新方法利用大语言模型结合上下文信息，智能修复软件回归错误，经验评估验证有效性
arXiv:2506.13182v2 Announce Type: replace-cross Abstract: [...] Since then, various APR approaches, especially those leveraging the power of large lan…
实证评估开源LLM代理能否替代传统SAST工具，结果可能颠覆安全测试领域。
arXiv:2606.11672v1 Announce Type: cross Abstract: This paper explores the value of agentic AI tools for cybersecurity purposes. We evaluate the effica…
针对LLM代理的自动化提示注入攻击在真实场景中未被充分研究，本文提供了系统性的实证评估。
arXiv:2606.10525v1 Announce Type: cross Abstract: Indirect prompt injection poses a critical threat to LLM agents that interact with untrusted externa…
400次重复实验揭示：大模型做黑客竟如此「不稳定」？首个LLM渗透测试一致性量化研究。
arXiv:2605.30096v1 Announce Type: cross Abstract: Large language models (LLMs) can autonomously conduct multi-stage cyber attacks, but the consistency…