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English(EN) PISmith: Reinforcement Learning-based Red Teaming for Prompt Injection Defenses

新的RL框架PISmith测试并攻破提示注入防御

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了PISmith，一个新颖的强化学习（RL）框架，旨在严格测试大型语言模型（LLMs）中提示注入防御的有效性。该框架训练一个攻击LLM，通过在黑盒设置中优化注入的提示来发现漏洞，在黑盒设置中只能观察到LLM的输出。PISmith结合了自适应熵正则化和动态优势加权，以克服奖励稀疏性等挑战，从而能够从罕见的成功攻击中进行更有效的学习。在13个基准和代理设置上的评估表明，PISmith能够成功突破最先进的防御，其性能优于现有的攻击方法，并对包括GPT-4o mini和GPT-5 nano在内的开源和闭源LLMs都取得了高成功率。 AI

影响 这项研究突显了LLM防御在面对复杂攻击时存在的持续性漏洞，可能影响已部署AI代理的安全性和可信度。

排序理由 详细介绍LLM安全评估新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的RL框架PISmith测试并攻破提示注入防御

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详细介绍LLM安全评估新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Chenlong Yin, Runpeng Geng, Yanting Wang, Jinyuan Jia ·

    PISmith：基于强化学习的红队测试用于提示注入防御

    arXiv:2603.13026v2 Announce Type: replace Abstract: Prompt injection poses serious security risks to real-world LLM applications, particularly autonomous agents. Although many defenses have been proposed, their robustness against adaptive attacks remains insufficiently evaluated,…

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