HoneyTrap: A Deceptive Multi-Agent Shield for Safer AI Chats
Turning jailbreakers into the trapped
Jailbreak prompts can trick chatbots into breaking rules. HoneyTrap flips the script: a deceptive, multi-agent defense that engages attackers, wastes their time, and keeps helpful answers flowing for regular users.
- Threat Interceptor: spots risky prompts early.
- Misdirection Controller: steers attackers into harmless honeypots.
- Forensic Tracker: logs and analyzes attack steps.
- System Harmonizer: coordinates safe, consistent replies.
The authors also release MTJ-Pro, a tough multi-turn jailbreak benchmark blending seven advanced strategies, plus two metrics: Mislead Success Rate (how well defense confuses attackers) and Attack Resource Consumption (how much time and compute it drains).
Results across GPT-4, GPT-3.5-turbo, Gemini-1.5-pro, and LLaMA-3.1: attack success drops by 68.77% vs top baselines, while MSR and ARC jump 118.11% and 149.16%. Even against adaptive attackers, HoneyTrap prolongs interactions and raises costs—without hurting normal queries.
Paper: https://arxiv.org/abs/2601.04034v1
Paper: https://arxiv.org/abs/2601.04034v1
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AI cybersecurity LLM jailbreak safety multiagent honeypot research
