Hive-AI: a defended multi-service honeypot framework for generative AI APIs

Public Large Language Model (LLM) APIs draw attacker traffic that defenders cannot see. Probes hit at the semantic layer—past TLS, past Web Application Firewall rules—and conventional intrusion detection picks up almost none of it. No open-source honeypot framework today captures this traffic at scale, and the few LLM-honeypot prototypes that exist push captured logs straight into a downstream LLM analyzer, exposing the analysis pipeline to indirect prompt injection through attacker-controlled inputs. We address both gaps with HIVE-AI, a 47,578-LoC honeypot framework deployed continuously on a single 4-vCPU/4-GB Virtual Private Server since 6 April 2026. Five protocol-faithful facades feed an eight-stage classification cascade with sub-5-ms p99 synchronous latency. The LLM threat-hunter is protected by a five-layer defense-in-depth architecture against indirect prompt injection, characterized theoretically and through operational field evidence; controlled per-layer validation is deferred to a follow-up deployment. We report these findings as a single-site, single-window case study: twenty days of operation captured 16,683 attacks from 1229 unique source IPs across 50 countries. The triangulation defense reduces mean adversarial evasion from 54.8 to 9.3% (paired test, $$p<0.001$$ ). Blind human validation on 100 events yields Cohen’s $$\kappa = 0.74$$ between the LLM and analyst majority—statistically indistinguishable from human-on-human $$\kappa = 0.71$$ . A local Ollama+Qwen2.5–1.5B backend reproduces the cloud severity verdict on 71% of events ( $$\kappa = 0.59$$ , moderate agreement), a promising low-cost alternative rather than a full substitute. Code and dataset are released under MIT License and CC BY 4.0; the source code is archived at Zenodo HIVE-AI: A Honeypot Source Code ( https://doi.org/10.5281/zenodo.19853664 ) and the live attack map at https://honeypot.ttpsec.cl:4443/livemap .