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

• • Gradient-based optimization

• • Targeted misclassification

PRIMARY DEFENSES

• • Adversarial training

• • Input preprocessing and filtering

• • Ensemble defense methods

KEY RISKS

Evasion Attacks#

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

KEY FEATURES

• • Detection system bypass

• • Pattern obfuscation

• • 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

Get your agent system red-teamed#

The attacks documented here work on production agent systems every day. Have yours tested before someone else does: prompt injection, jailbreaks, tool misuse and data exfiltration, with every finding written up next to its fix.

€750 instead of €1,500, one week, written report and walkthrough call, until 30 September

AI Red Teaming#

AI Red Teaming Techniques#