Description

Deserialization of Untrusted Data (CWE-502) in the Elasticsearch machine learning component can lead to remote code execution via Object Injection (CAPEC-586). A specially crafted trained model artifact could cause attacker-controlled logic to execute with a materially broader system-call surface than intended. Exploitation requires an authenticated user with sufficient privileges to create and deploy trained models.

Executive Summary

Technical Details

Mitigations

References

Additional Metadata

{
  "score": "8.8",
  "pubDate": "2026-09-01T20:17:16.853Z",
  "pubdate": "2026-09-01T20:17:16.853Z",
  "executiveSummary": "This vulnerability involves CWE-502, Deserialization of Untrusted Data, within the Elasticsearch machine learning component. The flaw allows for remote code execution (RCE) through Object Injection (CAPEC-586) when processing malicious trained model artifacts.\nImpact includes potential full system compromise, as the injected logic can execute with a broadened system-call surface, potentially bypassing intended sandbox constraints. The vulnerability is categorized as high risk due to the potential for arbitrary code execution on the underlying host.\nExploitation is strictly constrained to authenticated users who possess specific high-level privileges required to create and deploy trained models within the Elasticsearch environment.\nThe vulnerability highlights a critical failure in the deserialization process of untrusted model data, necessitating strict validation and security controls over model artifact inputs to prevent the instantiation of malicious objects during the machine learning pipeline execution.",
  "technicalDetails": "The root cause of this vulnerability lies in the unsafe deserialization of untrusted data during the ingestion or deployment phase of trained machine learning model artifacts within the Elasticsearch environment. When the machine learning component processes these serialized objects, it fails to sufficiently validate the input stream, permitting the instantiation of unauthorized or malicious object types.\nThe attack flow initiates when an authenticated user, possessing the requisite privileges to interface with the machine learning model deployment API, uploads a specially crafted model artifact. This artifact contains serialized data designed to exploit the deserialization mechanism. Upon processing, the vulnerable component triggers an Object Injection (CAPEC-586), allowing the attacker to influence the application's runtime logic.\nBy manipulating the object graph during the reconstruction process, an attacker can coerce the component into executing arbitrary code. Because the process occurs within the machine learning component, the injected logic may operate with access to a broader system-call surface than what is typically permitted, enabling the attacker to perform unauthorized operations on the host system.\nThe exploit bypasses standard security restrictions by leveraging the deserialization process to instantiate objects that provide a bridge to lower-level system capabilities. This effectively allows the attacker to move beyond the application-level constraints and execute payloads that interact directly with the operating system or the underlying Java Virtual Machine (JVM) environment.\nPost-exploitation impact includes persistent code execution, potential exfiltration of sensitive data stored within the Elasticsearch cluster, or lateral movement within the network from the compromised host. The necessity for authentication and specific high-privilege authorization acts as a primary boundary; however, once these are bypassed or obtained by an adversary, the integrity and confidentiality of the entire cluster are compromised."
}