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 14 083 subscribers, ranking 8 798 in the Technologies & Applications category and 14 979 in the China region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 14 083 subscribers.
According to the latest data from 16 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 352 over the last 30 days and by 7 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 0.38%. Within the first 24 hours after publication, content typically collects 0.48% reactions from the total number of subscribers.
- Post reach: On average, each post receives 53 views. Within the first day, a publication typically gains 68 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 fork, github, powered, 六合彩, cve-2026.
📝 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 17 September, 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.
A small Python toolkit that turns malware triage notes into clean reports, a browsable IOC dashboard, and YARA rules, with write-ups of some Windows lab samples
🔗 点击访问项目地址无描述
🔗 点击访问项目地址Curated collection of strict, zero-false-positive Nuclei v3 templates for bug bounty automation, infrastructure reconnaissance, and exposure hunting.
🔗 点击访问项目地址Documentation on my experience writing my first working position independent shellcode for windows from scratch using x64 assembly
🔗 点击访问项目地址The Smart AI Pentesting Agent is an automated vulnerability scanner that combines traditional payload-based testing with intelligent detection mechanisms. It can identify: SQL Injection (Error-based and Time-based Blind) Cross-Site Scripting (XSS) (Reflected)WAF Detection and adaptive response
🔗 点击访问项目地址Autonomous agent that mines Java deserialization gadget chains from any JAR directory. Static bytecode analysis + dynamic JVM probes + dual-model adversarial auditing → weaponized PoC. Solving the last mile.
🔗 点击访问项目地址Burp Suite extension that automates SQL injection detection (error/boolean/time-based, confidence-scored), WSTG-INPVAL-05. Detection only; exploitation stays manual.
🔗 点击访问项目地址A C2 Framework (Command & Control) tool developed for red team operations and educational research. It uses Telegram as its C2 channel and encrypts data with a Base64 + XOR combination.
🔗 点击访问项目地址Burp Suite extension that turns a request or response into a clean, report-ready PoC screenshot: hides browser noise headers, blurs secrets, removes lines. Repeater, Logger and Proxy.
🔗 点击访问项目地址Web Vulnerability Scanner for SQL Injection and XSS Detection
🔗 点击访问项目地址CVE-2026-85706
🔗 点击访问项目地址A Python Flask Web Vulnerability Scanner prototype that scans websites for missing security headers and basic input vulnerabilities, displaying results via a simple web interface. Task 1 establishes the core scanning functionality for later enhancements.
🔗 点击访问项目地址Automated web vulnerability scanner for authorized security testing. Zero dependencies, stdlib-only Python: scope-gated SQLi/XSS/redirect/header/path checks, boolean-blind extraction, offline lab, 17 tests.
🔗 点击访问项目地址