feat: Add banner image to all documentation chapters and field manuals.

2026-08-27 21:30:27 +02:00 · 2025-12-05 18:13:16 +01:00
parent 2c589d6463
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+![Banner](../assets/banner.svg)
+
 # Red Teaming AI & LLMs: The Consultant’s Complete Handbook

 ## Table of Contents
@@ -80,6 +82,8 @@

 ---

+![Banner](../assets/banner.svg)
+
 # Chapter 1: Introduction to AI Red Teaming

 ## 1.1 What Is AI Red Teaming?
@@ -161,6 +165,8 @@ The handbook is organized for practical learning and use:

 _Proceed to the next chapter to explore ethical and legal essentials, and begin developing the professional approach required of every AI red teamer._

+![Banner](../assets/banner.svg)
+
 # Chapter 2: Ethics, Legal, and Stakeholder Communication

 ## 2.1 Why Ethics Matter in AI Red Teaming
@@ -261,6 +267,8 @@ In AI red teaming, technical findings may have legal, business, or even social i

 _In the next chapter, you’ll develop the mindset that distinguishes effective AI red teamers from traditional security testers, bridging technology, psychology, and business acuity._

+![Banner](../assets/banner.svg)
+
 # Chapter 3: The Red Teamer's Mindset

 ## 3.1 What Sets a Red Teamer Apart?
@@ -339,6 +347,8 @@ Field engagements can be high-stress: production outages, tense clients, critica

 _Mastering the red team mindset primes you for the work ahead: scoping, planning, and then executing engagements with insight, rigor, and integrity. Proceed to the next chapter to learn how to prepare and manage a professional AI red team project from start to finish._

+![Banner](../assets/banner.svg)
+
 # Chapter 4: SOW, Rules of Engagement, and Client Onboarding

 ## 4.1 The Foundation of a Secure Engagement
@@ -456,6 +466,8 @@ Before you start:

 _Solid foundations prevent project failure and foster trust. The next chapter will guide you through threat modeling and risk analysis for AI systems, helping you identify what matters most before you begin attacking._

+![Banner](../assets/banner.svg)
+
 # Chapter 5: Threat Modeling and Risk Analysis

 ## 5.1 Why Threat Modeling Matters in AI Red Teaming
@@ -579,6 +591,8 @@ A good threat model is:

 _With a strong threat model, your red team engagement becomes risk-driven and results-focused. The next chapter will walk you through scoping these findings into a feasible, valuable engagement plan._

+![Banner](../assets/banner.svg)
+
 # Chapter 6: Scoping an Engagement

 ## 6.1 The Importance of Proper Scoping
@@ -685,6 +699,8 @@ An accurately scoped engagement shows professionalism and respect for the client

 _With a precise scope in place, you are ready to establish the laboratory, test environments, and safety measures needed for executing a secure and efficient AI red teaming exercise. Continue to the next chapter for practical lab setup and environmental safety._

+![Banner](../assets/banner.svg)
+
 # Chapter 7: Lab Setup and Environmental Safety

 ## 7.1 Why Lab Setup and Environmental Safety Matter
@@ -778,6 +794,8 @@ Remember:

 _With a robust lab and clear safety controls in place, you’re prepared to gather and preserve evidence in a trustworthy manner. Continue to the next chapter to master documentation and evidence handling in AI red team engagements._

+![Banner](../assets/banner.svg)
+
 # Chapter 8: Evidence, Documentation, and Chain of Custody

 ## 8.1 The Role of Evidence in Red Teaming
@@ -890,6 +908,8 @@ A robust chain of custody ensures that all evidence remains trustworthy and trac

 _With evidence and documentation in place, you’re equipped to deliver clear, credible findings. The next chapter will guide you through the art of writing actionable, impactful red team reports for both technical and executive audiences._

+![Banner](../assets/banner.svg)
+
 # Chapter 9: Writing Effective Reports and Deliverables

 ## 9.1 The Purpose of Red Team Reports
@@ -1014,6 +1034,8 @@ Successful reports are tailored to multiple audiences, such as:

 _You are now ready to communicate your findings with clarity and impact. The next chapter will cover presenting results to both technical and non-technical stakeholders—ensuring your work leads to measurable improvements in AI security._

+![Banner](../assets/banner.svg)
+
 # Chapter 10: Presenting Results and Remediation Guidance

 ## 10.1 The Importance of Presentation
@@ -1098,6 +1120,8 @@ Delivering findings is more than handing over a report—it's about ensuring you

 _Professional communication and practical remediation guidance ensure your red teaming work translates into real, measurable improvements. The next chapter will explore lessons learned, common pitfalls, and how to build a mature AI/LLM red teaming practice._

+![Banner](../assets/banner.svg)
+
 # Chapter 11: Lessons Learned and Building Future Readiness

 ## 11.1 Common Pitfalls in AI/LLM Red Teaming
@@ -1156,6 +1180,8 @@ To make AI red teaming a sustainable part of your organization’s security post

 _By systematically learning and adapting, your AI red teaming program matures—helping organizations stay resilient amid the evolving risks and rewards of intelligent systems._

+![Banner](../assets/banner.svg)
+
 # Chapter 12: Retrieval-Augmented Generation (RAG) Pipelines

 ## 12.1 What Is Retrieval-Augmented Generation (RAG)?
@@ -2434,6 +2460,8 @@ class RAGAccessControlTester:

 _RAG systems represent one of the most powerful—and vulnerable—implementations of LLM technology in enterprise environments. By understanding their architecture, attack surfaces, and testing methodologies, red teamers can help organizations build secure, production-ready AI assistants. The next chapter will explore data provenance and supply chain security—critical for understanding where your AI system's data comes from and how it can be compromised._

+![Banner](../assets/banner.svg)
+
 # Chapter 13: Data Provenance and Supply Chain Security

 ## 13.1 Understanding Data Provenance in AI/LLM Systems
@@ -4293,6 +4321,8 @@ def detect_insider_poisoning(training_data, baseline_distribution):

 ---

+![Banner](../assets/banner.svg)
+
 # Chapter 14: Prompt Injection (Direct/Indirect, 1st/3rd Party)

 ## 14.1 Introduction to Prompt Injection
@@ -8361,6 +8391,8 @@ _Prompt injection represents the defining security challenge of the LLM era. Lik

 ---

+![Banner](../assets/banner.svg)
+
 # Chapter 15: Data Leakage and Extraction

 ## 15.1 Introduction to Data Leakage in LLMs
@@ -12137,6 +12169,8 @@ _Continue to Chapter 16: Jailbreaks and Bypass Techniques to learn how attackers

 ---

+![Banner](../assets/banner.svg)
+
 # Chapter 16: Jailbreaks and Bypass Techniques

 _This chapter provides comprehensive coverage of jailbreak techniques, bypass methods, testing methodologies, and defenses for LLM systems._
@@ -13684,4 +13718,4 @@ _This chapter provided comprehensive coverage of jailbreak techniques, from clas

 ---

-_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._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # **AI/LLM Red Team Field Manual**

 > **For Junior Penetration Testers**: This manual is designed as a complete, standalone field guide. Follow the Quick Start below to begin testing within 15 minutes.
@@ -184,6 +186,8 @@ For step-by-step attack procedures with extensive code examples, see the **modul

 ---

+![Banner](../assets/banner.svg)
+
 ## **1. Introduction: Rules of Engagement (RoE)**

 Define in writing: in-scope systems/models, allowed techniques, test windows, handling of sensitive/user data, communications, and cleanup steps. Secure stakeholder approval before any engagement.
@@ -200,6 +204,8 @@ Define in writing: in-scope systems/models, allowed techniques, test windows, ha

 ---

+![Banner](../assets/banner.svg)
+
 ## **1.5 Environment Setup & Configuration**

 This section provides detailed instructions for setting up a professional testing environment.
@@ -484,6 +490,8 @@ API Config File: ✅

 ---

+![Banner](../assets/banner.svg)
+
 ## **2. Red Teaming Phases**

 This section outlines the complete workflow for conducting an LLM/AI red team engagement.
@@ -757,6 +765,8 @@ chmod +x cleanup.sh

 ---

+![Banner](../assets/banner.svg)
+
 ## **3\. Attack Types & Practical Test Examples**

 ### **3.1 Prompt Injection**
@@ -1240,6 +1250,8 @@ afl-fuzz \-i testcase_dir \-o findings_dir \-- ./your_cli_target @@

 ---

+![Banner](../assets/banner.svg)
+
 ## **4\. Tools Reference & CLI Commands**

 **Garak**
@@ -1284,6 +1296,8 @@ afl-fuzz \-i testcase_dir \-o findings_dir \-- ./your_cli_target @@

 ---

+![Banner](../assets/banner.svg)
+
 ## **5\. Attack-Type–to–Tool Quick Lookup Table**

 | Attack Type                | Tool(s)                 | Install & Example CLI |
@@ -1301,6 +1315,8 @@ afl-fuzz \-i testcase_dir \-o findings_dir \-- ./your_cli_target @@

 ---

+![Banner](../assets/banner.svg)
+
 ## **5.5 API Configuration Guide**

 This section provides detailed configuration for major LLM providers.
@@ -1567,6 +1583,8 @@ echo "✅ Provider test complete!"

 ---

+![Banner](../assets/banner.svg)
+
 ## **6\. Reporting Guidance**

 Report every finding with:
@@ -1579,6 +1597,8 @@ Report every finding with:

 ---

+![Banner](../assets/banner.svg)
+
 ## **7\. Additional Guidance**

 - Use isolated environments, cloud sandboxes, and always comply with organizational rules and ethical guidelines.
@@ -1589,6 +1609,8 @@ Report every finding with:

 ---

+![Banner](../assets/banner.svg)
+
 ## **8. Troubleshooting Guide**

 This section covers common issues and their solutions.
@@ -2433,4 +2455,4 @@ afl-fuzz -i testcase_dir -o findings_dir -- ./your_cli_target @@

 - When testing or reporting, document each finding with the mapped MITRE ATLAS ID for clear traceability.
 - Update mappings as ATLAS evolves or as you discover new techniques.
- This appendix may be copied or embedded directly into any detailed section of your field manual for immediate reference.
+- This appendix may be copied or embedded directly into any detailed section of your field manual for immediate reference.
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 1: Introduction to AI Red Teaming

 ## 1.1 What Is AI Red Teaming?
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 2: Ethics, Legal, and Stakeholder Communication

 ## 2.1 Why Ethics Matter in AI Red Teaming
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 3: The Red Teamer's Mindset

 ## 3.1 What Sets a Red Teamer Apart?
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 4: SOW, Rules of Engagement, and Client Onboarding

 ## 4.1 The Foundation of a Secure Engagement
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 5: Threat Modeling and Risk Analysis

 ## 5.1 Why Threat Modeling Matters in AI Red Teaming
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 6: Scoping an Engagement

 ## 6.1 The Importance of Proper Scoping
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 7: Lab Setup and Environmental Safety

 ## 7.1 Why Lab Setup and Environmental Safety Matter
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 8: Evidence, Documentation, and Chain of Custody

 ## 8.1 The Role of Evidence in Red Teaming
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 9: Writing Effective Reports and Deliverables

 ## 9.1 The Purpose of Red Team Reports
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 10: Presenting Results and Remediation Guidance

 ## 10.1 The Importance of Presentation
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 11: Lessons Learned and Building Future Readiness

 ## 11.1 Common Pitfalls in AI/LLM Red Teaming
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 12: Retrieval-Augmented Generation (RAG) Pipelines

 ## 12.1 What Is Retrieval-Augmented Generation (RAG)?
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 13: Data Provenance and Supply Chain Security

 ## 13.1 Understanding Data Provenance in AI/LLM Systems
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 14: Prompt Injection (Direct/Indirect, 1st/3rd Party)

 ## 14.1 Introduction to Prompt Injection
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 15: Data Leakage and Extraction

 ## 15.1 Introduction to Data Leakage in LLMs
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 16: Jailbreaks and Bypass Techniques

 _This chapter provides comprehensive coverage of jailbreak techniques, bypass methods, testing methodologies, and defenses for LLM systems._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 17: Plugin and API Exploitation

 _This chapter provides comprehensive coverage of security issues in LLM plugins, APIs, and third-party integrations, including architecture analysis, vulnerability discovery, exploitation techniques, and defensive strategies._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 18: Evasion, Obfuscation, and Adversarial Inputs

 _This chapter provides comprehensive coverage of evasion techniques, obfuscation methods, and adversarial input strategies used to bypass LLM security controls, along with detection and mitigation approaches._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 19: Training Data Poisoning

 _This chapter provides comprehensive coverage of training data poisoning attacks, backdoor injection techniques, model integrity compromise, detection methodologies, and defense strategies for LLM systems._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 20: Model Theft and Membership Inference

 _This chapter provides comprehensive coverage of model extraction attacks, membership inference techniques, privacy violations in ML systems, intellectual property theft, watermarking, detection methods, and defense strategies for protecting model confidentiality._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 21: Model DoS and Resource Exhaustion

 _This chapter provides comprehensive coverage of Denial of Service (DoS) attacks on LLM systems, resource exhaustion techniques, economic attacks, detection methods, and defense strategies for protecting API availability and cost management._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 22: Cross-Modal and Multimodal Attacks

 _This chapter provides comprehensive coverage of attacks on multimodal AI systems, including vision-language models (GPT-4V, Claude 3, Gemini), image-based prompt injection, adversarial images, audio attacks, cross-modal exploitation techniques, detection methods, and defense strategies._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 23: Advanced Persistence and Chaining

 _This chapter provides comprehensive coverage of advanced persistence techniques and attack chaining for LLM systems, including context manipulation, multi-turn attacks, state persistence, chain-of-thought exploitation, prompt chaining, session hijacking, detection methods, and defense strategies._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 24: Social Engineering with LLM

 _This chapter provides comprehensive coverage of social engineering attacks powered by Large Language Models, including AI-generated phishing, impersonation attacks, trust exploitation, persuasion technique automation, spear phishing at scale, pretexting, detection methods, defense strategies, and critical ethical considerations._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 25: Advanced Adversarial ML

 _This chapter is currently under development._
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+![Banner](../assets/banner.svg)
+
 # Chapter 26: Supply Chain Attacks on AI

 _This chapter is currently under development._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 27: Federated Learning Attacks

 _This chapter is currently under development._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 28: AI Privacy Attacks

 _This chapter is currently under development._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 29: Model Inversion Attacks

 _This chapter is currently under development._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 30: Backdoor Attacks

 _This chapter is currently under development._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 31: AI System Reconnaissance

 _This chapter is currently under development._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 32: Automated Attack Frameworks

 _This chapter is currently under development._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 33: Red Team Automation

 _This chapter is currently under development._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 34: Defense Evasion Techniques

 _This chapter is currently under development._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 35: Post-Exploitation in AI Systems

 _This chapter is currently under development._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 36: Reporting and Communication

 _This chapter is currently under development._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 37: Remediation Strategies

 _This chapter is currently under development._
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+![Banner](../assets/banner.svg)
+
 # Chapter 38: Continuous Red Teaming

 _This chapter is currently under development._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 39: AI Bug Bounty Programs

 _This chapter is currently under development._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 40: Compliance and Standards

 _This chapter is currently under development._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 41: Industry Best Practices

 _This chapter is currently under development._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 42: Case Studies and War Stories

 _This chapter is currently under development._
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+![Banner](../assets/banner.svg)
+
 # Chapter 43: Future of AI Red Teaming

 _This chapter is currently under development._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 44: Emerging Threats

 _This chapter is currently under development._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 45: Building an AI Red Team Program

 _This chapter is currently under development._
@@ -1,3 +1,5 @@
+![Banner](../assets/banner.svg)
+
 # Chapter 46: Conclusion and Next Steps

 _This chapter is currently under development._
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 # Summary

+![Banner](../assets/banner.svg)
+
 ## Introduction

 // Todo: Add introduction
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+![Banner](../../assets/banner.svg)
+
 # Field Manual Playbook 01: Prompt Injection

 **Attack Type**: Prompt Injection (Direct & Indirect)  
@@ -1,3 +1,5 @@
+![Banner](../../assets/banner.svg)
+
 # Field Manual Playbook 02: Data Leakage & Extraction

 **Attack Type**: Training Data & Memory Extraction  
@@ -1,3 +1,5 @@
+![Banner](../../assets/banner.svg)
+
 # Field Manual Playbook 03: Jailbreaks & Bypass

 **Attack Type**: Content Filter & Safety Bypass  
@@ -1,3 +1,5 @@
+![Banner](../../assets/banner.svg)
+
 # Field Manual Playbook 04: Plugin & API Exploitation

 **Attack Type**: Plugin/Function Calling Exploitation  
@@ -1,3 +1,5 @@
+![Banner](../../assets/banner.svg)
+
 # Field Manual Playbook 05: Evasion & Obfuscation

 **Attack Type**: Input Filter Bypass  
@@ -1,3 +1,5 @@
+![Banner](../../assets/banner.svg)
+
 # Field Manual Playbook 06: Data Poisoning

 **Attack Type**: Training Data / RAG Document Poisoning  
@@ -1,3 +1,5 @@
+![Banner](../../assets/banner.svg)
+
 # Field Manual Playbook 07: Model Theft & Extraction

 **Attack Type**: Model Stealing / Extraction  
@@ -1,3 +1,5 @@
+![Banner](../../assets/banner.svg)
+
 # Field Manual Playbook 08: DoS & Resource Exhaustion

 **Attack Type**: Denial of Service & Cost Inflation  
@@ -1,3 +1,5 @@
+![Banner](../../assets/banner.svg)
+
 # Field Manual Playbook 09: Multimodal Attacks

 **Attack Type**: Vision/Audio + LLM Exploitation  
@@ -1,3 +1,5 @@
+![Banner](../../assets/banner.svg)
+
 # Field Manual Playbook 10: Persistence & Chaining

 **Attack Type**: Multi-Turn Attack Sequences  
@@ -1,3 +1,5 @@
+![Banner](../../assets/banner.svg)
+
 # Field Manual Playbook 11: Social Engineering with LLMs

 **Attack Type**: AI-Powered Phishing & Impersonation  
@@ -1,3 +1,5 @@
+![Banner](../../assets/banner.svg)
+
 # Field Manual - Quick Reference Card

 **One-page cheat sheet for junior testers**