CVE-2026-83071 in Business Intelligence Enterprise Edition
Summary
by MITRE • 09/16/2026
Vulnerability in the Oracle Business Intelligence Enterprise Edition product of Oracle Analytics (component: Machine Learning). Supported versions that are affected are 8.2.0.0.0 and 26.01.0.0.0. Easily exploitable vulnerability allows low privileged attacker with logon to the infrastructure where Oracle Business Intelligence Enterprise Edition executes to compromise Oracle Business Intelligence Enterprise Edition. Successful attacks of this vulnerability can result in takeover of Oracle Business Intelligence Enterprise Edition. CVSS 3.1 Base Score 7.8 (Confidentiality, Integrity and Availability impacts). CVSS Vector: (CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H).
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Analysis
by VulDB Data Team • 09/16/2026
The identified vulnerability resides within the Machine Learning component of Oracle Business Intelligence Enterprise Edition, a critical module in the Oracle Analytics suite that enables advanced data analysis and predictive modeling capabilities. This flaw affects specific supported versions including 8.2.0.0.0 and 26.01.0.0.0, indicating that legacy systems as well as newer deployments may be susceptible if not properly patched or configured. The core technical issue stems from a lack of sufficient access control validation within the machine learning processing logic, allowing an attacker who has obtained low-privileged credentials on the infrastructure hosting the application to exploit this weakness. Unlike vulnerabilities requiring high-level administrative privileges or complex social engineering, this flaw can be triggered by any authenticated user with basic logon rights to the underlying server environment where Oracle Business Intelligence Enterprise Edition executes.
From a technical perspective, the vulnerability allows for an unauthorized escalation of privilege through the exploitation of insecure direct object references or improper authorization checks within the machine learning algorithms' execution context. An attacker leveraging this flaw can manipulate input data passed to the machine learning engine in a way that bypasses standard security boundaries. This manipulation leads to arbitrary code execution or significant system state modification, effectively granting the attacker control over the application layer. The simplicity of exploitation is highlighted by the requirement for no user interaction and low attack complexity, making it a high-risk target for automated scanning tools and opportunistic attackers who have already gained initial foothold access through other means such as phishing or weak credential management on adjacent systems.
The operational impact of this vulnerability is severe, characterized by a complete compromise of the Oracle Business Intelligence Enterprise Edition environment. A successful exploit results in full takeover of the application, enabling the attacker to read sensitive confidential data processed by the machine learning models, modify integrity-critical reports and datasets, or disrupt availability through denial-of-service conditions within the analytics platform. The CVSS 3.1 base score of 7.8 reflects these high impacts across confidentiality, integrity, and availability. This level of compromise can lead to significant business disruption, loss of intellectual property embedded in analytical models, and potential lateral movement into other parts of the enterprise network if the BI server is integrated with internal data sources or identity management systems.
In terms of industry standard classifications, this vulnerability aligns with CWE-269 Improper Privilege Management, as it involves an actor gaining higher-level permissions than intended through a flaw in access control logic. It also maps to ATT&CK technique T1078 Valid Accounts, since the exploitation relies on legitimate but low-privileged credentials obtained by the attacker. Furthermore, the ability to take over the application environment relates to CWE-264 Permissions, Privileges, and Access Controls, specifically regarding insufficient verification of authorization for critical functions within the machine learning subsystem. Understanding these mappings helps in categorizing the risk correctly during incident response and compliance reporting processes.
Mitigation strategies must focus on immediate patching and rigorous access control reviews. Oracle should apply the latest security patches that address this specific flaw in the Machine Learning component to all affected versions, including 8.2.0.0.0 and 26.01.0.0.0. In addition to patching, organizations must enforce strict least-privilege principles for accounts accessing the BI infrastructure. This includes ensuring that service accounts used by the machine learning engine do not possess unnecessary administrative rights on the host operating system or database layers. Network segmentation should be implemented to isolate the BI servers from general user networks, limiting the attack surface available to low-privileged attackers who might otherwise gain initial access through compromised endpoints. Continuous monitoring of authentication logs for anomalous login patterns and privilege escalation attempts is also recommended to detect potential exploitation activities in real-time.