Zoom Addresses EU Privacy Concerns and Updates Terms of Service Greetings! In response to discussions about potential EU privacy law implications, Zoom issues a statement and revises its Terms of Service. The focus? Ensuring customer data isn't utilized to train AI models. Zoom's statement and Terms affirm that user-generated content, including audio, video, chat, and more, isn't employed for training Zoom's or any third-party AI models. This step aims to dispel any concerns. Zoom initially shared its statement on August 7 and later updated it on August 11, aligned with the revised Terms. The shared stance now unequivocally states, "Zoom does not use any of your customer content to train AI models." Earlier, a Stack Diary article flagged changes to Zoom's March Terms, raising potential concerns about broad data utilization for AI model training. Zoom's quick response aims to address these concerns and reaffirm privacy commitments. #Zoom#PrivacyMatters#TermsOfService#AIModels#DataProtection#PrivacyLaw#TechUpdates
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🚨 GitHub 监控消息提醒 🚨发现关键词：#漏洞#验证#检测#分析 📦项目名称：LLM-MultiAgent-StackOverflow-Detector 👤项目作者：uguj 🛠开发语言： Python ⭐Star数量： 0 | 🍴Fork数量： 0 📅更新时间： 2026-05-18 10:51:12 📝项目描述： 基于ReAct范式的多智能体栈缓冲区溢出检测框架，用于C/C++代码漏洞分析。 - Finder Agent：基于libclang的AST解析，识别危险函数调用 - Tracer Agent：语法树回溯，分析参数可控性 - Hypothesis Agent：调用Qwen2.5-Coder大语言模型生成智能漏洞假设 - Verifier Agent：通过angr符号执行与objdump静态匹配进行二进制验证 框架形成“发现→追踪→假设→验证→反馈”的闭环检测流程。 本仓库包含毕业设计《基于大语言模型的栈溢出漏洞检测研究》的全部源代码、测试用例及实验结果。 🔗点击访问项目地址
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