CVE-2026-78607 in Elasticsearch
Summary
by MITRE • 09/01/2026
Missing Authorization (CWE-862) in the Elasticsearch custom inference service can lead to information disclosure via Privilege Abuse (CAPEC-122). A user holding only inference execution privileges could cause outbound inference traffic to be directed to a destination of their choosing and could cause administrator-provisioned credentials to be exposed.
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Analysis
by VulDB Data Team • 09/01/2026
The vulnerability identified as CWE-862, Missing Authorization, represents a critical flaw within the Elasticsearch custom inference service architecture. This security defect arises from an insufficient access control mechanism that fails to properly validate whether a user possesses the necessary administrative privileges before allowing them to configure or execute outbound network requests for machine learning model inference. In a typical deployment of Elasticsearch with integrated ML capabilities, the system distinguishes between users who are permitted to run inference tasks and those authorized to manage the underlying infrastructure configurations. The flaw lies in the fact that these two permission sets are not adequately segregated during the execution phase of custom inference services. Consequently, a user account granted only the minimal privileges required to submit data for inference processing is inadvertently endowed with the ability to manipulate the network destination parameters associated with that inference request. This architectural oversight effectively bypasses the intended security boundary between standard operational users and system administrators, creating a pathway for privilege escalation through indirect means rather than direct exploitation of authentication flaws.
The technical mechanism of this vulnerability leverages CAPEC-122, Privilege Abuse, where an attacker exploits legitimate permissions to perform actions outside their authorized scope. Specifically, the custom inference service allows clients to specify external endpoints or callback URLs where results should be sent or from which model definitions are fetched. Due to the missing authorization check, a low-privileged user can inject arbitrary hostnames into these fields. When Elasticsearch processes this request, it initiates an outbound connection to the attacker-controlled server without verifying if the initiating user has the right to define such network paths. This behavior transforms what should be a read-only or execution-limited operation into a vector for unauthorized network interaction. The system treats all inference requests with equal trust regarding their destination configuration, ignoring the principle of least privilege that dictates users should only have access to resources strictly necessary for their specific role.
The operational impact of this vulnerability is severe and multifaceted, primarily centering on information disclosure through credential exposure. Elasticsearch configurations often store sensitive authentication tokens, API keys, or service account credentials required to communicate with external machine learning services or internal microservices. When a malicious actor directs the inference traffic to their own server, they can intercept these outbound requests in transit or capture them at rest if logging is enabled on the attacker's infrastructure. This allows the extraction of administrator-provisioned credentials that were intended for secure, authorized communication channels. Furthermore, this capability enables SSRF-like scenarios where the Elasticsearch node itself becomes a proxy for network reconnaissance against internal services that may not be exposed to external networks but are accessible from within the cluster environment. The attacker can thus map out internal infrastructure, identify vulnerable services, or exfiltrate data stored in backend systems reachable only via the Elasticsearch host's network interface.
Mitigation strategies must address both immediate remediation and long-term architectural improvements. Administrators should immediately restrict access to the custom inference service endpoints by applying strict role-based access control policies that limit configuration changes exclusively to users with explicit administrative privileges, such as superuser or dedicated ML administrator roles. Network-level controls are also critical; implementing egress filtering rules on Elasticsearch nodes can prevent outbound connections to unauthorized IP addresses or domains, thereby neutralizing the ability of an attacker to direct traffic to their own servers even if they successfully exploit the authorization flaw. Additionally, enabling detailed audit logging for all inference service interactions allows security teams to detect anomalous patterns indicative of this abuse, such as frequent requests pointing to external IPs not previously seen in legitimate operations. Upgrading Elasticsearch to versions where this specific access control logic has been hardened is essential, alongside regular reviews of user permissions to ensure that privilege creep does not inadvertently grant excessive network configuration rights to standard operational accounts.