CVE-2026-94399 in Elasticsearch
Summary
by MITRE • 09/26/2026
Uncontrolled Resource Consumption (CWE-400) in Elasticsearch can lead denial of service via Excessive Allocation (CAPEC-130)
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Analysis
by VulDB Data Team • 09/26/2026
Elasticsearch, a widely deployed distributed search and analytics engine, is susceptible to an uncontrolled resource consumption vulnerability that stems from improper handling of specific query parameters or data structures. This flaw allows attackers to trigger excessive allocation of system resources such as memory, CPU cycles, or file descriptors without adequate limits or validation checks in place. The core technical issue lies in the processing logic for certain complex queries or aggregations where the engine fails to bound the computational complexity or resource footprint associated with the operation. When a maliciously crafted request is submitted, it forces Elasticsearch nodes to allocate significantly more memory than anticipated during query execution, potentially leading to out-of-memory errors or severe performance degradation across the cluster.
The operational impact of this vulnerability is primarily centered on denial of service conditions within the affected environment. By exploiting the excessive allocation mechanism, an attacker can exhaust available heap space on data nodes, causing them to crash or become unresponsive. In a distributed architecture like Elasticsearch, where shards are replicated and coordinated across multiple nodes, the failure of one node can trigger cascading effects, including increased load on remaining nodes as they attempt to handle failed shard assignments. This results in degraded search performance for legitimate users, potential data loss if recovery mechanisms fail due to resource exhaustion, and complete unavailability of critical indexing or querying services that depend on real-time analytics capabilities.
This vulnerability aligns with the Common Weakness Enumeration identifier CWE-400, which classifies issues related to uncontrolled consumption of resources within a software product. The attack vector corresponds closely to CAPEC-130, known as Excessive Allocation Attack, where an adversary manipulates input parameters to force the application into allocating more memory or processing power than intended. From a threat modeling perspective using the MITRE ATT&CK framework, this behavior can be categorized under resource hijacking techniques often employed in conjunction with other attacks such as credential dumping via heap inspection if the system crashes and dumps core files containing sensitive data. The lack of strict input validation and rate limiting for complex query structures creates an exploitable gap that malicious actors can leverage to disrupt service availability without necessarily gaining unauthorized access to underlying systems or data content directly through this specific flaw alone.
Mitigation strategies must focus on both immediate remediation and long-term architectural improvements. Administrators should ensure they are running the latest patched versions of Elasticsearch where resource limits for query execution have been tightened by upstream developers. Configuration adjustments such as reducing thread pool sizes, implementing stricter circuit breakers to limit heap usage per request, and enabling rate limiting at the gateway or load balancer level can help mitigate the impact of such attacks in real-time environments. Additionally, organizations should review their ingestion pipelines to filter out overly complex queries from untrusted sources before they reach Elasticsearch nodes. Regular security assessments focusing on query complexity limits and resource monitoring dashboards will provide early warning indicators for potential exploitation attempts, allowing teams to respond proactively rather than reactively when service degradation occurs due to resource exhaustion events triggered by these specific vulnerability patterns.