Qdrant UI Unauthenticated Access Scanner
This scanner detects the use of Qdrant UI Unauthenticated Access in digital assets. Unauthorized individuals can access the Qdrant UI dashboard without authentication. This detection helps address potential security risks.
Short Info
Level
Single Scan
Single Scan
Can be used by
Asset Owner
Estimated Time
10 seconds
Time Interval
16 days 23 hours
Scan only one
URL
Toolbox
The Qdrant UI is a dashboard interface utilized by organizations for managing their Qdrant vector search engine deployments. This tool is typically used by developers and data engineers within machine learning and data science teams. It allows users to interact with and manage collections of vectors, which are critical for various AI applications. The Qdrant UI provides a user-friendly way to visualize data, manage settings, and perform search operations on vector databases. It is typically deployed in enterprise environments and can be accessed via web browsers for ease of use. This makes it a crucial asset for teams relying on Qdrant for high-performance vector similarity searches.
The vulnerability in question is an unauthenticated access issue in the Qdrant UI. This means that the dashboard can be accessed without the need for authentication credentials. As a result, anyone with network access to the server can directly interact with the Qdrant system without verification. This vulnerability can lead to information leakage and unauthorized operations. The main concern is allowing unintended access that could compromise the integrity and confidentiality of the data managed through the Qdrant UI.
Technical details of this vulnerability include the lack of an authentication mechanism on the UI endpoint, specifically in the path that accesses collections. The vulnerability is identified by examining the HTTP response of the /collections endpoint, which returns a 200 status code and specific JSON body elements. These indicators confirm the presence of a Qdrant UI instance that is accessible without credentials. The issue is exacerbated when the interface is exposed to untrusted networks.
Exploiting this vulnerability could allow an attacker to view, modify, or delete collections of vector data inadvertently. This unauthorized access can disrupt services relying on the vector search capabilities and potentially expose sensitive data. Moreover, attackers might manipulate the data to interfere with machine learning models and services that depend on this data. Such actions can have significant consequences for businesses and applications relying on accurate data processing and integrity.
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