Exploit for Benchmarking-Agent-Architectures

## https://sploitus.com/exploit?id=1303B5DB-BF39-5F9B-939D-9E7813A30493
##  Benchmarking Agent Architectures for LLM-Based Exploit Generation
📌 Overview

Offensive security tasks such as exploit generation require deep technical reasoning, contextual understanding, and adaptive planning. With the rise of Large Language Models (LLMs), multiple agent architectures have emerged to automate and enhance these tasks.

This project benchmarks and compares different LLM-based agent architectures to determine their effectiveness across exploit generation scenarios.

🎯 Research Question

Which agent architecture (prompt-based, tool-augmented, or multi-agent) performs best across different exploit generation task types in terms of accuracy, efficiency, and robustness?

🧠 Architectures Evaluated
1. 🔹 Prompt-Based Systems
Single-shot and few-shot prompting
No external tools
Fast but limited reasoning depth
2. 🔧 Tool-Augmented Agents
Integrates external tools (e.g., vulnerability scanners, exploit databases)
Enhances retrieval and execution capabilities
More accurate but slightly slower
3. 🤖 Multi-Agent Systems
Multiple specialized agents:
Reconnaissance Agent
Planning Agent
Exploitation Agent
Collaborative problem solving
Best for complex tasks but computationally expensive
🎯 Objectives
✅ Implement multiple LLM-based agent architectures
✅ Evaluate performance across exploit generation tasks
✅ Compare reasoning, retrieval, and planning capabilities
✅ Provide guidelines for architecture selection
🏗️ Project Structure
├── agents/
│   ├── base_agent.py
│   ├── prompt_agent.py
│   ├── tool_agent.py
│   ├── multi_agent/
│   │   ├── recon_agent.py
│   │   ├── planner_agent.py
│   │   ├── executor_agent.py
│
├── tasks/
│   ├── cve_tasks.json
│   ├── reasoning_tasks.json
│   ├── retrieval_tasks.json
│
├── evaluation/
│   ├── metrics.py
│   ├── benchmark.py
│
├── utils/
│   ├── logger.py
│   ├── helpers.py
│
├── main.py
├── requirements.txt
└── README.md
⚙️ Installation
git clone https://github.com/your-username/llm-agent-benchmark.git
cd llm-agent-benchmark

pip install -r requirements.txt
▶️ Usage

Run benchmarking:

python main.py --architecture prompt
python main.py --architecture tool
python main.py --architecture multi

Run all architectures:

python main.py --all
📊 Evaluation Metrics

The architectures are evaluated using:

Accuracy → Correct exploit generation
Efficiency → Time and token usage
Robustness → Stability across diverse tasks
Reasoning Depth → Multi-step logical correctness
Tool Utilization → Effective use of external resources
🧪 Task Categories
🔍 Retrieval Tasks (e.g., CVE lookup, exploit database search)
🧠 Reasoning Tasks (e.g., vulnerability analysis)
🗺️ Planning Tasks (multi-step exploit workflows)
📈 Expected Insights
Prompt-based systems perform well for simple tasks
Tool-augmented agents improve retrieval-heavy tasks
Multi-agent systems excel in complex reasoning and planning
🛡️ Ethical Considerations

This project is strictly for educational and research purposes in cybersecurity.

⚠️ Do NOT use this system for unauthorized exploitation or illegal activities.

🔮 Future Work
Integration with real-time vulnerability feeds (CVE/NVD)
Reinforcement learning-based agent optimization
Automated red-teaming simulations
Benchmark dataset expansion
🤝 Contributing

Contributions are welcome!

fork → clone → create branch → commit → push → pull request