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| title: AI Deception OpenEnv | |
| emoji: π‘οΈ | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: docker | |
| app_port: 7860 | |
| pinned: false | |
| # π‘οΈ 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()` | |
| --- | |
| ## π― 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 | |
| --- | |
| ## βοΈ Action Space | |
| The AI agent can perform the following actions: | |
| - `detect_attack` | |
| - `deploy_honeypot` | |
| - `fake_database` | |
| - `block_ip` | |
| --- | |
| ## ποΈ Observation Space | |
| Environment returns structured observation: | |
| - `failed_logins` | |
| - `port_scans` | |
| - `suspicious_ips` | |
| - `total_requests` | |
| - `attack_types` | |
| --- | |
| ## π§ 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 | |
| --- | |
| ## π Reward Function | |
| | Action | Reward | | |
| |--------|--------| | |
| | detect_attack | 0.15β0.45 | | |
| | deploy_honeypot | 0.30 | | |
| | fake_database | 0.20 | | |
| | block_ip (correct) | 0.70 | | |
| | early block | 0.05 | | |
| Reward range normalized between **0.0 β 1.0** | |
| --- | |
| ## π API Endpoints | |
| Available endpoints: | |
| - `/reset` | |
| - `/step` | |
| - `/state` | |
| - `/logs` | |
| - `/status` | |
| Example: | |
| POST /reset | |
| POST /step | |
| GET /state | |
| --- | |
| ## π Run Locally | |
| Install dependencies: | |
| ```bash | |
| pip install -r requirements.txt | |
| Run inference: | |
| python inference.py | |
| π³ Docker | |
| Build: | |
| docker build -t ai-deception . | |
| Run: | |
| docker run -p 7860:7860 ai-deception | |
| π€ 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 | |
| π 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 | |
| ποΈ Architecture | |
| Attacker | |
| β | |
| Fake Server | |
| β | |
| AI Agent (Inference) | |
| β | |
| Defense Actions | |
| β | |
| Reward | |
| π¦ Project Structure | |
| ai-deception-openenv/ | |
| β | |
| βββ env/ | |
| β βββ __init__.py | |
| β βββ attacker.py | |
| β βββ deception.py | |
| β βββ env.py | |
| β βββ fake_server.py | |
| β βββ test_env.py | |
| β βββ test_server.py | |
| β | |
| βββ tasks/ | |
| β βββ __init__.py | |
| β β | |
| β βββ easy/ | |
| β β βββ __init__.py | |
| β β βββ task.py | |
| β β βββ grader.py | |
| β β | |
| β βββ medium/ | |
| β β βββ __init__.py | |
| β β βββ task.py | |
| β β βββ grader.py | |
| β β | |
| β βββ hard/ | |
| β β βββ __init__.py | |
| β β βββ task.py | |
| β β βββ grader.py | |
| β β | |
| β βββ test_tasks.py | |
| β | |
| βββ server/ | |
| β βββ app.py | |
| β | |
| βββ inference.py | |
| βββ app.py | |
| βββ models.py | |
| βββ openenv.yaml | |
| βββ Dockerfile | |
| βββ requirements.txt | |
| βββ pyproject.toml | |
| βββ uv.lock | |
| βββ README.md | |
| βββ LICENSE | |
| βββ .gitignore | |
| βββ .gitattributes | |
| β OpenEnv Compliance | |
| reset() implemented | |
| step() implemented | |
| state() implemented | |
| Docker support | |
| Structured logs | |
| Multiple tasks | |
| Reward normalization | |
| π¨βπ» Use Case | |
| This environment can be used for: | |
| Cybersecurity research | |
| Reinforcement learning | |
| AI defense strategy training | |
| Red team vs blue team simulations | |
| π‘οΈ AI Cyber Deception | |
| This project demonstrates how AI can: | |
| Detect attackers | |
| Deploy deception | |
| Block malicious actors | |
| Learn defensive strategies | |
| License | |
| MIT License | |