CVE-2026-71107 in Business Intelligence Enterprise Edition
Summary
by MITRE • 08/19/2026
Vulnerability in the Oracle Business Intelligence Enterprise Edition product of Oracle Analytics (component: Analytics Server). Supported versions that are affected are 8.2.0.0.0 and 26.01.0.0.0. Easily exploitable vulnerability allows unauthenticated attacker with network access via HTTP to compromise Oracle Business Intelligence Enterprise Edition. Successful attacks of this vulnerability can result in unauthorized access to critical data or complete access to all Oracle Business Intelligence Enterprise Edition accessible data. CVSS 3.1 Base Score 7.5 (Confidentiality impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N).
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Analysis
by VulDB Data Team • 08/19/2026
The identified vulnerability resides within the Analytics Server component of Oracle Business Intelligence Enterprise Edition, a core part of the Oracle Analytics suite used for data visualization and business intelligence reporting. This security flaw affects specific legacy and current versions, notably 8.2.0.0.0 through to 26.01.0.0.0, indicating that systems operating on these platforms are susceptible to exploitation over standard network protocols. The nature of this vulnerability is particularly concerning because it allows for unauthenticated access, meaning an attacker does not need valid credentials or prior authentication to initiate the attack vector. This significantly lowers the barrier to entry for malicious actors seeking to compromise the integrity and confidentiality of organizational data assets hosted on these systems.
From a technical perspective, the flaw enables unauthorized attackers with network access via HTTP to exploit weaknesses in how the Analytics Server processes requests. The absence of required authentication checks at critical points within the application logic allows external parties to bypass security controls entirely. This lack of proper authorization verification is a classic example of broken access control, where the system fails to restrict actions based on user privileges or identity validation. Consequently, an attacker can interact directly with sensitive endpoints and data structures that should be protected by authentication mechanisms. The exploitation path relies primarily on network connectivity via HTTP, which suggests that if the service is exposed to untrusted networks such as the internet without adequate perimeter defenses like web application firewalls or strict access control lists, it becomes a prime target for automated scanning tools and remote exploits.
The operational impact of this vulnerability is severe due to its high confidentiality rating in the CVSS 3.1 scoring model, which assigns a base score of 7.5. Successful exploitation grants the attacker unauthorized access to critical data within the Oracle Business Intelligence environment. Depending on the sensitivity of the datasets managed by the organization, this could lead to the exposure of proprietary business strategies, customer personal information, financial records, or other confidential metrics. In worst-case scenarios, an attacker may gain complete access to all accessible data within the BI system, leading to significant regulatory compliance violations under frameworks such as GDPR, HIPAA, or PCI-DSS. While the current assessment indicates no immediate impact on integrity or availability, the potential for further exploitation after initial unauthorized access cannot be ruled out, potentially escalating into more complex attacks involving data manipulation or lateral movement within the internal network.
To mitigate this risk, organizations must prioritize patching and updating their Oracle Analytics infrastructure to versions that include security fixes addressing these authentication flaws. Since unauthenticated remote code execution or data exfiltration is possible via HTTP, it is imperative to ensure that no instances of vulnerable BI servers are directly exposed to public networks without robust intermediary protections. Implementing network segmentation to isolate analytics servers from general-purpose web traffic can reduce the attack surface significantly. Additionally, deploying Web Application Firewalls with rules specifically designed to detect and block exploitation attempts related to broken access control can provide an additional layer of defense. Regular security audits and penetration testing should be conducted to verify that authentication mechanisms are functioning correctly across all supported versions. Monitoring for unusual HTTP requests targeting analytics endpoints can also aid in early detection of attempted exploits, allowing incident response teams to react before data is compromised.