Update README.md

README.md

@@ -1,3 +1,140 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+tags:
+- redteam
+- expolit
+- cybersecurity
+pretty_name: sunny thakur
+size_categories:
+- n<1K
+---
+
+# Shellcode Exploit Dataset for Red Team GPT Training
+# Dataset Overview
+The 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.
+
+It is structured in JSON format for compatibility with ML pipelines and red team training frameworks.
+
+# Key Features
+```sql
+Total Entries: 180 unique exploits, split into three JSON files .
+Timeframe: Historical (2021–2024) and recent (2025) exploits.
+Vulnerability Types:
+Buffer Overflow 
+Format String 
+Use-After-Free 
+Remote Code Execution 
+Privilege Escalation 
+Race Condition 
+Integer Overflow 
+```
+
+Platforms:
+```sql
+Linux 
+Windows 
+macOS 
+IoT 
+Android 
+
+
+Architectures:
+x86 
+x64 
+ARM 
+MIPS 
+
+
+Payload Goals:
+Remote Code Execution 
+Reverse Shell 
+Privilege Escalation 
+Data Exfiltration 
+Persistence 
+```
+
+Sources:
+```
+Exploit-DB 
+GitHub 
+CTF Challenges 
+CVE Databases 
+```
+
+# Data Format: JSON, with fields for exploit_id, cve, vulnerability_type, platform, architecture, payload_goal, cvss_score, shellcode, description, source, and date_added.
+
+# Dataset Structure
+The dataset is split into three JSON files, each containing unique entries:
+
+```java
+JSON Schema
+{
+  "exploit_id": "string", // Unique identifier (e.g., EDB-48789, CTF-2025-ABC)
+  "cve": "string", // CVE identifier or "N/A" for CTF exploits
+  "vulnerability_type": "string", // e.g., Buffer Overflow, Remote Code Execution
+  "platform": "string", // e.g., Linux, Windows, IoT
+  "architecture": "string", // e.g., x86, x64, ARM, MIPS
+  "payload_goal": "string", // e.g., Reverse Shell, Data Exfiltration
+  "cvss_score": float, // CVSS score (6.5–9.8)
+  "shellcode": "string", // Hex-encoded shellcode
+  "description": "string", // Brief exploit description
+  "source": "string", // Source URL or CTF identifier
+  "date_added": "string" // Date in YYYY-MM-DD format
+}
+```
+# Usage
+This dataset is intended for:
+```sql
+Machine Learning: Training red team GPT models for exploit generation, vulnerability analysis, or shellcode development.
+Penetration Testing Research: Analyzing exploit patterns across platforms and architectures.
+Educational Purposes: Studying historical and recent vulnerabilities in controlled environments.
+```
+Example Usage
+```python
+import json
+
+# Load dataset
+with open("shellcode expolit_dataset_n.json", "r") as f:
+    data = json.load(f)
+
+# Filter exploits by vulnerability type
+buffer_overflows = [entry for entry in data if entry["vulnerability_type"] == "Buffer Overflow"]
+
+# Print shellcode for Linux x64 exploits
+for entry in buffer_overflows:
+    if entry["platform"] == "Linux" and entry["architecture"] == "x64":
+        print(f"Exploit ID: {entry['exploit_id']}, Shellcode: {entry['shellcode']}")
+```
+
+# Ethical Considerations
+```
+Responsible Use: This dataset is provided for research and educational purposes only. Unauthorized use of exploits against systems without explicit permission is illegal and unethical.
+Controlled Environments: Test exploits in isolated, sandboxed environments (e.g., QEMU, virtual machines) to avoid unintended harm.
+Attribution: All exploits are sourced from public repositories (Exploit-DB, GitHub) or CTF challenges. Respect the original authors' work and licenses.
+```
+# Data Collection
+```
+
+Sources: Exploits were collected from Exploit-DB, GitHub repositories, CTF challenges, and CVE databases, ensuring diversity and relevance.
+Automation: A Python-based scraper (stored internally) was used to gather and validate exploits, with testing conducted in a QEMU sandbox.
+Validation: Shellcode was verified for functionality and uniqueness, with polymorphic variations included to enhance evasion training.
+```
+# Limitations
+```
+No Mitigation Details: The dataset focuses on exploits and does not include mitigation strategies.
+Projected 2025 Exploits: Some entries for 2025 are speculative, based on trends in vulnerability types and platforms.
+Sandbox Testing Required: Shellcode should be tested in controlled environments to ensure compatibility and safety.
+```
+# License
+This dataset is released under the MIT License. Users must comply with ethical guidelines and applicable laws when using the dataset.
+# Contact
+For questions, contributions, or additional datasets, please open an issue on this Hugging Face repository or contact the maintainers.
+
+# Acknowledgments
+```sql
+Exploit-DB: For providing a rich source of verified exploits.
+GitHub Community: For open-source exploit contributions.
+CTF Organizers: For challenging and innovative exploit scenarios.
+```