Agentic Design

Patterns

Adversarial Attacks

Creating inputs designed to fool AI models

2
Techniques
1
high
high Complexity
1
medium
medium Complexity

Available Techniques

Adversarial Examples

(AE)
high

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

Model reliability compromiseSecurity system bypassCritical system failuresMalicious exploitation
👻

Evasion Attacks

(EA)
medium

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

Security system compromiseUndetected threatsFalse sense of securitySystematic vulnerabilities

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

AI Red Teaming

closed

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