mirror of
https://github.com/Shiva108/ai-llm-red-team-handbook.git
synced 2026-02-12 14:42:46 +00:00
4.1 KiB
4.1 KiB
Summary
- 1. Introduction to AI Red Teaming
- 2. Ethics, Legal, and Stakeholder Communication
- 3. The Red Teamer's Mindset
- 4. SOW, Rules of Engagement, and Onboarding
- 5. Threat Modeling and Risk Analysis
- 6. Scoping an Engagement
- 7. Lab Setup and Environmental Safety
- 8. Evidence Documentation and Chain of Custody
- 9. LLM Architectures and System Components
- 10. Tokenization, Context, and Generation
- 11. Plugins, Extensions, and External APIs
- 12. Retrieval Augmented Generation (RAG) Pipelines
- 13. Data Provenance and Supply Chain Security
- 14. Prompt Injection
- 15. Data Leakage and Extraction
- 16. Jailbreaks and Bypass Techniques
- 17. Plugin and API Exploitation
- 18. Evasion, Obfuscation, and Adversarial Inputs
- 19. Training Data Poisoning
- 20. Model Theft and Membership Inference
- 21. Model DoS and Resource Exhaustion
- 22. Cross-Modal and Multimodal Attacks
- 23. Advanced Persistence and Chaining
- 24. Social Engineering LLMs
- 25. Advanced Adversarial ML
- 26. Supply Chain Attacks on AI
- 27. Federated Learning Attacks
- 28. AI Privacy Attacks
- 29. Model Inversion Attacks
- 30. Backdoor Attacks
- 31. AI System Reconnaissance
- 32. Automated Attack Frameworks
- 33. Red Team Automation
- 34. Defense Evasion Techniques
- 35. Post-Exploitation in AI Systems
- 36. Reporting and Communication
- 37. Remediation Strategies
- 38. Continuous Red Teaming
- 39. AI Bug Bounty Programs
- 40. Compliance and Standards
- 41. Industry Best Practices
- 42. Case Studies and War Stories
- 43. Future of AI Red Teaming
- 44. Emerging Threats
- 45. Building an AI Red Team Program
- 46. Conclusion and Next Steps