Description

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

Executive Summary

Technical Details

Mitigations

References

Additional Metadata

{
  "score": "6.5",
  "pubDate": "2026-09-26T21:16:56.393Z",
  "pubdate": "2026-09-26T21:16:56.393Z",
  "executiveSummary": "This vulnerability involves Uncontrolled Resource Consumption (CWE-400) within Elasticsearch, where improper resource management allows for Excessive Allocation (CAPEC-130).\nThe flaw stems from the inability of the system to properly throttle or constrain resource-heavy operations when handling malicious or malformed input requests.\nThe primary impact is a Denial of Service (DoS) condition, which degrades or terminates the availability of the Elasticsearch cluster.\nThis vulnerability affects Elasticsearch deployments where request limiting and resource quotas are not strictly enforced.\nAttackers with network access to the Elasticsearch API can exploit this by submitting crafted requests designed to consume disproportionate amounts of CPU, memory, or disk I/O.\nSuccessful exploitation requires no complex authentication if the API is exposed without security controls, though it can be triggered by authenticated users with standard read or write permissions.\nThe risk implication is significant for high-availability environments, as it allows an unprivileged entity to crash the search engine, leading to service disruption and potential data availability issues.",
  "technicalDetails": "The root cause of this vulnerability lies in the Elasticsearch request handling pipeline, which lacks sufficient granular limits on resource-intensive operations. When the system processes incoming requests, it may allocate excessive memory or CPU cycles to resolve complex queries, perform large aggregations, or process massive payload structures before validating the resource cost of those operations.\nExploitation follows a predictable flow: an attacker identifies an endpoint—typically related to search, aggregation, or bulk ingestion—that allows for complex query parameter manipulation. By crafting a payload that forces the engine to expand a search space significantly (e.g., using deeply nested aggregations or regex patterns that induce exponential backtracking), the attacker triggers the Excessive Allocation of system resources.\nIn a typical attack scenario, the adversary sends a stream of high-cost requests. Because Elasticsearch allocates memory for these operations in the heap, the Java Virtual Machine (JVM) may quickly encounter an OutOfMemory (OOM) error. If garbage collection cycles cannot keep up with the rate of allocation, the JVM thread may hang or crash, resulting in a node failure.\nThe attack is characterized by the lack of defensive circuit breakers or insufficient configuration of existing circuit breakers to prevent exhaustion. The vulnerable component includes the query execution engine and the bulk processing API. If the cluster has multiple nodes, the load balancer may distribute these malicious requests, potentially causing a cascading failure where all nodes in the cluster exhaust their resources simultaneously.\nNetwork exposure is a critical factor; if the Elasticsearch REST API is exposed to untrusted networks without authentication or IP allowlisting, the barrier to exploitation is minimal. Even within a secured network, an attacker with valid credentials can perform this attack to impact service availability. Once the heap is exhausted or the CPU remains at 100% saturation for an extended period, the node becomes unresponsive to legitimate traffic, and cluster-wide operations—such as rebalancing or shard recovery—are effectively blocked."
}