Overview
In the world of cybersecurity, the vulnerability CVE-2025-25014 is a significant threat to the integrity and security of systems running Kibana. This vulnerability is a prototype pollution flaw, an insidious type of vulnerability that can lead to a variety of detrimental security impacts. The affected software, Kibana, is a popular open-source data visualization and exploration tool used for log and time-series analytics, application monitoring, and operational intelligence use cases. The vulnerability, if exploited, allows an attacker to execute arbitrary code via carefully crafted HTTP requests to machine learning and reporting endpoints. It is thus of paramount importance for all users of Kibana to understand this vulnerability and to apply mitigation measures.
Vulnerability Summary
CVE ID: CVE-2025-25014
Severity: Critical (9.1 CVSS score)
Attack Vector: Network
Privileges Required: Low
User Interaction: None
Impact: System Compromise, Data Leakage
Affected Products
No phone number, email, or personal info required.
Product | Affected Versions
Kibana | All versions prior to the latest patched release
How the Exploit Works
The CVE-2025-25014 vulnerability involves a prototype pollution flaw in Kibana. Prototype pollution is essentially a vulnerability caused when an attacker is able to manipulate the prototype of a JavaScript object, which can lead to the addition or modification of existing properties that will then exist on all objects.
In the context of this vulnerability, an attacker sends a specially crafted HTTP request to the machine learning and reporting endpoints of Kibana. This request can manipulate the prototype of an object, leading to unexpected behavior in the application. The consequence of this vulnerability is arbitrary code execution, which means that an attacker can run any command of their choice on the affected system.
Conceptual Example Code
The following is a conceptual example of how an HTTP request exploiting this vulnerability might look. This is pseudocode and is for illustrative purposes only.
POST /api/machine_learning/anomalies HTTP/1.1
Host: target.example.com
Content-Type: application/json
{ "__proto__": { "exec": "rm -rf / --no-preserve-root" } }In this example, the “__proto__” object is being manipulated to execute a dangerous command that could wipe the server’s file system. This clearly illustrates the severity of this vulnerability and underscores the importance of mitigating it effectively.
