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AI Red vs Blue Arena

Introduction

AI Red vs Blue Arena

A self-contained adversarial simulation platform where AI agents attack and defend each other to test robustness, security, alignment, and reasoning integrity.

What is the AI Arena?

The AI Red vs Blue Arena creates a continuous loop of:

  1. Attack Generation (Red) - AI agents attempt to exploit vulnerabilities
  2. Defense + Hardening (Blue) - AI agents defend and patch vulnerabilities
  3. Evaluation & Scoring - Automated scoring and analysis
  4. Retraining & Improvement - Integration with Oumi for model fine-tuning
  5. Continuous Simulation + Monitoring - Real-time dashboards and alerts

Core Concept

Red Teaming (Attacker LLM)

Red Agents attempt to break the target AI via:

  • Prompt injection & jailbreaks
  • Tool execution manipulation
  • Context poisoning
  • Chain-of-thought leakage extraction
  • Reasoning corruption
  • Goal drift and long-game manipulation
  • Adversarial content crafting
  • Safety boundary probing

Blue Teaming (Defender LLM)

Blue Agents defend by:

  • Sanitizing incoming prompts
  • Enforcing guardrails & permissions
  • Validating tool calls
  • Intercepting policy violations
  • Auto-rewriting system prompts
  • Creating patch suggestions
  • Identifying harmful inputs
  • Suppressing hallucinations

The Arena Engine

The Arena runs Red vs Blue simulations on user-provided agents. Each simulation consists of:

  • Attack rounds
  • Defense rounds
  • Scoring
  • Logging
  • Post-analysis
  • Recommendations

Architecture Overview

  • Frontend: Next.js dashboard with real-time visualization
  • Backend: Express.js API server with WebSocket support
  • Database: lowdb (JSON-based) for persistence
  • LLM Integration: Groq (recommended), OpenAI, and Anthropic (with mock fallback)
  • Tool Execution: Cline for agent tool execution and sandboxing
  • Orchestration: Kestra workflows + custom match runner
  • Training: Oumi integration stubs for fine-tuning

Key Features

  • ✅ Real tool execution via Cline (not just simulation)
  • ✅ Sandboxed environment with permission gates
  • ✅ Multiple LLM providers with automatic fallback
  • ✅ 13+ Groq models available with automatic rotation
  • ✅ Real-time WebSocket updates
  • ✅ Kestra orchestration for automated workflows
  • ✅ Oumi integration for fine-tuning
  • ✅ Comprehensive scoring and evaluation system

Use Cases

For Developers

  • Validate agents before shipping
  • Test specific vulnerabilities
  • Auto-generate secure system prompts
  • Ensure tools cannot be exploited

For Enterprises

  • Continuous evaluation of internal AI systems
  • Centralized security metrics
  • Compliance reporting
  • Early detection of risky behaviors

For AI Safety Research

  • Observe model drift
  • Study multi-step manipulation
  • Evaluate long-horizon deception
  • Compare frontier models

For Security Teams

  • Automated LLM penetration testing
  • Attack simulation libraries
  • Replay and audit logs

Next Steps