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": "2026-09-26T21:16:55.783Z",
  "pubdate": "2026-09-26T21:16:55.783Z",
  "executiveSummary": "This vulnerability involves an Uncontrolled Resource Consumption flaw (CWE-400) within Elasticsearch, facilitating Excessive Allocation (CAPEC-130). The issue arises when the application fails to properly restrict the quantity of resources, such as memory or CPU cycles, allocated to specific requests. An attacker can exploit this by submitting crafted requests that force the system to consume disproportionate amounts of resources, ultimately resulting in a Denial of Service (DoS) condition.\nThe vulnerability affects Elasticsearch deployments where input validation or resource throttling mechanisms are inadequate. The impact is significant, as it can render the search and data indexing capabilities of the cluster unavailable, disrupting business operations dependent on the service. Exploitation generally requires network access to the Elasticsearch instance, and depending on the specific configuration, may be achievable without elevated privileges. This vulnerability poses a severe risk to service availability and cluster stability, necessitating immediate attention to request-handling policies and resource management configurations.",
  "technicalDetails": "The core issue identified as CWE-400 pertains to the improper management of resource allocation within Elasticsearch, specifically triggered by complex or malformed queries that bypass internal circuit breakers or default resource limits. When an attacker initiates a request—such as a highly nested aggregation, an unbounded search query, or a bulk indexing operation—the system fails to impose sufficient constraints, leading to Excessive Allocation (CAPEC-130).\nThe exploitation flow begins with the attacker identifying an endpoint capable of executing expensive computational tasks, such as the _search API. The attacker crafts a request designed to maximize CPU or heap usage; for instance, a recursive or deeply nested query structure that forces the engine to expand a massive state tree in memory. Because the system does not enforce strict per-request thresholds during the execution phase, the heap memory expands uncontrollably.\nAs the memory consumption approaches the Java Virtual Machine (JVM) heap limit, Elasticsearch may experience severe Garbage Collection (GC) pressure, frequently entering 'stop-the-world' states. This degrades response times for legitimate users and eventually crashes the node or leads to an OutOfMemoryError (OOM). If the node fails, the cluster may undergo shard reallocation, potentially triggering a cascading failure across other nodes due to the increased load of synchronizing indices. \nThe vulnerable component is typically the query execution pipeline or the bulk ingestion coordinator, which processes incoming requests before the circuit breaker can intervene. This vulnerability is particularly potent when the Elasticsearch instance is exposed to the network without proper authentication, allowing an unauthenticated remote attacker to exhaust resources. Even in authenticated environments, users with standard query privileges may possess sufficient access to trigger this condition if the cluster is not hardened with restrictive resource quotas. The post-exploitation state results in a degraded service availability, where the cluster is either non-responsive or permanently locked in a state of high load, effectively silencing the search engine functionality."
}