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CVE-2026-72681: CWE-862 Missing Authorization in Elastic KibanaCVE-2026-72681
0

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.

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CVE-2026-72680: CWE-639 Authorization Bypass Through User-Controlled Key in Elastic KibanaCVE-2026-72680
0

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.

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CVE-2026-72677: CWE-23 Relative Path Traversal in Elastic KibanaCVE-2026-72677
0

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.

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CVE-2026-72675: CWE-862 Missing Authorization in Elastic KibanaCVE-2026-72675
0

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.

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CVE-2026-72674: CWE-770 Allocation of Resources Without Limits or Throttling in Elastic KibanaCVE-2026-72674
0

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.

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CVE-2026-72672: CWE-863 Incorrect Authorization in Elastic KibanaCVE-2026-72672
0

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.

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CVE-2026-72671: CWE-862 Missing Authorization in Elastic KibanaCVE-2026-72671
0

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.

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CVE-2026-72670: CWE-200 Exposure of Sensitive Information to an Unauthorized Actor in Elastic KibanaCVE-2026-72670
0

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.

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CVE-2026-72669: CWE-862 Missing Authorization in Elastic KibanaCVE-2026-72669
0

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.

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CVE-2026-72667: CWE-770 Allocation of Resources Without Limits or Throttling in Elastic KibanaCVE-2026-72667
0

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.

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