Description

Uncontrolled Resource Consumption (CWE-400) in Elasticsearch can lead to denial of service via Excessive Allocation (CAPEC-130).

Executive Summary

Technical Details

Mitigations

References

Additional Metadata

{
  "score": "6.5",
  "pubDate": "2026-09-26T21:16:55.907Z",
  "pubdate": "2026-09-26T21:16:55.907Z",
  "executiveSummary": "This vulnerability involves an Uncontrolled Resource Consumption flaw (CWE-400) within Elasticsearch, facilitating Excessive Allocation (CAPEC-130). The issue arises when an attacker sends specially crafted requests that force the application to consume disproportionate amounts of system memory or CPU cycles. By leveraging this mechanism, an unauthenticated or authenticated attacker can induce a Denial of Service (DoS) state, rendering the Elasticsearch cluster unresponsive to legitimate operations. The impact is significant, as it disrupts data indexing, search capabilities, and cluster availability. The risk is high for deployments exposed to untrusted networks or those lacking stringent request size and complexity constraints. Exploitation requires only the ability to reach the Elasticsearch REST API; no advanced permissions are necessary if the API is exposed. The vulnerability highlights a failure in input validation or resource management policies, allowing attackers to exhaust the heap or thread pool capacity, thereby compromising the stability and integrity of the affected product.",
  "technicalDetails": "The vulnerability resides in the request processing pipeline of Elasticsearch, where the lack of granular constraints on incoming payloads allows for resource exhaustion. Under normal operational conditions, Elasticsearch expects requests to adhere to predefined sizing limits; however, the CWE-400 condition suggests that certain request types bypass these safety mechanisms, leading to excessive allocation of JVM heap memory or thread resources.\nThe attack flow commences when an attacker submits a malicious query, document indexing request, or aggregation task designed to trigger maximum resource consumption. For instance, an attacker might submit a deeply nested query or a request involving an extremely large set of complex aggregations that force the engine to maintain large intermediate states in memory. Because the Elasticsearch engine attempts to fulfill these requests before validating their cumulative impact on the JVM, the heap memory usage spikes rapidly.\nThe exploitation method relies on 'Excessive Allocation' (CAPEC-130), where the attacker induces the engine to allocate object trees or temporary data structures that exceed available physical RAM. As the heap approaches capacity, the JVM enters a state of perpetual Garbage Collection (GC) 'thrashing,' where CPU cycles are consumed entirely by the GC process in an attempt to reclaim memory that is effectively held by the attacker's request. This results in the cluster becoming unresponsive, as all available worker threads become blocked waiting for memory allocation.\nThe vulnerable components are primarily the query execution and request parsing modules. This flaw manifests in environments where external users can reach the REST API without strict rate limiting or request payload size restrictions. Post-exploitation, the impact involves total service unavailability. If the memory pressure is high enough, it may trigger an Out-of-Memory (OOM) killer event on the operating system, resulting in an abnormal termination of the Elasticsearch process. Restoration requires manual intervention, such as process restarts and, in severe cases, the purging of the problematic request from the transaction logs or queue buffers if the system attempts to process the malicious payload immediately upon restart.\nThe vulnerability is agnostic of specific privilege levels, provided the attacker has network access to the target endpoint. Attackers leverage the asynchronous nature of the search and indexing pipeline to saturate the system before defensive monitors can trigger circuit breakers or traffic shedding protocols."
}