InferD: A Honeypot Framework for AI Inference Infrastructure

Summary

InferD is a low-interaction honeypot framework built to measure attacks against AI inference infrastructure, such as LLM serving stacks, inference APIs, vector databases and agent gateways. It answers every observed request from static response pools and never executes adversary input, a principle the authors call deception without inference. The framework is organized into four deception planes and a provenance plane that links planted credentials to downstream model-mediated processing.