# Adversarial Attacks


📂 ai-red-teaming

## AI RED TEAMING TECHNIQUES

# Adversarial Attacks

Creating inputs designed to fool AI models

## Available Techniques

### Adversarial Examples

Crafted inputs designed to fool AI models into making incorrect predictions or classifications.

#### KEY FEATURES

- •
Perturbation-based attacks

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Gradient-based optimization

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Targeted misclassification

#### PRIMARY DEFENSES

- •
Adversarial training

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Input preprocessing and filtering

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Ensemble defense methods

#### KEY RISKS

### Evasion Attacks

Techniques to evade detection systems and security mechanisms through input manipulation.

#### KEY FEATURES

- •
Detection system bypass

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Pattern obfuscation

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Steganographic techniques

#### PRIMARY DEFENSES

- •
Multi-modal detection systems

- •
Ensemble-based approaches

- •
Continuous learning mechanisms

#### KEY RISKS

### Ethical Guidelines for Adversarial Attacks

When working with adversarial attacks techniques, always follow these ethical guidelines:

- • Only test on systems you own or have explicit written permission to test

- • Focus on building better defenses, not conducting attacks

- • Follow responsible disclosure practices for any vulnerabilities found

- • Document and report findings to improve security for everyone

- • Consider the potential impact on users and society

- • Ensure compliance with all applicable laws and regulations

FROM THE ENGINEER BEHIND THIS CATALOG

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## AI Red Teaming

## AI Red Teaming Techniques