The native inference process that Elasticsearch uses to evaluate uploaded machine learning models accepts a model operation that computes a memory address from an offset supplied inside the model, without validating that the offset stays within the bounds of the underlying storage. A user with the privileges required to upload and deploy a trained model can craft a model that reads and writes memory outside the intended allocation. The result is heap corruption that crashes the inference process, and, with sufficient control over the heap layout, could allow arbitrary code execution in the context of that process.

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.

Uncontrolled Recursion (CWE-674) in the Elasticsearch wildcard matching helper can lead to a denial of service via Excessive Allocation (CAPEC-130). The matcher used to resolve wildcard patterns against names is implemented recursively and had no bound on recursion depth or on the total number of match operations performed. A search request containing a wildcard pattern with a large number of wildcard groups, evaluated against a sufficiently long name, exhausts the thread stack. Elasticsearch treats a stack overflow as an unrecoverable condition and shuts the node down, so the request terminates the affected node rather than failing gracefully.

A flaw in Elasticsearch allows a low-privileged authenticated user to submit a single small request containing a forged opaque identifier. Elasticsearch decodes and deserializes the identifier before confirming that it was legitimately issued by the cluster, and a size value carried inside the identifier drives an allocation that is neither capped nor accounted for by the available memory-usage controls. The resulting out-of-memory condition is fatal and terminates the affected node process, resulting in a denial of service.

Elasticsearch does not validate a size value taken from a user-supplied input before that value is used to reserve memory for an internal data structure. An authenticated user holding only read privileges can submit a single small crafted request to a product API endpoint that causes the node to attempt an excessively large allocation. The resulting memory exhaustion raises a fatal error that terminates the Elasticsearch node process, causing a denial of service for the affected node and degrading cluster health. The defect is not volumetric, so a single request is sufficient regardless of the heap size configured on the target node.

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.

Memory Allocation with Excessive Size Value (CWE-789) in the ES|QL query processing of Elasticsearch can lead to denial of service via Excessive Allocation (CAPEC-130). An authenticated user able to submit ES|QL queries could send a specially crafted query whose evaluation allocates an unbounded amount of heap memory, exhausting the available heap on the receiving node and causing the node to become unavailable.