Elasticsearch Excessive Allocation Vulnerability (CVE-2026-56149)
Overview
CVE-2026-56149 describes a Denial of Service (DoS) vulnerability in Elasticsearch, specifically an 'Allocation of Resources Without Limits or Throttling' issue (CWE-770). This vulnerability, also known as Excessive Allocation (CAPEC-130), can be triggered by a malicious user with elevated privileges. By submitting a crafted machine learning request, an attacker can induce the Elasticsearch node to consume an excessive amount of memory. This resource exhaustion can lead to the node becoming unresponsive, effectively causing a denial of service for the entire cluster or affected node. The impact is particularly severe as it can disrupt critical data processing, search capabilities, and overall availability of the Elasticsearch service. The vulnerability stems from insufficient validation or throttling mechanisms for memory allocations during the processing of complex machine learning tasks, allowing for resource starvation attacks.
Affected Systems
Testing Guide
1. **Identify privileged users:** Determine which users have elevated privileges and access to Elasticsearch's machine learning features. 2. **Simulate crafted requests:** Attempt to submit specially crafted machine learning requests that are known or suspected to cause high memory allocation. This may involve complex data transformations or large datasets within the ML job. 3. **Monitor node memory:** Closely observe the memory usage of the affected Elasticsearch nodes during the submission and processing of these requests. 4. **Check for unresponsiveness:** If memory usage increases drastically and the node becomes unresponsive or times out during operations, it may be affected. 5. **Review logs:** Examine Elasticsearch logs for errors related to memory allocation, out-of-memory exceptions, or node instability.
Mitigation Steps
1. **Limit user privileges:** Restrict access to the machine learning features to only trusted and authorized users. 2. **Monitor resource usage:** Implement robust monitoring for memory consumption on Elasticsearch nodes and set up alerts for abnormal spikes. 3. **Review and sanitize ML requests:** Carefully vet and sanitize any machine learning requests submitted to Elasticsearch, especially those originating from less trusted sources. 4. **Apply security patches:** Keep Elasticsearch and its related components updated to the latest stable versions, which should include patches for known vulnerabilities. 5. **Configure resource limits:** Explore and configure any available node or JVM-level memory limits specific to machine learning tasks.