CVE-2025-25014 is a critical security vulnerability identified in Elastic Kibana, specifically affecting versions 8.3.0, 8.18.0, and 9.0.0. The vulnerability is classified under CWE-1321, which relates to prototype pollution. Prototype pollution is a type of attack where an attacker manipulates the prototype of a base object in JavaScript, leading to unexpected behavior in the application. In this case, the vulnerability allows an attacker to send specially crafted HTTP requests targeting Kibana's machine learning and reporting endpoints. By exploiting this flaw, an attacker can achieve arbitrary code execution on the affected system. The vulnerability has a CVSS v3.1 score of 9.1, indicating a critical severity level. The vector details show that the attack can be performed remotely over the network (AV:N) with low attack complexity (AC:L), but requires high privileges (PR:H) and no user interaction (UI:N). The scope is changed (S:C), meaning the vulnerability affects resources beyond the initially vulnerable component. The impact on confidentiality, integrity, and availability is high (C:H/I:H/A:H), meaning an attacker can fully compromise the system. Although there are no known exploits in the wild at the time of publication, the potential for severe damage is significant. The vulnerability is particularly dangerous because it targets machine learning and reporting endpoints, which are critical components in Kibana for data analysis and visualization. Successful exploitation could allow attackers to execute arbitrary code, potentially leading to full system compromise, data theft, or disruption of services. No official patches or fixes are linked yet, emphasizing the need for immediate attention and mitigation by affected users.

For European organizations, the impact of CVE-2025-25014 could be substantial. Kibana is widely used across various sectors including finance, healthcare, government, and critical infrastructure for log analysis, monitoring, and data visualization. Exploitation of this vulnerability could lead to unauthorized access to sensitive data, manipulation of analytics results, and disruption of monitoring capabilities. This could impair incident response and operational awareness, increasing the risk of further attacks. Additionally, arbitrary code execution could allow attackers to move laterally within networks, escalate privileges, and deploy ransomware or other malware. Given the critical nature of the vulnerability and the high privileges required, organizations with less stringent access controls or exposed Kibana instances are at higher risk. The lack of user interaction needed for exploitation means automated attacks could be feasible once exploit code becomes available. Compliance with GDPR and other data protection regulations could be jeopardized if sensitive personal data is exposed or altered, leading to legal and financial consequences.

1. Immediate mitigation should include restricting access to Kibana's machine learning and reporting endpoints to trusted internal networks only, using network segmentation and firewall rules. 2. Implement strict authentication and authorization controls to ensure only privileged users can access Kibana features, minimizing the risk posed by the high privilege requirement. 3. Monitor network traffic for unusual or malformed HTTP requests targeting Kibana endpoints, using IDS/IPS systems or custom detection rules. 4. Apply virtual patching via Web Application Firewalls (WAFs) to block suspicious payloads that could exploit prototype pollution patterns. 5. Regularly audit and review Kibana configurations and user permissions to reduce the attack surface. 6. Stay informed on Elastic's official security advisories and apply patches promptly once available. 7. Consider deploying Kibana instances behind VPNs or zero-trust network architectures to limit exposure. 8. Conduct internal penetration testing and code reviews focusing on prototype pollution vulnerabilities in custom plugins or integrations with Kibana. These targeted actions go beyond generic advice by focusing on access control hardening, network-level protections, and proactive monitoring specific to the nature of this vulnerability.