darkknight25/Shellcode_Exploit_Dataset

1---2license: mit3language:4- en5tags:6- redteam7- expolit8- cybersecurity9pretty_name: sunny thakur10size_categories:11- n<1K12---13 14# Shellcode Exploit Dataset for Red Team GPT Training15# Dataset Overview16The Shellcode Exploit Dataset is a comprehensive collection of 700 unique shellcode exploits, spanning 2021–2025, designed for training machine learning models, particularly for red team and cybersecurity research. The dataset includes a diverse set of vulnerabilities, platforms, architectures, and payload goals, sourced from Exploit-DB, GitHub, CTF challenges, and CVE databases.17 18It is structured in JSON format for compatibility with ML pipelines and red team training frameworks.19 20# Key Features21```sql22Total Entries: 180 unique exploits, split into three JSON files .23Timeframe: Historical (2021–2024) and recent (2025) exploits.24Vulnerability Types:25Buffer Overflow 26Format String 27Use-After-Free 28Remote Code Execution 29Privilege Escalation 30Race Condition 31Integer Overflow 32```33 34Platforms:35```sql36Linux 37Windows 38macOS 39IoT 40Android 41 42 43Architectures:44x86 45x64 46ARM 47MIPS 48 49 50Payload Goals:51Remote Code Execution 52Reverse Shell 53Privilege Escalation 54Data Exfiltration 55Persistence 56```57 58Sources:59```60Exploit-DB 61GitHub 62CTF Challenges 63CVE Databases 64```65 66# Data Format: JSON, with fields for exploit_id, cve, vulnerability_type, platform, architecture, payload_goal, cvss_score, shellcode, description, source, and date_added.67 68# Dataset Structure69The dataset is split into three JSON files, each containing unique entries:70 71```java72JSON Schema73{74  "exploit_id": "string", // Unique identifier (e.g., EDB-48789, CTF-2025-ABC)75  "cve": "string", // CVE identifier or "N/A" for CTF exploits76  "vulnerability_type": "string", // e.g., Buffer Overflow, Remote Code Execution77  "platform": "string", // e.g., Linux, Windows, IoT78  "architecture": "string", // e.g., x86, x64, ARM, MIPS79  "payload_goal": "string", // e.g., Reverse Shell, Data Exfiltration80  "cvss_score": float, // CVSS score (6.5–9.8)81  "shellcode": "string", // Hex-encoded shellcode82  "description": "string", // Brief exploit description83  "source": "string", // Source URL or CTF identifier84  "date_added": "string" // Date in YYYY-MM-DD format85}86```87# Usage88This dataset is intended for:89```sql90Machine Learning: Training red team GPT models for exploit generation, vulnerability analysis, or shellcode development.91Penetration Testing Research: Analyzing exploit patterns across platforms and architectures.92Educational Purposes: Studying historical and recent vulnerabilities in controlled environments.93```94Example Usage95```python96import json97 98# Load dataset99with open("shellcode expolit_dataset_n.json", "r") as f:100    data = json.load(f)101 102# Filter exploits by vulnerability type103buffer_overflows = [entry for entry in data if entry["vulnerability_type"] == "Buffer Overflow"]104 105# Print shellcode for Linux x64 exploits106for entry in buffer_overflows:107    if entry["platform"] == "Linux" and entry["architecture"] == "x64":108        print(f"Exploit ID: {entry['exploit_id']}, Shellcode: {entry['shellcode']}")109```110 111# Ethical Considerations112```113Responsible Use: This dataset is provided for research and educational purposes only. Unauthorized use of exploits against systems without explicit permission is illegal and unethical.114Controlled Environments: Test exploits in isolated, sandboxed environments (e.g., QEMU, virtual machines) to avoid unintended harm.115Attribution: All exploits are sourced from public repositories (Exploit-DB, GitHub) or CTF challenges. Respect the original authors' work and licenses.116```117# Data Collection118```119 120Sources: Exploits were collected from Exploit-DB, GitHub repositories, CTF challenges, and CVE databases, ensuring diversity and relevance.121Automation: A Python-based scraper (stored internally) was used to gather and validate exploits, with testing conducted in a QEMU sandbox.122Validation: Shellcode was verified for functionality and uniqueness, with polymorphic variations included to enhance evasion training.123```124# Limitations125```126No Mitigation Details: The dataset focuses on exploits and does not include mitigation strategies.127Projected 2025 Exploits: Some entries for 2025 are speculative, based on trends in vulnerability types and platforms.128Sandbox Testing Required: Shellcode should be tested in controlled environments to ensure compatibility and safety.129```130# License131This dataset is released under the MIT License. Users must comply with ethical guidelines and applicable laws when using the dataset.132# Contact133For questions, contributions, or additional datasets, please open an issue on this Hugging Face repository or contact the maintainers.134 135# Acknowledgments136```sql137Exploit-DB: For providing a rich source of verified exploits.138GitHub Community: For open-source exploit contributions.139CTF Organizers: For challenging and innovative exploit scenarios.140```141