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arXiv AI By Hicham Hammouchi, Gabriele Lenzini

InferD: A Honeypot Framework for AI Inference Infrastructure

Read the original on arXiv AI →

InferD is a low‑interaction honeypot framework designed to emulate AI inference infrastructure without executing any real model inference or tool calls. It achieves this by answering all adversary requests from static response pools across four deception planes—transport, service protocol, vulnerability surface, and agentic session—while adding a provenance plane to link credentials to evidence of downstream model‑mediated processing. The framework addresses the unique deception challenges posed by AI services, which differ from conventional network services in observable behavior and authorization flows.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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