GitHub 红队武器库🚨
📦 GitHub 全球红队渗透资源中转站。 旨在收录那些“好用却难找”的安全项目。 🔗 定时推送:GitHub Trending (Security) 🛠 必备清单:后渗透、远控、免杀、提权工具集 📅 更新频率:每日精选,绝不灌水。 ⚠️ 本频道仅供安全研究与授权测试使用。
Show more📈 Analytical overview of Telegram channel GitHub 红队武器库🚨
Channel GitHub 红队武器库🚨 (@githubredteam) in the Chinese language segment is an active participant. Currently, the community unites 13 053 subscribers, ranking 9 709 in the Technologies & Applications category and 16 186 in the China region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 13 053 subscribers.
According to the latest data from 29 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 290 over the last 30 days and by 13 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 0.24%. Within the first 24 hours after publication, content typically collects 0.30% reactions from the total number of subscribers.
- Post reach: On average, each post receives 31 views. Within the first day, a publication typically gains 39 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 1.
- Thematic interests: Content is focused on key topics such as github, fork, 异性spa, cve-2026, vme.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“📦 GitHub 全球红队渗透资源中转站。
旨在收录那些“好用却难找”的安全项目。
🔗 定时推送:GitHub Trending (Security)
🛠 必备清单:后渗透、远控、免杀、提权工具集
📅 更新频率:每日精选,绝不灌水。
⚠️ 本频道仅供安全研究与授权测试使用。”
Thanks to the high frequency of updates (latest data received on 30 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
本项目基于 Multi-Agent 协同架构与 AI 驱动安全分析能力,构建了一套自动化渗透测试与 SRC 漏洞挖掘平台,重点解决传统安全测试中高度依赖人工经验、漏洞验证效率低、误报率高以及大规模资产难以持续化检测的问题。
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