solo-red-team - Ethical offensive security lab: sanitized red-team

项目说明

!Python

⚠️ Educational Use Only: All examples are fictional and for learning purposes. Do not use against systems without explicit written authorization.

Recruiter note: This repository demonstrates offensi methodology in controlled, permissioned lab en for defensi, and training purposes. Full artifacts are retained pri and may be shared under NDA for interview or research review.

Cybersecurity Portfolio Project: Web Application & Authentication Security Analysis

> Recruiter note: Demonstrates red‑team simulation in a controlled lab; no li.

Project O

This repository provides an educational simulation of red-team techniques intended for use only in isolated lab en. All examples are executed against intentionally , containerized targets and are designed to help defenders understand attack patterns and detection engineering.

How to run safely: Use docker-compose -f lab/docker-compose.yml up to start an isolated lab. Do not run any tools on production or third-party networks.

Key Acti & Learning Goals

• Reconnaissance methodology for identifying common web-authentication misconfigurations (lab-only).
• Demonstration of password-testing strategies against synthetic lab accounts to e detection efficacy.
• Exploration of parameter-handling and timing-analysis concepts in controlled scenarios.
• De of detection-rule sketches and reporting templates for defensi.

E & Reporting (sanitized)

Raw logs, binaries, and any potentially sensiti have been removed from this public repository for legal and safety reasons. This repo contains sanitized examples and synthetic outputs that illustrate methodology without distributing dangerous artifacts. Full artifacts are preser in a private archive and can be made a under NDA for legitimate research or hiring review.

Module summary

| Module | Purpose | Key scripts | Outputs |
|---|---|---|---|
| Recon | Fingerprint services and auth schemes | response_fingerprint.py | service map, headers |
| Exploitation | Online/offline auth testing | credential_reuse.py, digest_nonce_reuse.py, timing_attack.py | attempt logs, findings |
| Reporting | Aggregate and explain findings | report/ LaTeX and PNGs | final report, |

Detection Engineering Insight

Failed‑auth telemetry, nonce issuance cadence, and response size can signal brute‑force and digest replay. Rate‑limit challenges, bind nonce to client tuple, and randomize static response sizes.

Password reuse signals across services inform hardening and user training; enforce unique credentials and breached‑password checks.

See Also

See also: federated-ids — pri distributed detection system complementary to this red-team lab.

Methodology

This project followed a structured, fi ethical hacking methodology:

1. Reconnaissance: Fingerprinted target systems and applications to map the attack surface and identify authentication mechanisms.
2. Initial Access: Gained an initial foothold by exploiting weak passwords and common in a controlled lab setting.
3. Pri Escalation & Lateral Mo: Escalated pri by le credential reuse and other .
4. Ad Exploitation: Deployed sophisticated techniques, including nonce reuse and timing attacks, against hardened targets.
5. Reporting: Meticulously documented all findings, attack paths, and recommendations in a professional security assessment report.

Tools Used

This project le a combination of standard cybersecurity tools and custom scripts to simulate real-world attack scenarios. The primary tools include:

* cURL: A command-line tool for making HTTP requests, essential for initial service fingerprinting and testing web endpoints.
* Hydra: A popular online password-cracking tool used to perform brute-force and dictionary attacks against authentication services.
* John the Ripper: A powerful offline password-cracking tool used to crack password hashes obtained from target systems.
* Ophcrack: A specialized tool for cracking Windows passwords using rainbow tables, demonstrated in the project's analysis of NTLM hashes.
* Wordlists: Standard password lists, such as RockYou, were used to simulate credential reuse and dictionary attacks.

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Ethical statement: The author supports responsible disclosure and safe training. All examples are fictional and for learning purposes only.

项目信息

开发语言：TeX

Star 数：1

技术要点

该项目属于防护思路方向的开源资源，代码结构清晰，适合安全研究人员学习参考。项目围绕Ethical offensive security lab: sanitized red-team simulation, Docker sandbox, defensive insight展开，提供了可运行的实现，对理解相关安全机制、工具原理与攻防技术具有较高的研究价值。

从技术实现角度看，此类项目的核心在于对底层原理的深入理解与工程化落地。开发者在研究过程中可以结合官方文档与社区讨论，梳理项目的初始化流程、核心模块职责与数据流转，逐步掌握其中的技术细节与设计思路。

使用与学习建议

1. 下载源码压缩包，解压到本地环境。

2. 根据项目类型准备对应的运行环境与依赖。

3. 按照 README 中的步骤完成配置与部署。

4. 在隔离的测试环境中运行验证，避免影响生产系统。

免责声明

本资源来源于开源社区，仅供安全研究、技术学习与合法授权范围内的攻防演练使用，请勿用于任何非法或侵害他人权益的用途。使用本项目产生的一切后果由使用者自行承担。