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 13 149 subscribers, ranking 9 592 in the Technologies & Applications category and 15 900 in the China region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 13 149 subscribers.
According to the latest data from 10 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 243 over the last 30 days and by 16 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 0.28%. Within the first 24 hours after publication, content typically collects 0.42% reactions from the total number of subscribers.
- Post reach: On average, each post receives 37 views. Within the first day, a publication typically gains 55 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 11 July, 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.
Secure PHP weather application with user authentication, CSRF protection, and a server-side API proxy using environment variables.
🔗 点击访问项目地址A list of useful Powershell scripts with 100% AV bypass (At the time of publication).
🔗 点击访问项目地址本项目是基于大语言模型的可解释钓鱼网站检测系统,基于 URL、正文与外链等结构化特征上进行分类与解释生成,通过功能解耦双LoRA与专家-学生知识蒸馏完成模型压缩。仓库提供从模型训练训练、统一推理评测,到黑盒/白盒对抗样本生成、主动学习与增量复训、动态路由阈值扫描以及 LLM-as-a-Judge 解释质量评估的完整脚本链路,可用于钓鱼网站检测算法验证、可解释安全分析、对抗鲁棒性研究等场景。
🔗 点击访问项目地址Self-contained AppSec harness suite: threat modeling, SAST/DAST, PoC validation (PoC-or-GTFO), nuclei + Burp Pro integration, autonomous goal-driven loop.
🔗 点击访问项目地址CVE-2026-33825
🔗 点击访问项目地址本项目是基于大语言模型的可解释钓鱼网站检测系统,基于 URL、正文与外链等结构化特征上进行分类与解释生成,通过功能解耦双LoRA与专家-学生知识蒸馏完成模型压缩。仓库提供从模型训练训练、统一推理评测,到黑盒/白盒对抗样本生成、主动学习与增量复训、动态路由阈值扫描以及 LLM-as-a-Judge 解释质量评估的完整脚本链路,可用于钓鱼网站检测算法验证、可解释安全分析、对抗鲁棒性研究等学术场景。
🔗 点击访问项目地址本项目实现了一个基于RoBERTa-TextCNN的Web攻击检测模型。项目以HTTP请求文本为输入,完成请求字段提取、编码解码、统一文本表示、RoBERTa领域继续预训练以及多类别攻击检测,支持Normal、SQLi、XSS、SSI、XPath、LDAPi、PathTraversal和OSCommandInjection等类别的识别。
🔗 点击访问项目地址本项目是面向恶意 URL 检测的研究型系统,围绕组件分割与多源特征融合方法开展二分类识别研究,基于 PyTorch 构建模型训练与评测流程,通过对 URL 的域名、路径、查询参数等组件进行结构化拆分,并融合字符级序列特征、域名分支特征和统计结构特征,实现对可疑链接的高精度识别。系统支持主模型训练、基线对比、消融实验、分布外(OOD)鲁棒性评测和推理时延测试,可用于网络钓鱼 识别、恶意链接拦截、邮件与短信安全过滤、浏览器安全防护等场景。
🔗 点击访问项目地址A state-machine-driven simulation environment for distributed XML Bomb and XXE attacks.
🔗 点击访问项目地址GPU bypass for IBM POWER8/POWER9 and PowerPC Mac - internal PCIe rescan + OCuLink support
🔗 点击访问项目地址渗透agent
🔗 点击访问项目地址