www-project-ai-testing-guide/Document/README.md

OWASP AI Testing Guide Table of Contents

1. Introduction

• 1.1 Preface and Contributors

• 1.2 Principles of AI Testing

• 1.3 Objectives of OWASP AI Testing Guide

2. Threat Modeling AI Systems

• 2.1 Identify AI System Threats

• 2.1.1 Map OWASP AI Threats To AI Architectural Components

• 2.1.2 Identify AI System Responsible AI (RAI)/Trustworthy AI Threats

3. OWASP AI Testing Guide Framework

• 3.1 🟦 AI Application Testing

• 3.1.1 | AITG-APP-01 | Testing for Prompt Injection |

• 3.1.2 | AITG-APP-02 | Testing for Indirect Prompt Injection |

• 3.1.3 | AITG-APP-03 | Testing for Sensitive Data Leak |

• 3.1.4 | AITG-APP-04 | Testing for Input Leakage |

• 3.1.5 | AITG-APP-05 | Testing for Unsafe Outputs |

• 3.1.6 | AITG-APP-06 | Testing for Agentic Behavior Limits |

• 3.1.7 | AITG-APP-07 | Testing for Prompt Disclosure |

• 3.1.8 | AITG-APP-08 | Testing for Embedding Manipulation |

• 3.1.9 | AITG-APP-09 | Testing for Model Extraction |

• 3.1.10 | AITG-APP-10 | Testing for content Bias |

• 3.1.11 | AITG-APP-11 | Testing for Hallucinations |

• 3.1.12 | AITG-APP-12 | Testing for Toxic Output |

• 3.1.13 | AITG-APP-13 | Testing for Over-Reliance on AI |

• 3.1.14 | AITG-APP-14 | Testing for Explainability and Interpretability |

• 3.2 🟪 AI Model Testing

• 3.2.1 | AITG-MOD-01 | Testing for Evasion Attacks |

• 3.2.2 | AITG-MOD-02 | Testing for Runtime Model Poisoning |

• 3.2.3 | AITG-MOD-03 | Testing for Poisoned Training Sets |

• 3.2.4 | AITG-MOD-04 | Testing for Membership Inference |

• 3.2.5 | AITG-MOD-05 | Testing for Inversion Attacks |

• 3.2.6 | AITG-MOD-06 | Testing for Robustness to New Data |

• 3.2.7 | AITG-MOD-07 | Testing for Goal Alignment |

• 3.3 🟩 AI Infrastructure Testing

• 3.3.1 | AITG-INF-01 | Testing for Supply Chain Tampering |

• 3.3.2 | AITG-INF-02 | Testing for Resource Exhaustion |

• 3.3.3 | AITG-INF-03 | Testing for Plugin Boundary Violations |

• 3.3.4 | AITG-INF-04 | Testing for Capability Misuse |

• 3.3.5 | AITG-INF-05 | Testing for Fine-tuning Poisoning |

• 3.3.6 | AITG-INF-06 | Testing for Dev-Time Model Theft |

• 3.4 🟨 AI Data Testing

• 3.4.1 | AITG-DAT-01 | Testing for Training Data Exposure |

• 3.4.2 | AITG-DAT-02 | Testing for Runtime Exfiltration |

• 3.4.3 | AITG-DAT-03 | Testing for Dataset Diversity & Coverage |

• 3.4.4 | AITG-DAT-04 | Testing for Harmful in Data |

• 3.4.5 | AITG-DAT-05 | Testing for Data Minimization & Consent |

4. Chapter 4: Appendices and References

• 4.1 Appendix A: Rationale For Using SAIF (Secure AI Framework)

• 4.2 Appendix B: Distributed, Immutable, Ephemeral (DIE) Threat Identification

• 4.3 Appendix C: Risk Lifecycle for Secure AI Systems

• 4.4 Appendix D: Threat Enumeration to AI Architecture Components

• 4.5 Appendix E: Mapping AI Threats Against AI Systems Vulnerabilities (CVEs & CWEs)

• 4.6 References