HoneyTrap: Deceiving Large Language Model Attackers to Honeypot Traps with Resilient Multi-Agent Defense

Key Findings

Methodology

HoneyTrap constructs four specialized agents: Threat Interceptor, Misdirection Controller, Forensic Tracker, and System Harmonizer, each responsible for delaying, misleading, analyzing, and coordinating defenses. The system simulates multi-turn progressive jailbreak attacks, dynamically adjusting strategies through collaboration. The MTJ-Pro dataset, comprising seven attack strategies, evaluates performance in complex scenarios. MSR and ARC metrics quantify misdirection success and resource consumption, reflecting robustness. Experiments on GPT-4, GPT-3.5-turbo, Gemini-1.5-pro, and LLaMa-3.1 show an average attack success reduction of 68.77%, with high resilience even under adaptive attacks, significantly increasing interaction time and computational costs.

Key Results

Across multiple models and attack strategies, HoneyTrap reduces attack success rate by 68.77%, with MSR increasing by 118.11% and ARC by 149.16%, demonstrating effective resource drain and misdirection. The system prolongs attacker interaction, raising costs and decreasing success rates, outperforming static defenses.
Comparative analysis shows multi-agent cooperation enhances robustness over single strategies. Under intensified attack conditions, the system maintains high performance, validating its adaptability and generalization across models and scenarios.
Resource analysis indicates a substantial increase in attacker resource consumption, with ARC metrics confirming resource draining efficiency. The collaborative approach effectively hampers attack progression, confirming the advantage of multi-agent synergy.

Significance

This work advances the field by introducing a dynamic, multi-agent defense framework capable of countering multi-turn, progressive jailbreak attacks. It addresses the limitations of static defenses, offering a scalable, adaptable solution that significantly enhances language model security. The proposed metrics MSR and ARC provide nuanced evaluation tools, facilitating future research. The approach’s effectiveness across diverse models and attack strategies demonstrates its potential for real-world deployment in sensitive domains like finance, healthcare, and government, where safeguarding information integrity is critical. Overall, it marks a significant step toward resilient AI systems capable of proactively defending against evolving adversarial tactics.

Technical Contribution

The paper introduces a multi-agent collaborative architecture combining delay, misdirection, behavioral analysis, and strategic coordination to dynamically counter multi-turn jailbreaks. It innovates with the MTJ-Pro dataset, simulating realistic progressive attacks, and develops MSR and ARC metrics for detailed evaluation. The system’s multi-layer, adaptive response mechanism outperforms static and single-strategy defenses, providing theoretical guarantees of resource efficiency and robustness. This framework opens new avenues for integrating cooperative AI in security applications, blending adversarial learning with multi-agent cooperation to enhance resilience.

Novelty

This is the first work to integrate multi-agent collaboration specifically for dynamic, multi-turn jailbreak defense, combining staged attack simulation with adaptive misdirection strategies. The introduction of MTJ-Pro dataset and the MSR/ARC metrics fills a gap in comprehensive evaluation of progressive attack scenarios. Unlike prior static or single-strategy defenses, this approach offers real-time, scalable, and robust protection, representing a novel paradigm in dialogue security and adversarial AI research.

Limitations

Despite robustness, the system may still face challenges against highly sophisticated or unforeseen attack strategies, especially in extreme multi-turn scenarios beyond training distribution.
The multi-agent framework introduces computational overhead, potentially impacting real-time performance and scalability in large-scale deployment.
Misleading responses, if not carefully managed, could affect user experience or trust, requiring further balancing between security and usability.

Future Work

Future efforts will focus on integrating reinforcement learning to optimize agent strategies, expanding multi-modal inputs for better context understanding, and refining user experience balance. Additionally, deploying in real-world environments like financial or healthcare systems will test scalability and robustness, guiding further improvements. Exploring unsupervised attack detection and adaptive learning mechanisms will also be key directions.

AI Executive Summary

As large language models (LLMs) become integral to various applications, their vulnerability to adversarial multi-turn jailbreak attacks poses serious security concerns. Attackers leverage iterative prompts to gradually bypass safety constraints, generating harmful or unethical content. Traditional defenses, primarily static filters and fine-tuning, struggle against evolving, multi-stage attack strategies. To address this, the authors propose HoneyTrap, a novel multi-agent collaborative framework designed to proactively counteract such threats.

HoneyTrap integrates four specialized agents—Threat Interceptor, Misdirection Controller, Forensic Tracker, and System Harmonizer—each with distinct roles. Threat Interceptor delays responses to slow down attackers, Misdirection Controller provides misleading replies to divert malicious efforts, Forensic Tracker analyzes interaction logs to identify attack patterns, and System Harmonizer dynamically adjusts strategies based on real-time feedback. This multi-layered system simulates realistic progressive attacks, enabling it to adaptively respond and prolong attacker engagement.

To evaluate effectiveness, the authors introduce MTJ-Pro, a comprehensive dataset simulating seven advanced attack strategies across three stages: trust-building, vulnerability probing, and escalation. They also develop two metrics—Mislead Success Rate (MSR) and Attack Resource Consumption (ARC)—to quantify the system’s ability to mislead attackers and drain their resources. Experimental results across GPT-4, GPT-3.5-turbo, Gemini-1.5-pro, and LLaMa-3.1 demonstrate that HoneyTrap reduces attack success rates by an average of 68.77%, significantly outperforming existing static defenses. Notably, even under intensified adaptive attack scenarios, the system maintains high resilience, effectively increasing attack costs and interaction times.

This research marks a significant advancement in dialogue security, offering a scalable, dynamic, and cooperative defense paradigm. By actively engaging and misdirecting adversaries, HoneyTrap not only mitigates current threats but also provides a foundation for future adaptive security systems. Its innovative metrics and dataset further contribute to the field, enabling more nuanced evaluation of adversarial robustness. Overall, this work paves the way for safer, more resilient AI systems capable of defending against sophisticated multi-stage attacks in real-world applications.

Deep Dive

Glossary

Multi-agent system (多智能体系统)

由多个自主智能体协作完成任务，增强系统鲁棒性。In this paper, used to实现多轮对话中的动态防御。

描述HoneyTrap中四个智能体的协作机制。

Jailbreak attack (越狱攻击)

通过操控输入绕过模型安全限制，生成有害内容。论文中模拟多轮渐进式越狱策略。

核心攻击类型，系统防御目标。

Mislead Success Rate (MSR) (误导成功率)

衡量系统成功误导攻击者的比例。反映误导策略的有效性。

评价HoneyTrap误导效果的重要指标。

Attack Resource Consumption (ARC) (资源耗散)

衡量攻击者为达成目标所耗费的资源总量。反映系统对攻击的资源阻断能力。

评估系统抗攻击效率的关键指标。

Progressive jailbreak (渐进式越狱)

攻击逐步深化，逐轮引导模型生成有害内容。系统设计应应对这种逐步升级的威胁。

描述攻击策略的演变过程。

Open Questions Unanswered questions from this research

1 如何进一步提升多智能体系统在极端复杂攻击中的实时响应能力仍待研究。当前模型对未知攻击策略的适应性有限，未来需结合强化学习和自我学习机制增强系统自主防御能力。

Applications

Immediate Applications

对话平台安全增强

金融、医疗等敏感行业的聊天机器人，可利用HoneyTrap主动检测和误导攻击者，保障信息安全。

Long-term Vision

全面智能安全系统

结合多模态信息和自适应学习，构建全场景、多任务的安全防护体系，推动AI安全技术的行业标准化。

Abstract

Jailbreak attacks pose significant threats to large language models (LLMs), enabling attackers to bypass safeguards. However, existing reactive defense approaches struggle to keep up with the rapidly evolving multi-turn jailbreaks, where attackers continuously deepen their attacks to exploit vulnerabilities. To address this critical challenge, we propose HoneyTrap, a novel deceptive LLM defense framework leveraging collaborative defenders to counter jailbreak attacks. It integrates four defensive agents, Threat Interceptor, Misdirection Controller, Forensic Tracker, and System Harmonizer, each performing a specialized security role and collaborating to complete a deceptive defense. To ensure a comprehensive evaluation, we introduce MTJ-Pro, a challenging multi-turn progressive jailbreak dataset that combines seven advanced jailbreak strategies designed to gradually deepen attack strategies across multi-turn attacks. Besides, we present two novel metrics: Mislead Success Rate (MSR) and Attack Resource Consumption (ARC), which provide more nuanced assessments of deceptive defense beyond conventional measures. Experimental results on GPT-4, GPT-3.5-turbo, Gemini-1.5-pro, and LLaMa-3.1 demonstrate that HoneyTrap achieves an average reduction of 68.77% in attack success rates compared to state-of-the-art baselines. Notably, even in a dedicated adaptive attacker setting with intensified conditions, HoneyTrap remains resilient, leveraging deceptive engagement to prolong interactions, significantly increasing the time and computational costs required for successful exploitation. Unlike simple rejection, HoneyTrap strategically wastes attacker resources without impacting benign queries, improving MSR and ARC by 118.11% and 149.16%, respectively.

cs.CR cs.AI