TurnGate-0.1

TurnGate: Response-Aware Defense Against Hidden Malicious Intent in Multi-Turn Dialogue

<a href="https://arxiv.org/abs/2605.05630" target="_blank"> <img alt="arXiv" src="https://img.shields.io/badge/arXiv-TurnGate-red?logo=arxiv&style=for-the-badge" /> </a> <a href="https://turn-gate.github.io" target="_blank"> <img alt="Website" src="https://img.shields.io/badge/🌎_Homepage-blue.svg?style=for-the-badge" /> </a> <a href="https://github.com/Graph-COM/TurnGate" target="_blank"> <img alt="GitHub code" src="https://img.shields.io/badge/💻_Code_GitHub-black.svg?style=for-the-badge" /> </a> <a href="#cite" target="_blank"> <img alt="Cite" src="https://img.shields.io/badge/📖_Cite!-lightgrey?style=for-the-badge" /> </a> <a href="https://www.python.org/" target="_blank"> <img alt="Python" src="https://img.shields.io/badge/Python-3.12-blue?style=for-the-badge" /> </a>

Overview

TurnGate is a response-aware defense mechanism designed to detect and mitigate hidden malicious intent in multi-turn dialogue systems. Defending state-of-the-art multi-turn malicious attacks like CKA-Agent.

TurnGate-0.1

TurnGate is a specialized monitor designed to detect hidden malicious intent in multi-turn dialogues. Unlike traditional filters that look at queries in isolation, TurnGate is response-aware: it inspects the assistant's candidate response in the context of the full dialogue history to identify the precise "closure turn" where a harmful objective becomes actionable.

This repository contains the weights for TurnGate-0.1, a model trained on the Multi-Turn Intent Dataset (MTID) and optimized via reinforcement learning with turn-level process rewards.

Cite

If you find this repository useful for your research, please consider citing the following paper:

@misc{shen2026turnlateresponseawaredefense,
      title={One Turn Too Late: Response-Aware Defense Against Hidden Malicious Intent in Multi-Turn Dialogue}, 
      author={Xinjie Shen and Rongzhe Wei and Peizhi Niu and Haoyu Wang and Ruihan Wu and Eli Chien and Bo Li and Pin-Yu Chen and Pan Li},
      year={2026},
      eprint={2605.05630},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2605.05630}, 
}