AI Platforms:

Reconnaissance

Guides for mapping AI endpoints, models, APIs, and dependencies

6 guides • Created Mar 18, 2026 • Updated Mar 29, 2026

6 guides

Discovering AI Applications and LLM Endpoints

Techniques for identifying exposed AI/LLM endpoints, APIs, and model interfaces during recon, including fingerprinting frameworks, discovering hidden routes, and mapping AI attack surfaces.

Mar 18, 2026 • 2 min read

Enumerating Vector Databases and RAG Pipeline Components

Techniques for discovering and mapping vector database instances, embedding endpoints, and RAG pipeline components to identify attack surface in AI-driven applications.

Mar 18, 2026 • 3 min read

Finding AI Supply Chain Components

Techniques for mapping AI supply chain components including models, datasets, APIs, and dependencies to identify attack surfaces in AI platform environments.

Mar 18, 2026 • 4 min read

Fingerprinting Model Providers and Versions

Techniques to identify AI model providers, versions, and architectures through behavioral probing, response analysis, and metadata extraction.

Mar 18, 2026 • 4 min read

Identifying Exposed Model Servers and Inference APIs

Techniques for discovering publicly exposed ML model servers, inference endpoints, and AI APIs through passive recon, port scanning, and service fingerprinting.

Mar 18, 2026 • 3 min read

Mapping AI Dependencies, SDKs, and Frameworks

Identify and enumerate AI/ML frameworks, SDKs, and third-party dependencies within target environments to uncover attack surface and supply chain risks.

Mar 18, 2026 • 4 min read

Reconnaissance against AI targets follows the same passive-first principle as traditional reconnaissance, with an expanded scope. Beyond identifying hostnames and open ports, AI reconnaissance maps the model stack , which foundation model is deployed, what fine-tuning has been applied, which orchestration framework is in use, what tools are connected, and whether the deployment exposes vector databases or model serving endpoints directly.

Much of this information is available without touching the target. Job postings reveal the AI stack. GitHub repositories expose fine-tuning scripts, requirements files, and model configurations. Public cloud storage buckets contain training datasets and model artifacts. Certificate transparency logs and subdomain enumeration surface model serving endpoints and internal API gateways before any active probing begins.