Apppublic

Bytecore1/ai-deception-openenv

sourceHugging Faceupdated 6mo agoView on Hugging Face

0likes

App README

🛡️ AI Cyber Deception OpenEnv

Overview

AI Cyber Deception OpenEnv is a real-world cybersecurity simulation environment where an AI agent learns to detect, deceive, and mitigate cyber attacks.

This environment simulates production-like cybersecurity defense scenarios including brute force attacks, port scanning, SQL injection, directory traversal, and credential stuffing.

The environment follows the OpenEnv specification and supports:

—reset()
—step()
—state()

🎯 Real-World Task

Simulate cybersecurity defense in a production-like environment:

—Detect brute force attacks
—Detect port scanning
—Detect SQL injection
—Detect directory traversal
—Deploy deception mechanisms
—Block malicious attackers

⚙️ Action Space

The AI agent can perform the following actions:

—detect_attack
—deploy_honeypot
—fake_database
—block_ip

👁️ Observation Space

Environment returns structured observation:

—failed_logins
—port_scans
—suspicious_ips
—total_requests
—attack_types

🧠 Tasks

Easy Task

Detect cyber attack

Goal:

—Detect suspicious activity
—Identify attack patterns

Medium Task

Detect attack and deploy deception

Goal:

—Detect cyber attack
—Deploy honeypot or fake database

Hard Task

Full cyber defense workflow

Goal:

—Detect attack
—Deploy deception
—Block attacker

🏆 Reward Function

Action	Reward
detect_attack	0.15–0.45
deploy_honeypot	0.30
fake_database	0.20
block_ip (correct)	0.70
early block	0.05

Reward range normalized between 0.0 – 1.0

🌐 API Endpoints

Available endpoints:

—/reset
—/step
—/state
—/logs
—/status

Example:

POST /reset POST /step GET /state

🚀 Run Locally

Install dependencies:

bash

pip install -r requirements.txt

Run inference:

python inference.py
🐳 Docker

Build:

docker build -t ai-deception .

Run:

docker run -p 7860:7860 ai-deception
🤗 Hugging Face Deployment

Live Space:

https://bytecore1-ai-deception-openenv.hf.space/

Endpoints:

https://bytecore1-ai-deception-openenv.hf.space/reset

https://bytecore1-ai-deception-openenv.hf.space/state

https://bytecore1-ai-deception-openenv.hf.space/status

https://bytecore1-ai-deception-openenv.hf.space/logs

📊 Baseline Results

Example run:

[START] task=easy env=ai-deception-openenv model=Qwen
[STEP] step=1 action=detect_attack reward=0.45 done=false error=null
[STEP] step=2 action=deploy_honeypot reward=0.30 done=false error=null
[STEP] step=3 action=block_ip reward=0.70 done=true error=null
[END] success=true steps=3 score=0.48 rewards=0.45,0.30,0.70
🏗️ Architecture
Attacker
   ↓
Fake Server
   ↓
AI Agent (Inference)
   ↓
Defense Actions
   ↓
Reward
📦 Project Structure

ai-deception-openenv/
│
├── env/
│   ├── __init__.py
│   ├── attacker.py
│   ├── deception.py
│   ├── env.py
│   ├── fake_server.py
│   ├── test_env.py
│   └── test_server.py
│
├── tasks/
│   ├── __init__.py
│   │
│   ├── easy/
│   │   ├── __init__.py
│   │   ├── task.py
│   │   └── grader.py
│   │
│   ├── medium/
│   │   ├── __init__.py
│   │   ├── task.py
│   │   └── grader.py
│   │
│   ├── hard/
│   │   ├── __init__.py
│   │   ├── task.py
│   │   └── grader.py
│   │
│   └── test_tasks.py
│
├── server/
│   └── app.py
│
├── inference.py
├── app.py
├── models.py
├── openenv.yaml
├── Dockerfile
├── requirements.txt
├── pyproject.toml
├── uv.lock
├── README.md
├── LICENSE
├── .gitignore
└── .gitattributes

✅ OpenEnv Compliance
reset() implemented
step() implemented
state() implemented
Docker support
Structured logs
Multiple tasks
Reward normalization
👨‍💻 Use Case

This environment can be used for:

Cybersecurity research
Reinforcement learning
AI defense strategy training
Red team vs blue team simulations
🛡️ AI Cyber Deception

This project demonstrates how AI can:

Detect attackers
Deploy deception
Block malicious actors
Learn defensive strategies
License

MIT License