Determined AI Detection Scanner

Determined AI is an open-source deep learning training platform used by data scientists, machine learning engineers, and researchers. It provides capabilities for distributed training, hyperparameter tuning, and experiment tracking. The platform integrates seamlessly into existing machine learning workflows, acting as a catalyst for rapid prototyping and model development. With its web-based UI and REST/gRPC-gateway API served by the "master" component, users can track and monitor their experiments efficiently. Its integration supports various compute environments, from on-premise clusters to cloud-based infrastructures, allowing flexible execution. The software's open-source nature includes comprehensive documentation and community support, enhancing collaborative development and innovation.

This scanner's detection focuses on the Determined AI platform by identifying key elements in its metadata and application structure. By looking for specific application titles and meta descriptions, this scanner ensures accurate identification of the platform presence. Detecting these elements provides users with insight into their digital asset configurations. Assessing the usage of Determined AI helps organizations understand their software deployment landscape, aiding risk assessment and management. This detection process does not involve intrusive testing or the exploitation of vulnerabilities, ensuring a non-disruptive approach to identifying the platform's presence. The simplicity of this scanner aligns well with its purpose, aimed at aiding IT teams in discovery efforts.

The detection details revolve around matching both the application title and meta description, which are served at the application root. Using a GET request to the base URL, the scanner checks for these specific indicators in the HTTP response. The detection mechanism requires both "Determined Deep Learning Training Platform" and "" to be present for a positive identification. This strict condition minimizes false positives, ensuring the scanner's reliability. The process leverages HTTP 200 status code checks to confirm site availability before additional checks. These techniques enable precise identification of Determined AI deployments across different environments, promoting consistency in discovery processes.

Possible effects of this scanner relate primarily to the improvement of asset visibility and inventory management in IT environments by detecting Determined AI. Understanding the presence of this platform aids in formulating an effective ecosystem strategy and mitigating any associated risks. Security professionals can use this information to plan further security assessments and decide on appropriate countermeasures. Awareness of Determined AI's installation also enables better resource allocation and capability planning, enhancing the deployment's value. Additionally, identifying the platform assists in complying with organizational software use policies and helps assure licensing adherence and governance.

REFERENCES

• https://github.com/determined-ai/determined
• https://docs.determined.ai