker to have low-level privileges equivalent to the viewer role, which grants read access to agent policies. The crafted request can cause the application to perform redundant database retrieval operations that immediately consume memory until the server crashes and becomes unavailable to all users.

Allocation of Resources Without Limits or Throttling (CWE-770) in Kibana Fleet can lead to Excessive Allocation (CAPEC-130) via a specially crafted request. This causes the application to perform redundant processing operations that continuously consume system resources until service degradation or complete unavailability occurs.

Improper Validation of Array Index (CWE-129) exists in Metricbeat can allow an attacker to cause a Denial of Service through Input Data Manipulation (CAPEC-153) via specially crafted, malformed payloads sent to the Graphite server metricset or Zookeeper server metricset. Additionally, Improper Input Validation (CWE-20) exists in the Prometheus helper module that can allow an attacker to cause a Denial of Service through Input Data Manipulation (CAPEC-153) via specially crafted, malformed metric data.