牛哥精选 · 半年

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📝 深度技术 arXiv NLP 2026-06-16

From ASR to ASP: Evaluating Prompt Attack Vulnerabilities Against Open-Source LLMs

从攻击成功率到攻击成功概率，系统性评估开源大模型提示攻击漏洞，为安全防护提供新视角。

arXiv:2505.14368v2 Announce Type: replace-cross Abstract: Recent studies demonstrate that Large Language Models (LLMs) are vulnerable to attacks that …

提示攻击大模型安全 asp asr 漏洞评估

📝 深度技术 arXiv AI 2026-06-02

Dive into Ambiguity: A*-Inspired Multi-Agents Commonsense Obfuscation Attack on LLM Prompts

A*算法驱动多智能体，对LLM提示发起常识混淆攻击，揭示大模型语言理解的脆弱性。

arXiv:2606.01441v1 Announce Type: new Abstract: Large language models (LLMs) excel in reasoning and knowledge-intensive tasks but remain vulnerable to…

llm安全提示攻击多智能体常识混淆 a*算法

🤖 AI·大模型 arXiv AI 2026-05-27

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks

系统研究大语言模型对提示攻击的鲁棒性，提出增强方法，为AI安全提供新思路

arXiv:2506.03627v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable performance across various tasks b…

大语言模型提示攻击鲁棒性安全性对抗性攻击

📅 日期

2026-05-20 2026-05-19

🐂 牛哥精选

From ASR to ASP: Evaluating Prompt Attack Vulnerabilities Against Open-Source LLMs

Dive into Ambiguity: A*-Inspired Multi-Agents Commonsense Obfuscation Attack on LLM Prompts

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks

📅 日期