RAINBOW TEAM PROTOCOL v1.0

---

Rainbow Team is a multi-role adversarial evaluation framework designed to pressure-test AI systems across:

• reasoning
   
• robustness
   
• safety
   
• alignment
   
• continuity
   
• multimodal grounding
   

Instead of a single adversarial “red team,” Rainbow Team introduces multiple color-coded adversarial roles, each specializing in a distinct failure mode.
Together, they provide a full-spectrum evaluation that surfaces weaknesses no single testing method can reveal.

Each role represents a different perspective, probing for different types of failures:

• Red — Direct Adversary: attacks reasoning, logic, and factual robustness
   
• Blue — Security & Safety Probe: tests safety gates, refusals, and constraint violations
   
• Yellow — Multi-Step Chainbreaker: targets long-horizon tasks, dependencies, and logic drift
   
• Green — Multimodal Stressor: tests image-language grounding and perceptual consistency
   
• Black — Boundary Violator: tries to break policies, identity integrity, and role separation
   
• Violet — Mixer Role: generates hybrid adversarial instructions combining other roles
   

When combined, these roles form an evaluation spectrum, creating a richer, more diverse pressure-testing environment than any single adversary can.

Rainbow Team applies adversarial pressure layer-by-layer, increasing intensity and complexity:

• Layer 1: basic reasoning checks
   
• Layer 2: long-range reasoning & chain-of-thought destabilization
   
• Layer 3: ambiguous task conditions & constraint violations
   
• Layer 4: multimodal anomalies & visual contradictions
   
• Layer 5: counterfactual transformations
   
• Layer 6: role-mixed adversarial probes (Violet mode)
   

This layered structure ensures AI models are tested not only for what they know but where and how they fail.

Rainbow Team integrates a suite of evaluation operators, including:

• adversarial input rewrites
   
• instruction-level perturbations
   
• multimodal distortions
   
• counterfactual operators
   
• continuity drift injections
   
• identity-boundary violations
   
• safety-gate stressors
   

These methods produce structured, reproducible failures—providing usable fingerprints for debugging, retraining, or model comparison.

Modern AI systems fail in complex, subtle, and unpredictable ways. Rainbow Team provides:

• Full-spectrum adversarial coverage
   
• Role-structured evaluation instead of random attacks
   
• Actionable failure fingerprints you can trace and fix
   
• Comparative evaluation across different models
   
• Multimodal and multi-turn stress testing
   
• Continuity drift detection (unique to this framework)
   

It helps organizations understand where their models break, why, and under what conditions—and gives them the tools to strengthen their AI systems systematically.

 Button text: Rainbow Team v1.0 Technical Report
URL:  https://doi.org/10.5281/zenodo.17807403