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100
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66
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1
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.
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.
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.
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.
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.
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.
Server-Side Request Forgery (CWE-918) in Kibana One Workflow can lead to information disclosure. An authenticated user with workflow creation and execution privileges can bypass host allowlist restrictions in the Workflows Execution Engine, potentially exposing sensitive internal endpoints and data.
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).
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.
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.
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.
Improper Neutralization of Special Elements Used in a Template Engine (CWE-1336) exists in Workflows in Kibana which could allow an attacker to read arbitrary files from the Kibana server filesystem, and perform Server-Side Request Forgery (SSRF) via Code Injection (CAPEC-242). This requires an authenticated user who has the workflowsManagement:executeWorkflow privilege.
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.
Improper Authorization (CWE-285) in Kibana can lead to privilege escalation (CAPEC-233) by allowing an authenticated user to bypass intended permission restrictions via a crafted HTTP request. This allows an attacker who lacks the live queries - read permission to successfully retrieve the list of live queries.
Allocation of Resources Without Limits or Throttling (CWE-770) in Kibana can allow a low-privileged authenticated user to cause Excessive Allocation (CAPEC-130) of computing resources and a denial of service (DoS) of the Kibana process via a crafted HTTP request.
Improper neutralization of input during web page generation ('Cross-site Scripting') (CWE-79) allows an unauthenticated user to embed a malicious script in content that will be served to web browsers causing cross-site scripting (XSS) (CAPEC-63) via a vulnerability a function handler in the Vega AST evaluator.
Improper Authorization (CWE-285) in Kibana can lead to privilege escalation (CAPEC-233) by allowing an authenticated user to change a document's sharing type to "global," even though they do not have permission to do so, making it visible to everyone in the space via a crafted a HTTP request.
Improper neutralization of input during web page generation ('Cross-site Scripting') (CWE-79) allows an authenticated user to embed a malicious script in content that will be served to web browsers causing cross-site scripting (XSS) (CAPEC-63) via a method in Vega bypassing a previous Vega XSS mitigation.
Improper neutralization of input during web page generation ('Cross-site Scripting') (CWE-79) allows an authenticated user to render HTML tags within a user’s browser via the integration package upload functionality. This issue is related to ESA-2025-17 (CVE-2025-25018) bypassing that fix to achieve HTML injection.
Origin Validation Error in Kibana can lead to Server-Side Request Forgery via a forged Origin HTTP header processed by the Observability AI Assistant.
Improper Neutralization of Input During Web Page Generation in Kibana can lead to stored Cross-Site Scripting (XSS)
Improper Neutralization of Input During Web Page Generation in Kibana can lead to Cross-Site Scripting (XSS)
Improper Neutralization of Input During Web Page Generation in Kibana can lead to Stored XSS via case file upload.
Incorrect authorization in Kibana can lead to privilege escalation via the built-in reporting_user role which incorrectly has the ability to access all Kibana Spaces.
URL redirection to an untrusted site ('Open Redirect') in Kibana can lead to sending a user to an arbitrary site and server-side request forgery via a specially crafted URL.
Improper authorization in Kibana can lead to privilege abuse via a direct HTTP request to a Synthetic monitor endpoint.
A Prototype pollution vulnerability in Kibana leads to arbitrary code execution via crafted HTTP requests to machine learning and reporting endpoints.
Unrestricted file upload in Kibana allows an authenticated attacker to compromise software integrity by uploading a crafted malicious file due to insufficient server-side validation.
Unrestricted upload of a file with dangerous type in Kibana can lead to arbitrary JavaScript execution in a victim’s browser (XSS) via crafted HTML and JavaScript files. The attacker must have access to the Synthetics app AND/OR have access to write to the synthetics indices.
Prototype Pollution in Kibana can lead to code injection via unrestricted file upload combined with path traversal.
An issue has been identified where a specially crafted request sent to an Observability API could cause the kibana server to crash. A successful attack requires a malicious user to have read permissions for Observability assigned to them.
Prototype pollution in Kibana leads to arbitrary code execution via a crafted file upload and specifically crafted HTTP requests. In Kibana versions >= 8.15.0 and < 8.17.1, this is exploitable by users with the Viewer role. In Kibana versions 8.17.1 and 8.17.2 , this is only exploitable by users that have roles that contain all the following privileges: fleet-all, integrations-all, actions:execute-advanced-connectors
An allocation of resources without limits or throttling in Kibana can lead to a crash caused by a specially crafted payload to a number of inputs in Kibana UI. This can be carried out by users with read access to any feature in Kibana.
An allocation of resources without limits or throttling in Kibana can lead to a crash caused by a specially crafted request to /api/metrics/snapshot. This can be carried out by users with read access to the Observability Metrics or Logs features in Kibana.
A server side request forgery vulnerability was identified in Kibana where the /api/fleet/health_check API could be used to send requests to internal endpoints. Due to the nature of the underlying request, only endpoints available over https that return JSON could be accessed. This can be carried out by users with read access to Fleet.
An issue was identified in Kibana where a user without access to Fleet can view Elastic Agent policies that could contain sensitive information. The nature of the sensitive information depends on the integrations enabled for the Elastic Agent and their respective versions.
An allocation of resources without limits or throttling in Kibana can lead to a crash caused by a specially crafted request to /api/log_entries/summary. This can be carried out by users with read access to the Observability-Logs feature in Kibana.
A deserialization issue in Kibana can lead to arbitrary code execution when Kibana attempts to parse a YAML document containing a crafted payload. A successful attack requires a malicious user to have a combination of both specific Elasticsearch indices privileges https://www.elastic.co/guide/en/elasticsearch/reference/current/defining-roles.html#roles-indices-priv and Kibana privileges https://www.elastic.co/guide/en/fleet/current/fleet-roles-and-privileges.html assigned to them. The following Elasticsearch indices permissions are required * write privilege on the system indices .kibana_ingest* * The allow_restricted_indices flag is set to true Any of the following Kibana privileges are additionally required * Under Fleet the All privilege is granted * Under Integration the Read or All privilege is granted * Access to the fleet-setup privilege is gained through the Fleet Server’s service account token
A deserialization issue in Kibana can lead to arbitrary code execution when Kibana attempts to parse a YAML document containing a crafted payload. This issue only affects users that use Elastic Security’s built-in AI tools https://www.elastic.co/guide/en/security/current/ai-for-security.html and have configured an Amazon Bedrock connector https://www.elastic.co/guide/en/security/current/assistant-connect-to-bedrock.html .
A flaw allowing arbitrary code execution was discovered in Kibana. An attacker with access to ML and Alerting connector features, as well as write access to internal ML indices can trigger a prototype pollution vulnerability, ultimately leading to arbitrary code execution.
An issue was discovered in Kibana where a user with Viewer role could cause a Kibana instance to crash by sending a large number of maliciously crafted requests to a specific endpoint.
A high-privileged user, allowed to create custom osquery packs 17 could affect the availability of Kibana by uploading a maliciously crafted osquery pack.
An open redirect issue was discovered in Kibana that could lead to a user being redirected to an arbitrary website if they use a maliciously crafted Kibana URL.
A flaw was discovered in Kibana, allowing view-only users of alerting to use the run_soon API making the alerting rule run continuously, potentially affecting the system availability if the alerting rule is running complex queries.
An issue was discovered by Elastic, whereby the Detection Engine Search API does not respect Document-level security (DLS) or Field-level security (FLS) when querying the .alerts-security.alerts-{space_id} indices. Users who are authorized to call this API may obtain unauthorized access to documents if their roles are configured with DLS or FLS against the aforementioned index.
An issue was discovered by Elastic whereby sensitive information may be recorded in Kibana logs in the event of an error or in the event where debug level logging is enabled in Kibana. Elastic has released Kibana 8.11.2 which resolves this issue. The messages recorded in the log may contain Account credentials for the kibana_system user, API Keys, and credentials of Kibana end-users, Elastic Security package policy objects which can contain private keys, bearer token, and sessions of 3rd-party integrations and finally Authorization headers, client secrets, local file paths, and stack traces. The issue may occur in any Kibana instance running an affected version that could potentially receive an unexpected error when communicating to Elasticsearch causing it to include sensitive data into Kibana error logs. It could also occur under specific circumstances when debug level logging is enabled in Kibana. Note: It was found that the fix for ESA-2023-25 in Kibana 8.11.1 for a similar issue was incomplete.
An issue was discovered by Elastic whereby sensitive information may be recorded in Kibana logs in the event of an error. Elastic has released Kibana 8.11.1 which resolves this issue. The error message recorded in the log may contain account credentials for the kibana_system user, API Keys, and credentials of Kibana end-users. The issue occurs infrequently, only if an error is returned from an Elasticsearch cluster, in cases where there is user interaction and an unhealthy cluster (for example, when returning circuit breaker or no shard exceptions).
It was discovered that Kibana was not validating a user supplied path, which would load .pbf files. Because of this, a malicious user could arbitrarily traverse the Kibana host to load internal files ending in the .pbf extension.
It was discovered that a user with Fleet admin permissions could upload a malicious package. Due to using an older version of the js-yaml library, this package would be loaded in an insecure manner, allowing an attacker to execute commands on the Kibana server.
Kibana contains an embedded version of the Chromium browser that the Reporting feature uses to generate the downloadable reports. If a user with permissions to generate reports is able to render arbitrary HTML with this browser, they may be able to leverage known Chromium vulnerabilities to conduct further attacks. Kibana contains a number of protections to prevent this browser from rendering arbitrary content.
An issue was discovered by Elastic whereby sensitive information is recorded in Kibana logs in the event of an error. The issue impacts only Kibana version 8.10.0 when logging in the JSON layout or when the pattern layout is configured to log the %meta pattern. Elastic has released Kibana 8.10.1 which resolves this issue. The error object recorded in the log contains request information, which can include sensitive data, such as authentication credentials, cookies, authorization headers, query params, request paths, and other metadata. Some examples of sensitive data which can be included in the logs are account credentials for kibana_system, kibana-metricbeat, or Kibana end-users.
Kibana version 8.7.0 contains an arbitrary code execution flaw. An attacker with All privileges to the Uptime/Synthetics feature could send a request that will attempt to execute JavaScript code. This could lead to the attacker executing arbitrary commands on the host system with permissions of the Kibana process.
Kibana versions 8.0.0 through 8.7.0 contain an arbitrary code execution flaw. An attacker with write access to Kibana yaml or env configuration could add a specific payload that will attempt to execute JavaScript code. This could lead to the attacker executing arbitrary commands on the host system with permissions of the Kibana process.
An open redirect issue was discovered in Kibana that could lead to a user being redirected to an arbitrary website if they use a maliciously crafted Kibana URL.
A flaw (CVE-2022-38900) was discovered in one of Kibana’s third party dependencies, that could allow an authenticated user to perform a request that crashes the Kibana server process.
It was discovered that Kibana was not sanitizing document fields containing HTML snippets. Using this vulnerability, an attacker with the ability to write documents to an elasticsearch index could inject HTML. When the Discover app highlighted a search term containing the HTML, it would be rendered for the user.
An open redirect flaw was found in Kibana versions before 7.13.0 and 6.8.16. If a logged in user visits a maliciously crafted URL, it could result in Kibana redirecting the user to an arbitrary website.
A cross-site-scripting (XSS) vulnerability was discovered in the Vega Charts Kibana integration which could allow arbitrary JavaScript to be executed in a victim’s browser.
A vulnerability in Kibana could expose sensitive information related to Elastic Stack monitoring in the Kibana page source. Elastic Stack monitoring features provide a way to keep a pulse on the health and performance of your Elasticsearch cluster. Authentication with a vulnerable Kibana instance is not required to view the exposed information. The Elastic Stack monitoring exposure only impacts users that have set any of the optional monitoring.ui.elasticsearch.* settings in order to configure Kibana as a remote UI for Elastic Stack Monitoring. The same vulnerability in Kibana could expose other non-sensitive application-internal information in the page source.
A cross-site-scripting (XSS) vulnerability was discovered in the Data Preview Pane (previously known as Index Pattern Preview Pane) which could allow arbitrary JavaScript to be executed in a victim’s browser.
A flaw was discovered in Kibana in which users with Read access to the Uptime feature could modify alerting rules. A user with this privilege would be able to create new alerting rules or overwrite existing ones. However, any new or modified rules would not be enabled, and a user with this privilege could not modify alerting connectors. This effectively means that Read users could disable existing alerting rules.
An XSS vulnerability was found in Kibana index patterns. Using this vulnerability, an authenticated user with permissions to create index patterns can inject malicious javascript into the index pattern which could execute against other users
It was discovered that Kibana’s JIRA connector & IBM Resilient connector could be used to return HTTP response data on internal hosts, which may be intentionally hidden from public view. Using this vulnerability, a malicious user with the ability to create connectors, could utilize these connectors to view limited HTTP response data on hosts accessible to the cluster.
It was discovered that on Windows operating systems specifically, Kibana was not validating a user supplied path, which would load .pbf files. Because of this, a malicious user could arbitrarily traverse the Kibana host to load internal files ending in the .pbf extension. Thanks to Dominic Couture for finding this vulnerability.
It was discovered that OpenShift Container Platform's (OCP) distribution of Kibana could open in an iframe, which made it possible to intercept and manipulate requests. This flaw allows an attacker to trick a user into performing arbitrary actions in OCP's distribution of Kibana, such as clickjacking.
Kibana versions before 7.12.1 contain a denial of service vulnerability was found in the webhook actions due to a lack of timeout or a limit on the request size. An attacker with permissions to create webhook actions could drain the Kibana host connection pool, making Kibana unavailable for all other users.
In Kibana versions before 7.12.0 and 6.8.15 a flaw in the session timeout was discovered where the xpack.security.session.idleTimeout setting is not being respected. This was caused by background polling activities unintentionally extending authenticated users sessions, preventing a user session from timing out.
The elasticsearch-operator does not validate the namespace where kibana logging resource is created and due to that it is possible to replace the original openshift-logging console link (kibana console) to different one, created based on the new CR for the new kibana resource. This could lead to an arbitrary URL redirection or the openshift-logging console link damage. This flaw affects elasticsearch-operator-container versions before 4.7.
Kibana versions before 6.8.9 and 7.7.0 contains a stored XSS flaw in the TSVB visualization. An attacker who is able to edit or create a TSVB visualization could allow the attacker to obtain sensitive information from, or perform destructive actions, on behalf of Kibana users who edit the TSVB visualization.
Kibana versions before 6.8.9 and 7.7.0 contain a prototype pollution flaw in TSVB. An authenticated attacker with privileges to create TSVB visualizations could insert data that would cause Kibana to execute arbitrary code. This could possibly lead to an attacker executing code with the permissions of the Kibana process on the host system.
Kibana versions 6.7.0 to 6.8.8 and 7.0.0 to 7.6.2 contain a prototype pollution flaw in the Upgrade Assistant. An authenticated attacker with privileges to write to the Kibana index could insert data that would cause Kibana to execute arbitrary code. This could possibly lead to an attacker executing code with the permissions of the Kibana process on the host system.
Kibana versions before 6.8.6 and 7.5.1 contain a cross site scripting (XSS) flaw in the coordinate and region map visualizations. An attacker with the ability to create coordinate map visualizations could create a malicious visualization. If another Kibana user views that visualization or a dashboard containing the visualization it could execute JavaScript in the victim�s browser.
A local file disclosure flaw was found in Elastic Code versions 7.3.0, 7.3.1, and 7.3.2. If a malicious code repository is imported into Code it is possible to read arbitrary files from the local filesystem of the Kibana instance running Code with the permission of the Kibana system user.
Kibana versions before 6.8.2 and 7.2.1 contain a server side request forgery (SSRF) flaw in the graphite integration for Timelion visualizer. An attacker with administrative Kibana access could set the timelion:graphite.url configuration option to an arbitrary URL. This could possibly lead to an attacker accessing external URL resources as the Kibana process on the host system.
Kibana versions before 6.6.1 contain an arbitrary code execution flaw in the security audit logger. If a Kibana instance has the setting xpack.security.audit.enabled set to true, an attacker could send a request that will attempt to execute javascript code. This could possibly lead to an attacker executing arbitrary commands with permissions of the Kibana process on the host system.
Kibana versions before 5.6.15 and 6.6.1 contain an arbitrary code execution flaw in the Timelion visualizer. An attacker with access to the Timelion application could send a request that will attempt to execute javascript code. This could possibly lead to an attacker executing arbitrary commands with permissions of the Kibana process on the host system.
Kibana versions before 5.6.15 and 6.6.1 had a cross-site scripting (XSS) vulnerability that could allow an attacker to obtain sensitive information from or perform destructive actions on behalf of other Kibana users.
Kibana versions before 6.4.3 and 5.6.13 contain an arbitrary file inclusion flaw in the Console plugin. An attacker with access to the Kibana Console API could send a request that will attempt to execute javascript code. This could possibly lead to an attacker executing arbitrary commands with permissions of the Kibana process on the host system.
Kibana versions 4.0 to 4.6, 5.0 to 5.6.12, and 6.0 to 6.4.2 contain an error in the way authorization credentials are used when generating PDF reports. If a report requests external resources plaintext credentials are included in the HTTP request that could be recovered by an external resource provider.
Kibana versions 5.3.0 to 6.4.1 had a cross-site scripting (XSS) vulnerability via the source field formatter that could allow an attacker to obtain sensitive information from or perform destructive actions on behalf of other Kibana users.
Kibana versions after 5.1.1 and before 5.6.7 and 6.1.3 had a cross-site scripting (XSS) vulnerability in the tag cloud visualization that could allow an attacker to obtain sensitive information from or perform destructive actions on behalf of other Kibana users.
Kibana versions after 6.1.0 and before 6.1.3 had a cross-site scripting (XSS) vulnerability in labs visualizations that could allow an attacker to obtain sensitive information from or perform destructive actions on behalf of other Kibana users.
The fix in Kibana for ESA-2017-23 was incomplete. With X-Pack security enabled, Kibana versions before 6.1.3 and 5.6.7 have an open redirect vulnerability on the login page that would enable an attacker to craft a link that redirects to an arbitrary website.
Kibana versions 5.1.1 to 6.1.2 and 5.6.6 had a cross-site scripting (XSS) vulnerability via the colored fields formatter that could allow an attacker to obtain sensitive information from or perform destructive actions on behalf of other Kibana users.
The Kibana fix for CVE-2017-8451 was found to be incomplete. With X-Pack installed, Kibana versions before 6.0.1 and 5.6.5 have an open redirect vulnerability on the login page that would enable an attacker to craft a link that redirects to an arbitrary website.
Kibana versions prior to 6.0.1 and 5.6.5 had a cross-site scripting (XSS) vulnerability via URL fields that could allow an attacker to obtain sensitive information from or perform destructive actions on behalf of other Kibana users.