Seldon Core Technology Detection Scanner
This scanner detects the use of Seldon Core in digital assets.
Short Info
Level
Single Scan
Single Scan
Can be used by
Asset Owner
Estimated Time
10 seconds
Time Interval
10 days 5 hours
Scan only one
URL
Toolbox
Seldon Core is an open-source MLOps framework used for deploying, monitoring, and scaling machine learning models on Kubernetes environments. It is utilized by data scientists and developers who aim to implement robust machine learning pipelines efficiently. Seldon Core allows for the management of complex model configurations and inference scaling from small experiments to large production deployments. It facilitates the running of machine learning workloads, offering features like multi-framework support and powerful deployment patterns. Leveraging Kubernetes, Seldon Core ensures flexibility and reliability for enterprise-level AI model operations. As such, it is a pivotal tool for organizations seeking to refine and automate their AI delivery processes.
The scanner is specifically designed to detect the presence of Seldon Core's components within a network. It identifies exposed instances where the Seldon Core's Pipeline Gateway component is accessible. The detection focuses on recognizing unauthenticated status endpoints and Server response headers that are indicative of Seldon Core. This detection process is crucial as it highlights potential security configurations that may need addressing. Identifying the presence of such components can assist in ensuring the organization's machine learning infrastructure is not inadvertently exposed.
The detection details hinge on analyzing the HTTP response of the target system. By matching specific JSON banners in the body and associated headers, it identifies systems that are running the Seldon Pipeline Gateway. The detection mechanism specifically looks for key-value pairs within the JSON response that align with Seldon Core signatures. The template operates by sending GET requests to endpoints to determine the presence of these signatures. This allows administrators to verify if components are accessible to unauthenticated entities inadvertently. Moreover, it checks for matching server response headers to ensure accuracy in detection.
If exploited, unauthorized users could potentially access configuration information regarding machine learning pipelines. This exposure could lead to further probing into the organization's machine learning API, possibly revealing sensitive data or model patterns. The potential unauthorized access highlights the risk of data breaches or manipulation if proper controls and checks are not enforced. Additionally, exposed endpoints could become targets for denial-of-service attacks or other forms of disruptions. Such security misconfigurations could severely impact the performance and integrity of the machine learning operations.
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