README.md

metadata

title: SentinelOps Arena
emoji: 🛡️
colorFrom: green
colorTo: red
sdk: gradio
sdk_version: 6.9.0
app_file: app.py
pinned: false

SentinelOps Arena

Multi-agent self-play RL environment for enterprise security training, built on OpenEnv for the OpenEnv Hackathon SF (March 7-8, 2026).

Three AI agents compete in a simulated enterprise environment:

• RED TEAM (Attacker) — Launches schema drift, policy drift, social engineering, and rate limiting attacks
• BLUE TEAM (Worker) — Handles customer requests across CRM, Billing, and Ticketing systems
• AUDITOR (Oversight) — Monitors worker actions and flags policy violations

Through adversarial self-play with GRPO training, all three agents improve simultaneously.

Quick Start

# Setup
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# Run Gradio demo
python app.py

# Run HTTP server
python -m sentinelops_arena.server --port 8000

# Run demo script
python -m sentinelops_arena.demo

Project Structure

NexusEnv/
├── sentinelops_arena/
│   ├── models.py              # Action, Observation, State, data models
│   ├── environment.py         # SentinelOpsArena (MCPEnvironment) — core env
│   ├── systems/
│   │   ├── crm.py             # CRM simulator
│   │   ├── billing.py         # Billing simulator
│   │   └── ticketing.py       # Ticketing simulator
│   ├── attacks.py             # 4 attack types (schema/policy drift, social eng, rate limit)
│   ├── rewards.py             # Reward functions for all 3 agents
│   ├── task_generator.py      # Customer task generation
│   ├── demo.py                # Heuristic agents + episode runner
│   ├── server.py              # HTTP/WebSocket server
│   ├── test_phase1.py         # Unit tests
│   └── test_environment.py    # Integration tests
├── app.py                     # Gradio UI (HuggingFace Spaces)
├── train.py                   # GRPO training script (Unsloth + TRL)
├── requirements.txt
├── pyproject.toml
└── README.md

Architecture

3 Agents, 3 Systems, 30 Ticks per Episode

Each tick: Attacker acts → Worker acts → Oversight acts

Attack Types

1. Schema Drift — Renames fields across all records. Worker must detect KeyError, call get_schema(), and adapt.
2. Policy Drift — Changes business rules (refund windows, approval requirements). Worker must call get_current_policy().
3. Social Engineering — Injects fake authority messages. Worker must resist manipulation.
4. Rate Limiting — Throttles API calls. Worker must handle gracefully.

MCP Tools

19 tools exposed via FastMCP, organized by agent role:

• Worker: lookup_customer, check_balance, issue_refund, create_ticket, get_schema, get_current_policy, etc.
• Attacker: launch_attack, get_attack_budget
• Oversight: flag_action, get_trajectory

Training

Uses GRPO (Group Relative Policy Optimization) with Unsloth + TRL:

# Train with Unsloth (recommended, 2x faster)
python train.py --use_unsloth --model_name unsloth/Qwen2.5-0.5B-Instruct

# Train without Unsloth
python train.py --model_name Qwen/Qwen2.5-0.5B-Instruct

See train.py for the full training pipeline.

Partner Tracks

• Fleet AI — Scalable Oversight: the Oversight agent monitors and explains Worker behavior
• Patronus AI — Schema Drift: schema and policy drift are core attack types

Tech Stack

• OpenEnv 0.2.x — Environment framework
• FastMCP — MCP tool server
• Gradio — Demo UI
• HuggingFace TRL — GRPO training
• Unsloth — Fast fine-tuning (2x speed, 70% less VRAM)
• Pydantic — Data validation

Tests

python sentinelops_arena/test_phase1.py
python sentinelops_arena/test_environment.py