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docs: Overhaul AI LLM Red Team Handbook table of contents, introducing linked chapters, new parts, and field manuals.

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## Table of Contents
### PART I: FOUNDATIONS
### Part I: Foundations
1. Introduction to AI Red Teaming
2. Ethics, Legal, and Stakeholder Communication
3. The Red Teamer's Mindset
- [Chapter 1: Introduction to AI Red Teaming](Chapter_01_Introduction_to_AI_Red_Teaming.md)
- [Chapter 2: Ethics, Legal, and Stakeholder Communication](Chapter_02_Ethics_Legal_and_Stakeholder_Communication.md)
- [Chapter 3: The Red Teamer's Mindset](Chapter_03_The_Red_Teamers_Mindset.md)
### PART II: PROJECT PREPARATION
### Part II: Project Preparation
4. SOW, Rules of Engagement, and Client Onboarding
5. Threat Modeling and Risk Analysis
6. Scoping an Engagement
7. Lab Setup and Environmental Safety
8. Evidence, Documentation, and Chain of Custody
- [Chapter 4: SOW, Rules of Engagement, and Client Onboarding](Chapter_04_SOW_Rules_of_Engagement_and_Client_Onboarding.md)
- [Chapter 5: Threat Modeling and Risk Analysis](Chapter_05_Threat_Modeling_and_Risk_Analysis.md)
- [Chapter 6: Scoping an Engagement](Chapter_06_Scoping_an_Engagement.md)
- [Chapter 7: Lab Setup and Environmental Safety](Chapter_07_Lab_Setup_and_Environmental_Safety.md)
- [Chapter 8: Evidence, Documentation, and Chain of Custody](Chapter_08_Evidence_Documentation_and_Chain_of_Custody.md)
### PART III: TECHNICAL FUNDAMENTALS
### Part III: Operational Workflows
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
- [Chapter 9: Writing Effective Reports and Deliverables](Chapter_09_Writing_Effective_Reports_and_Deliverables.md)
- [Chapter 10: Presenting Results and Remediation Guidance](Chapter_10_Presenting_Results_and_Remediation_Guidance.md)
- [Chapter 11: Lessons Learned and Building Future Readiness](Chapter_11_Lessons_Learned_and_Building_Future_Readiness.md)
### PART IV: ATTACKS & TECHNIQUES
### Part IV: Technical Fundamentals
14. Prompt Injection (Direct/Indirect, 1st/3rd Party)
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/Resource Exhaustion
22. Cross-Modal & Multimodal Attacks
23. Advanced Persistence and Chaining
24. Social Engineering with LLMs
- [Chapter 12: Retrieval-Augmented Generation (RAG) Pipelines](Chapter_12_Retrieval_Augmented_Generation_RAG_Pipelines.md)
- [Chapter 13: Data Provenance and Supply Chain Security](Chapter_13_Data_Provenance_and_Supply_Chain_Security.md)
### PART V: DEFENSE & MITIGATION
### Part V: Attacks & Techniques
25. Input Filtering and Sanitization
26. Output Validation and Safe Execution
27. Monitoring and Anomaly Detection
28. Safe Plugin/Function Design
29. Defense-in-Depth Patterns
30. Tuning, Fine-tuning, and RLHF for Security
- [Chapter 14: Prompt Injection (Direct/Indirect, 1st/3rd Party)](Chapter_14_Prompt_Injection.md)
- [Chapter 15: Data Leakage and Extraction](Chapter_15_Data_Leakage_and_Extraction.md)
- [Chapter 16: Jailbreaks and Bypass Techniques](Chapter_16_Jailbreaks_and_Bypass_Techniques.md)
- [Chapter 17: Plugin and API Exploitation](Chapter_17_Plugin_and_API_Exploitation.md)
- [Chapter 18: Evasion, Obfuscation, and Adversarial Inputs](Chapter_18_Evasion_Obfuscation_and_Adversarial_Inputs.md)
- [Chapter 19: Training Data Poisoning](Chapter_19_Training_Data_Poisoning.md)
- [Chapter 20: Model Theft and Membership Inference](Chapter_20_Model_Theft_and_Membership_Inference.md)
- [Chapter 21: Model DoS and Resource Exhaustion](Chapter_21_Model_DoS_Resource_Exhaustion.md)
- [Chapter 22: Cross-Modal and Multimodal Attacks](Chapter_22_Cross_Modal_Multimodal_Attacks.md)
- [Chapter 23: Advanced Persistence and Chaining](Chapter_23_Advanced_Persistence_Chaining.md)
- [Chapter 24: Social Engineering with LLMs](Chapter_24_Social_Engineering_LLMs.md)
### PART VI: OPERATIONAL WORKFLOWS
### Part VI: Defense & Mitigation
31. Automating Red Teaming (Tooling, CI/CD)
32. Reporting: Technical, Executive, and Remediation
33. After-Action Review, Feedback Loops, and Knowledge Transfer
- [Chapter 25: Advanced Adversarial ML](Chapter_25_Advanced_Adversarial_ML.md)
- [Chapter 26: Supply Chain Attacks on AI](Chapter_26_Supply_Chain_Attacks_on_AI.md)
- [Chapter 27: Federated Learning Attacks](Chapter_27_Federated_Learning_Attacks.md)
- [Chapter 28: AI Privacy Attacks](Chapter_28_AI_Privacy_Attacks.md)
- [Chapter 29: Model Inversion Attacks](Chapter_29_Model_Inversion_Attacks.md)
- [Chapter 30: Backdoor Attacks](Chapter_30_Backdoor_Attacks.md)
### PART VII: CASE STUDIES, WAR STORIES, AND ANTI-PATTERNS
### Part VII: Advanced Operations
34. Real-world Engagements (with artifacts, scrubbed)
35. Fails, Lessons, and What Not to Do
- [Chapter 31: AI System Reconnaissance](Chapter_31_AI_System_Reconnaissance.md)
- [Chapter 32: Automated Attack Frameworks](Chapter_32_Automated_Attack_Frameworks.md)
- [Chapter 33: Red Team Automation](Chapter_33_Red_Team_Automation.md)
- [Chapter 34: Defense Evasion Techniques](Chapter_34_Defense_Evasion_Techniques.md)
- [Chapter 35: Post-Exploitation in AI Systems](Chapter_35_Post-Exploitation_in_AI_Systems.md)
- [Chapter 36: Reporting and Communication](Chapter_36_Reporting_and_Communication.md)
- [Chapter 37: Remediation Strategies](Chapter_37_Remediation_Strategies.md)
- [Chapter 38: Continuous Red Teaming](Chapter_38_Continuous_Red_Teaming.md)
- [Chapter 39: AI Bug Bounty Programs](Chapter_39_AI_Bug_Bounty_Programs.md)
### PART VIII: ADVANCED TOPICS
### Part VIII: Advanced Topics
36. Graph Theory in Red Teaming
37. Formal Methods and Verification
38. Regulatory Compliance, AI Act, and Standards
39. Ethics in the Age of AGI
- [Chapter 40: Compliance and Standards](Chapter_40_Compliance_and_Standards.md)
- [Chapter 41: Industry Best Practices](Chapter_41_Industry_Best_Practices.md)
- [Chapter 42: Case Studies and War Stories](Chapter_42_Case_Studies_and_War_Stories.md)
- [Chapter 43: Future of AI Red Teaming](Chapter_43_Future_of_AI_Red_Teaming.md)
- [Chapter 44: Emerging Threats](Chapter_44_Emerging_Threats.md)
- [Chapter 45: Building an AI Red Team Program](Chapter_45_Building_an_AI_Red_Team_Program.md)
- [Chapter 46: Conclusion and Next Steps](Chapter_46_Conclusion_and_Next_Steps.md)
### PART IX: HANDS-ON LABS AND EXERCISES
### Field Manuals (Operational Playbooks)
40. Guided Attack Scenarios and Labs
41. Mastery Rubrics and Self-Assessment
42. Career Growth and Continuous Learning
- [Field Manual Index](Field_Manual_00_Index.md)
- [Quick Reference Card](field_manuals/Field_Manual_Quick_Reference.md)
### PART X: REFERENCE MATERIALS
#### Attack Playbooks
43. Visual Glossary and Cheat Sheets
44. MITRE and OWASP Cross-References
45. Templates and Sample Docs
46. Further Reading, Communities, and Conferences
- [Playbook 01: Prompt Injection](field_manuals/Field_Manual_01_Prompt_Injection_Playbook.md)
- [Playbook 02: Data Leakage](field_manuals/Field_Manual_02_Data_Leakage_Playbook.md)
- [Playbook 03: Jailbreaks](field_manuals/Field_Manual_03_Jailbreak_Playbook.md)
- [Playbook 04: Plugin Exploitation](field_manuals/Field_Manual_04_Plugin_Exploitation_Playbook.md)
- [Playbook 05: Evasion & Obfuscation](field_manuals/Field_Manual_05_Evasion_Playbook.md)
- [Playbook 06: Data Poisoning](field_manuals/Field_Manual_06_Data_Poisoning_Playbook.md)
- [Playbook 07: Model Theft](field_manuals/Field_Manual_07_Model_Theft_Playbook.md)
- [Playbook 08: DoS Attacks](field_manuals/Field_Manual_08_DoS_Playbook.md)
- [Playbook 09: Multimodal Attacks](field_manuals/Field_Manual_09_Multimodal_Playbook.md)
- [Playbook 10: Persistence & Chaining](field_manuals/Field_Manual_10_Persistence_Playbook.md)
- [Playbook 11: Social Engineering](field_manuals/Field_Manual_11_Social_Engineering_Playbook.md)
### Reference Materials
_(To be added)_
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@@ -13718,4 +13735,4 @@ _This chapter provided comprehensive coverage of jailbreak techniques, from clas
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_The field of AI/LLM red teaming evolves rapidly! Stay engaged with community updates, train with new attack techniques, and continually share knowledge to build a safer, more robust future for intelligent systems._
_The field of AI/LLM red teaming evolves rapidly! Stay engaged with community updates, train with new attack techniques, and continually share knowledge to build a safer, more robust future for intelligent systems._