Datasets:

darkknight25
/

Shellcode_Exploit_Dataset

---
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.
```

	---
	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.
	```