> ## Documentation Index
> Fetch the complete documentation index at: https://hydroxai.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Red Teaming & Attacks

> Why AI red teaming matters and an overview of attack categories for evaluating AI system safety.

## Why Red Teaming Matters

AI systems are increasingly deployed in high-stakes environments — from healthcare and finance to autonomous agents and customer-facing chatbots. As these systems grow more capable, so do the risks they introduce. **Red teaming** is the practice of systematically probing AI systems to discover vulnerabilities, biases, and failure modes *before* they cause real-world harm.

Unlike traditional software security testing, AI red teaming must cover a uniquely broad attack surface:

* **Non-deterministic outputs** — The same input can produce different responses, making vulnerabilities harder to detect and reproduce.
* **Natural language attack vectors** — Attackers don't need code exploits; carefully crafted prompts can bypass safety guardrails.
* **Emergent behaviors** — Large language models exhibit capabilities and failure modes that weren't explicitly programmed.
* **Agentic autonomy** — AI agents that use tools, call APIs, and interact with other agents introduce entirely new classes of risk.

## The Cost of Not Testing

Organizations that skip rigorous red teaming risk:

| Risk                    | Impact                                                                   |
| ----------------------- | ------------------------------------------------------------------------ |
| **Data breaches**       | PII or proprietary data leaked through model outputs                     |
| **Regulatory fines**    | Non-compliance with GDPR, CCPA, EU AI Act, and emerging AI regulations   |
| **Reputational damage** | Toxic, biased, or harmful outputs going viral                            |
| **Financial loss**      | Exploitation of AI-powered business logic or agentic workflows           |
| **Safety incidents**    | AI systems providing dangerous instructions or enabling illegal activity |

## Attack Categories

Know Your AI organizes attacks into six core categories, each targeting a different dimension of AI risk:

<CardGroup cols={2}>
  <Card title="Data Privacy" icon="shield-halved" href="/redteaming/pii-leakage">
    Attacks that extract personal information, training data, or system prompts from AI models.
  </Card>

  <Card title="Responsible AI" icon="scale-balanced" href="/redteaming/bias">
    Testing for bias, toxicity, fairness violations, and ethical failures in model outputs.
  </Card>

  <Card title="Security" icon="lock" href="/redteaming/bfla">
    Traditional and AI-specific security vulnerabilities including injection attacks, access control bypass, and reconnaissance.
  </Card>

  <Card title="Safety" icon="triangle-exclamation" href="/redteaming/illegal-activity">
    Probing whether models can be coerced into generating illegal, graphic, or dangerous content.
  </Card>

  <Card title="Business" icon="building" href="/redteaming/misinformation">
    Attacks targeting business integrity — misinformation, IP theft, and competitive intelligence extraction.
  </Card>

  <Card title="Agentic" icon="robot" href="/redteaming/goal-theft">
    Vulnerabilities unique to AI agents — goal hijacking, tool abuse, excessive autonomy, and multi-agent compromise.
  </Card>
</CardGroup>

## How to Use This Guide

Each attack page in this section covers:

1. **What is the attack** — A clear definition and explanation
2. **Why it matters** — Real-world impact and risk context
3. **Example scenarios** — Concrete examples of how the attack manifests
4. **Mitigation strategies** — Defensive measures and best practices

Use these pages as a reference when designing your red-team evaluations in Know Your AI, or as an educational resource for understanding the threat landscape facing modern AI systems.