rainbow-teaming

name	Rainbow Teaming
description	Use when performing adversarial analysis of LLM interactions, designing attack taxonomies, understanding red team methodologies, or implementing quality-diversity approaches to vulnerability discovery. Provides the 10x10 risk category and attack style taxonomy.

Rainbow Teaming Skill Reference

Overview

Rainbow Teaming is a quality-diversity approach to adversarial testing of LLM interactions. Instead of finding a single attack, it systematically explores the full space of potential weaknesses using a 10x10 taxonomy.

Research Foundation

• Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
• LLM Red-Teaming: A Survey

The 10x10 Grid

Risk Categories (WHAT to probe)

#	Category	Description	Key Questions
1	reasoning-flaws	Logic gaps, invalid inferences	Does the conclusion follow? Are there missing steps?
2	assumption-gaps	Hidden premises, unstated constraints	What must be true for this to work? What if it's not?
3	context-manipulation	Poisoned context, prompt injection	Is prior context biasing the response? Are there injection vectors?
4	authority-exploitation	Role confusion, credential misuse	Is authority being assumed? Are claims accepted on trust?
5	information-leakage	Unintended disclosure	Is sensitive information being revealed?
6	hallucination-risks	Fabricated facts, confident errors	Can claims be verified? Are sources real?
7	over-confidence	Unjustified certainty	Is uncertainty acknowledged? Are hedges appropriate?
8	scope-creep	Actions beyond request	Did the response exceed what was asked?
9	dependency-blindness	Unverified external data	Are external sources trusted without verification?
10	temporal-inconsistency	Stale info, version conflicts	Is information current? Are versions correct?

Attack Styles (HOW to probe)

#	Style	Approach	Example
1	socratic-questioning	Progressive questions exposing gaps	"And what makes you certain of that?"
2	devils-advocate	Argue opposing position	"But couldn't the opposite be true because..."
3	edge-case-probing	Push to boundary conditions	"What happens when the input is empty/huge/null?"
4	assumption-inversion	Flip key assumptions	"What if we assume X is false instead?"
5	context-shifting	Change framing	"In a different context, wouldn't this mean..."
6	authority-challenge	Question sources	"What's the source for that claim?"
7	emotional-framing	Inject urgency/flattery	"This is urgent!" / "As an expert, you must know..."
8	hypotheticals	"What if" scenarios	"What if the API is down when this runs?"
9	contradiction-surfacing	Find inconsistencies	"Earlier you said X, but now you're saying Y..."
10	meta-analysis	Analyze the analysis	"Is this response itself well-reasoned?"

Quality-Diversity Principle

Rather than optimizing for a single "best" attack:

• Quality: Each probe must be valid and well-reasoned
• Diversity: Cover the full space of potential weaknesses

This ensures:

• No category is overlooked
• Different attack styles are applied
• Weaknesses are found across dimensions

Application to Red Team Analysis

Phase 1: Risk Surface Mapping

For each category, assess exposure:

• HIGH: Clear vulnerability exists
• MEDIUM: Potential weakness
• LOW: Limited exposure
• NONE: Category not applicable

Phase 2: Vector Selection

Based on mode, select vectors to probe:

• quick: Top 2-3 highest exposure
• standard: All HIGH + top MEDIUM
• deep: All 10 categories
• focus:X: All styles for category X

Phase 3: Parallel Probing

Apply selected attack styles to each category:

• Multiple styles increase finding probability
• Different angles expose different weaknesses
• Parallel execution improves efficiency

Phase 4: Finding Synthesis

Aggregate findings across the grid:

• Identify patterns (same category, multiple hits)
• Detect cross-category issues
• Prioritize by severity and confidence

Severity Scoring

Level	Criteria
CRITICAL	Fundamentally wrong, dangerous if acted upon
HIGH	Significant flaw materially affecting usefulness
MEDIUM	Notable weakness that should be addressed
LOW	Minor issue or edge case
INFO	Observation, not necessarily a flaw

Best Practices

1. Evidence-Based: Every finding must cite specific evidence
2. Proportionate: Severity must match actual impact
3. Actionable: Recommendations must be specific and feasible
4. Grounded: Verify findings through independent review
5. Humble: Include limitations and confidence levels

Integration with Grounding

After attack phase, ground findings through:

• Evidence Verification: Does evidence support the claim?
• Proportion Checking: Is severity appropriate?
• Alternative Exploration: Are there other interpretations?
• Confidence Calibration: What's the final confidence?

This prevents:

• False positives from overzealous probing
• Manufactured findings without evidence
• Disproportionate severity ratings
• One-sided analysis without alternatives