HoneyTrap: 通过坚韧的多智能体防御欺骗大语言模型攻击者以诱捕陷阱
HoneyTrap: Deceiving Large Language Model Attackers to Honeypot Traps with Resilient Multi-Agent Defense
AI总结：
HoneyTrap通过多智能体协作防御机制，有效降低大语言模型劫持攻击的成功率，提升防御效率与资源消耗效果。
摘要：
针对大语言模型（LLM）的劫持攻击构成了重大威胁，使攻击者能够绕过安全措施。然而，现有的反应性防御方法难以应对快速演变的多轮劫持攻击，其中攻击者持续深入攻击以利用漏洞。为了解决这一关键挑战，我们提出了HoneyTrap，一种新颖的欺骗LLM防御框架，利用协作防御者来对抗劫持攻击。它集成了四个防御代理，威胁拦截器、误导控制器、取证追踪器和系统调和器，每个代理执行专门的安全角色并协作完成欺骗防御。为了确保全面评估，我们引入了MTJ-Pro，一个具有挑战性的多轮渐进式劫持数据集，结合了七种先进的劫持策略，旨在逐步加深多轮攻击策略。此外，我们提出了两个新的指标：误导成功率（MSR）和攻击资源消耗（ARC），它们提供了比传统度量更细致的欺骗防御评估。在GPT-4、GPT-3.5-turbo、Gemini-1.5-pro和LLaMa-3.1上的实验结果表明，HoneyTrap相比最先进的基线在攻击成功率上平均减少了68.77%。值得注意的是，即使在专门的自适应攻击者设置中，HoneyTrap仍表现出坚韧，利用欺骗互动延长交互时间，显著增加了成功利用所需的时间和计算成本。与简单的拒绝不同，HoneyTrap战略性地浪费攻击者的资源而不影响良性查询，从而将MSR和ARC分别提高了118.11%和149.16%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
