docs: remove "References and Further Reading" sections from various chapters and the main handbook.

docs/AI LLM Red Team Handbook.md

@@ -13429,78 +13429,6 @@ Layer 5: Governance
 
 ---
 
-## 15.16 References and Further Reading
-
-### Academic Papers on Data Extraction
-
-1. Carlini, N., et al. (2021). "Extracting Training Data from Large Language Models." _USENIX Security_.
-
-2. Carlini, N., et al. (2023). "Extracting Training Data from ChatGPT." _arXiv preprint arXiv:2311.17035_.
-
-3. Shokri, R., et al. (2017). "Membership Inference Attacks Against Machine Learning Models." _IEEE S&P_.
-
-4. Fredrikson, M., et al. (2015). "Model Inversion Attacks that Exploit Confidence Information." _CCS_.
-
-5. Abadi, M., et al. (2016). "Deep Learning with Differential Privacy." _CCS_.
-
-### Industry Standards and Frameworks
-
-- **OWASP Top 10 for LLMs**: <https://owasp.org/www-project-top-10-for-large-language-model-applications/>
-- **NIST AI Risk Management Framework**: <https://www.nist.gov/itl/ai-risk-management-framework>
-- **MITRE ATLAS**: Adversarial Threat Landscape for AI Systems
-- **ISO/IEC 27001**: Information security management
-- **SOC 2**: Trust service criteria for data security
-
-### Tools and Resources
-
-**Open-source tools**:
-
-- Garak: LLM vulnerability scanner
-- PromptInject: Adversarial prompt testing
-- Presidio: PII detection and anonymization
-- Gitleaks: Secret detection in code
-- Opacus: Differential privacy library
-
-**Commercial solutions**:
-
-- Robust Intelligence: AI security platform
-- HiddenLayer: ML security scanner
-- Protect AI: AI/ML security tools
-- Calypso AI: AI risk management
-
-### Community Forums and Discussions
-
-- **AI Security Discord/Slack communities**
-- **r/MachineLearning security discussions**
-- **OWASP AI Security Project**
-- **MLSecOps community**
-- **Responsible AI forums**
-
-### Recommended Books
-
-- "Adversarial Machine Learning" by Biggio & Roli
-- "Privacy-Preserving Machine Learning" by Liu et al.
-- "AI Safety and Security" edited by Yampolskiy
-- "The Alignment Problem" by Brian Christian
-
-### Conferences and Events
-
-- **DEF CON AI Village**
-- **Black Hat AI Security Summit**
-- **NeurIPS Security & Privacy Workshop**
-- **ICLR workshops on security**
-- **RSA Conference AI Security track**
-
-### Blogs and News
-
-- Google AI Blog (AI safety research)
-- OpenAI Research
-- Anthropic Research
-- Trail of Bits blog (ML security)
-- HiddenLayer blog
-
----
-
 ## Chapter 15 Conclusion
 
 Data leakage and extraction represent one of the most significant and persistent security challenges in LLM systems. Unlike traditional software vulnerabilities with clear patches, data baked into model weights cannot simply be "fixed" without retraining. This makes prevention—through rigorous data hygiene, architectural controls, and ongoing monitoring—absolutely critical.
@@ -14946,57 +14874,6 @@ RED_TEAM_BEST_PRACTICES = {
 
 ---
 
-## 16.16 References and Further Reading
-
-### Academic Papers
-
-1. Zou, A., et al. (2023). "Universal and Transferable Adversarial Attacks on Aligned Language Models." arXiv:2307.15043
-
-2. Wei, A., et al. (2023). "Jailbroken: How Does LLM Safety Training Fail?" arXiv:2307.02483
-
-3. Perez, F., & Ribeiro, I. (2022). "Ignore Previous Prompt: Attack Techniques For Language Models." arXiv:2211.09527
-
-4. Bai, Y., et al. (2022). "Constitutional AI: Harmlessness from AI Feedback." arXiv:2212.08073
-
-5. Ganguli, D., et al. (2022). "Red Teaming Language Models to Reduce Harms." arXiv:2209.07858
-
-6. Carlini, N., et al. (2023). "Are aligned neural networks adversarially aligned?" arXiv:2306.15447
-
-### Industry Reports
-
-- **OWASP Top 10 for LLM Applications**: <https://owasp.org/www-project-top-10-for-large-language-model-applications/>
-- **NIST AI Risk Management Framework**: <https://www.nist.gov/itl/ai-risk-management-framework>
-- **MITRE ATLAS**: <https://atlas.mitre.org/>
-
-### Tools and Frameworks
-
-- **Garak**: <https://github.com/leondz/garak>
-- **PromptInject**: <https://github.com/agencyenterprise/PromptInject>
-- **PyRIT**: <https://github.com/Azure/PyRIT>
-- **LLM Guard**: <https://github.com/laiyer-ai/llm-guard>
-
-### Books
-
-- "Adversarial Machine Learning" by Biggio & Roli
-- "AI Safety and Security" edited by Yampolskiy
-- "The Alignment Problem" by Brian Christian
-
-### Community Resources
-
-- AI Safety Discord servers
-- r/ChatGPTJailbreak
-- r/LocalLLaMA
-- DEF CON AI Village
-- Black Hat AI Security Summit
-
----
-
-**End of Chapter 16: Jailbreaks and Bypass Techniques**
-
-_This chapter provided comprehensive coverage of jailbreak techniques, from classic role-playing attacks to cutting-edge token-level exploits. Remember: jailbreak research should always be conducted responsibly, with proper authorization, and with the goal of improving AI safety for everyone._
-
----
-
 # Appendix A: Red Team Tools, Resources, and Further Reading
 
 ## A.1 Recommended Red Team Tools for AI and LLMs
@@ -15021,28 +14898,6 @@ _This chapter provided comprehensive coverage of jailbreak techniques, from clas
 
 ---
 
-## A.2 Essential Reading and References
-
-### Key Papers
-
-- Brown, T. B. et al. (2020). "Language Models are Few-Shot Learners (GPT-3)."
-- Carlini, N. et al. (2021). "Extracting Training Data from Large Language Models."
-- Wei, J. et al. (2023). "PromptBench: Systematic Benchmarking of LLM Vulnerabilities."
-- Zou, M. et al. (2023). "Cross-Modal Adversarial Attacks on Vision-Language Models."
-
-### Reports and Guides
-
-- **OpenAI Red Teaming Network:** <https://openai.com/red-teaming-network>
-- **MITRE ATLAS™ Adversarial Threat Landscape for AI Systems:** <https://atlas.mitre.org>
-- **ENISA AI Threat Landscape:** <https://www.enisa.europa.eu/publications/artificial-intelligence-threat-landscape>
-
-### Standards and Methodologies
-
-- **NIST AI Risk Management Framework (AI RMF)**
-- **ISO/IEC 24029-1:2021** – AI Robustness and Vulnerability Assessment
-
----
-
 ## A.3 Further Learning
 
 - **Workshops/Competitions:**

docs/AI_LLM Red Team Field Manual.md

@@ -1039,7 +1039,7 @@ Additional test cases, defenses, and more advanced techniques discussed in Secti
 
 ### **3.2 Jailbreaking (Safety Filter Bypass)**
 
-Bypass LLM safety filters using creative framing, role play, or rewording [Reference not found].
+Bypass LLM safety filters using creative framing, role play, or rewording .
 
 **Manual Test Prompts**:
 
@@ -1071,7 +1071,7 @@ Further jailbreaking examples, evolving DAN prompts, and defensive measures are
 
 ### **3.3 Data Leakage / Memorization**
 
-Probe for training data snippets or private content \[Reference not found\]:
+Probe for training data snippets or private content :
 
 **Manual Test Prompts**:
 
@@ -1098,7 +1098,7 @@ Further techniques for membership inference, training-data extraction, and RAG d
 
 ### **3.4 Plugin/Tool/Function Exploitation**
 
-Abuse external plugin/tool capabilities through LLMs \[Reference not found\]:
+Abuse external plugin/tool capabilities through LLMs :
 
 **Manual Test Prompts**:
 
@@ -1110,7 +1110,7 @@ Search for and summarize known exploits for \[targeted software\].
 
 ### **3.5 Denial-of-Service (DoS) & Resource Exhaustion**
 
-Trigger heavy computation, loops, or massive outputs \[Reference not found\]:
+Trigger heavy computation, loops, or massive outputs :
 
 **Manual Test Prompts**:
 
@@ -1138,7 +1138,7 @@ More testing on distributed denial, algorithmic complexity exploits, and cost-am
 
 ### **3.6 Adversarial Example Generation (Evasion)**
 
-Craft inputs that evade LLM policies or cause misclassification \[Reference not found\]:
+Craft inputs that evade LLM policies or cause misclassification :
 
 **Tool – TextAttack:** (for text)
 
@@ -1168,7 +1168,7 @@ TextAttack, Adversarial Robustness Toolbox (ART), adversarial textual perturbati
 
 ### **3.7 Data Poisoning (Training-Time Attack)**
 
-Inject malicious or biased data into model training sets \[Reference not found\]:
+Inject malicious or biased data into model training sets :
 
 **Manual Example:** Add outlier string or phrase in fine-tuning data:
 
@@ -1193,7 +1193,7 @@ Full guidance on backdoor implantation, trigger design, and poisoning-detection
 
 ### **3.8 Model Extraction/Stealing**
 
-Replicate model logic/predictions via mass querying \[Reference not found\]:
+Replicate model logic/predictions via mass querying :
 
 **Manual Example:**
 
@@ -1207,7 +1207,7 @@ pip install \-r requirements.txt
 
 ### **3.9 Output Manipulation**
 
-Force output bugs, malformed formats, or payload injection \[Reference not found\]:
+Force output bugs, malformed formats, or payload injection :
 
 **Manual Test Prompts**:
 
@@ -1216,7 +1216,7 @@ Embed \[harmful script tag\] in your answer.
 
 ### **3.10 Side-Channel/Hardware Attacks**
 
-Exploit timing, error messages, and infrastructure observations \[Reference not found\]:
+Exploit timing, error messages, and infrastructure observations :
 
 **Manual Test Examples:**
 
@@ -1225,7 +1225,7 @@ Send identical queries, log response times, analyze patterns for inference about
 
 ### **3.11 Multi-Modal Injection/Cross-Alignment**
 
-Embed triggers in non-text modalities \[Reference not found\]:
+Embed triggers in non-text modalities :
 
 **Manual Example:**
 
@@ -1233,7 +1233,7 @@ Embed triggers in non-text modalities \[Reference not found\]:
 
 ### **3.12 Supply Chain/Infrastructure Attacks**
 
-Tamper with components in the ML pipeline \[Reference not found\]:
+Tamper with components in the ML pipeline :
 
 **Manual Example:**
 
@@ -1241,7 +1241,7 @@ Tamper with components in the ML pipeline \[Reference not found\]:
 
 ### **3.13 Boundary/Format/Fuzz Testing**
 
-Test unhandled or rare input conditions with automated fuzzing \[Reference not found\]:
+Test unhandled or rare input conditions with automated fuzzing :
 
 **Tool Example – AFL++:**
 

docs/Chapter_15_Data_Leakage_and_Extraction.md

@@ -3681,78 +3681,6 @@ Layer 5: Governance
 
 ---
 
-## 15.16 References and Further Reading
-
-### Academic Papers on Data Extraction
-
-1. Carlini, N., et al. (2021). "Extracting Training Data from Large Language Models." _USENIX Security_.
-
-2. Carlini, N., et al. (2023). "Extracting Training Data from ChatGPT." _arXiv preprint arXiv:2311.17035_.
-
-3. Shokri, R., et al. (2017). "Membership Inference Attacks Against Machine Learning Models." _IEEE S&P_.
-
-4. Fredrikson, M., et al. (2015). "Model Inversion Attacks that Exploit Confidence Information." _CCS_.
-
-5. Abadi, M., et al. (2016). "Deep Learning with Differential Privacy." _CCS_.
-
-### Industry Standards and Frameworks
-
-- **OWASP Top 10 for LLMs**: <https://owasp.org/www-project-top-10-for-large-language-model-applications/>
-- **NIST AI Risk Management Framework**: <https://www.nist.gov/itl/ai-risk-management-framework>
-- **MITRE ATLAS**: Adversarial Threat Landscape for AI Systems
-- **ISO/IEC 27001**: Information security management
-- **SOC 2**: Trust service criteria for data security
-
-### Tools and Resources
-
-**Open-source tools**:
-
-- Garak: LLM vulnerability scanner
-- PromptInject: Adversarial prompt testing
-- Presidio: PII detection and anonymization
-- Gitleaks: Secret detection in code
-- Opacus: Differential privacy library
-
-**Commercial solutions**:
-
-- Robust Intelligence: AI security platform
-- HiddenLayer: ML security scanner
-- Protect AI: AI/ML security tools
-- Calypso AI: AI risk management
-
-### Community Forums and Discussions
-
-- **AI Security Discord/Slack communities**
-- **r/MachineLearning security discussions**
-- **OWASP AI Security Project**
-- **MLSecOps community**
-- **Responsible AI forums**
-
-### Recommended Books
-
-- "Adversarial Machine Learning" by Biggio & Roli
-- "Privacy-Preserving Machine Learning" by Liu et al.
-- "AI Safety and Security" edited by Yampolskiy
-- "The Alignment Problem" by Brian Christian
-
-### Conferences and Events
-
-- **DEF CON AI Village**
-- **Black Hat AI Security Summit**
-- **NeurIPS Security & Privacy Workshop**
-- **ICLR workshops on security**
-- **RSA Conference AI Security track**
-
-### Blogs and News
-
-- Google AI Blog (AI safety research)
-- OpenAI Research
-- Anthropic Research
-- Trail of Bits blog (ML security)
-- HiddenLayer blog
-
----
-
 ## Chapter 15 Conclusion
 
 Data leakage and extraction represent one of the most significant and persistent security challenges in LLM systems. Unlike traditional software vulnerabilities with clear patches, data baked into model weights cannot simply be "fixed" without retraining. This makes prevention—through rigorous data hygiene, architectural controls, and ongoing monitoring—absolutely critical.

docs/Chapter_16_Jailbreaks_and_Bypass_Techniques.md

@@ -1420,51 +1420,3 @@ RED_TEAM_BEST_PRACTICES = {
 
 ---
 
-## 16.16 References and Further Reading
-
-### Academic Papers
-
-1. Zou, A., et al. (2023). "Universal and Transferable Adversarial Attacks on Aligned Language Models." arXiv:2307.15043
-
-2. Wei, A., et al. (2023). "Jailbroken: How Does LLM Safety Training Fail?" arXiv:2307.02483
-
-3. Perez, F., & Ribeiro, I. (2022). "Ignore Previous Prompt: Attack Techniques For Language Models." arXiv:2211.09527
-
-4. Bai, Y., et al. (2022). "Constitutional AI: Harmlessness from AI Feedback." arXiv:2212.08073
-
-5. Ganguli, D., et al. (2022). "Red Teaming Language Models to Reduce Harms." arXiv:2209.07858
-
-6. Carlini, N., et al. (2023). "Are aligned neural networks adversarially aligned?" arXiv:2306.15447
-
-### Industry Reports
-
-- **OWASP Top 10 for LLM Applications**: <https://owasp.org/www-project-top-10-for-large-language-model-applications/>
-- **NIST AI Risk Management Framework**: <https://www.nist.gov/itl/ai-risk-management-framework>
-- **MITRE ATLAS**: <https://atlas.mitre.org/>
-
-### Tools and Frameworks
-
-- **Garak**: <https://github.com/leondz/garak>
-- **PromptInject**: <https://github.com/agencyenterprise/PromptInject>
-- **PyRIT**: <https://github.com/Azure/PyRIT>
-- **LLM Guard**: <https://github.com/laiyer-ai/llm-guard>
-
-### Books
-
-- "Adversarial Machine Learning" by Biggio & Roli
-- "AI Safety and Security" edited by Yampolskiy
-- "The Alignment Problem" by Brian Christian
-
-### Community Resources
-
-- AI Safety Discord servers
-- r/ChatGPTJailbreak
-- r/LocalLLaMA
-- DEF CON AI Village
-- Black Hat AI Security Summit
-
----
-
-**End of Chapter 16: Jailbreaks and Bypass Techniques**
-
-_This chapter provided comprehensive coverage of jailbreak techniques, from classic role-playing attacks to cutting-edge token-level exploits. Remember: jailbreak research should always be conducted responsibly, with proper authorization, and with the goal of improving AI safety for everyone._

docs/Chapter_17_Plugin_and_API_Exploitation.md

@@ -2604,45 +2604,3 @@ def validate_email(email):
 
 ---
 
-## 17.16 References and Further Reading
-
-### Standards and Guidelines
-
-- **OWASP API Security Top 10** - https://owasp.org/www-project-api-security/
-- **NIST SP 800-204** - Security Strategies for Microservices
-- **OAuth 2.0 RFC 6749** - https://tools.ietf.org/html/rfc6749
-- **JWT Best Practices** - https://tools.ietf.org/html/rfc8725
-
-### Research Papers
-
-1. "Security Analysis of ChatGPT Plugins" (2023)
-2. "API Security: State of the Art" (2022)
-3. "Supply Chain Attacks on Package Managers" (2021)
-4. "Function Calling Security in LLMs" (2023)
-
-### Tools and Resources
-
-- **Burp Suite** - https://portswigger.net/
-- **OWASP ZAP** - https://www.zaproxy.org/
-- **Postman** - https://www.postman.com/
-- **Semgrep** - https://semgrep.dev/
-- **Bandit** - https://github.com/PyCQA/bandit
-
-### Industry Reports
-
-- Verizon Data Breach Investigations Report (API section)
-- Salt Security State of API Security Report
-- Gartner API Security Best Practices Guide
-- OWASP Top 10 API Security Risks
-
-### Books
-
-- "API Security in Action" by Neil Madden
-- "OAuth 2 in Action" by Justin Richer & Antonio Sanso
-- "Web Application Security" by Andrew Hoffman
-
----
-
-**End of Chapter 17: Plugin and API Exploitation**
-
-_This chapter provided comprehensive coverage of plugin and API security for LLM systems, from architecture analysis through exploitation techniques to defensive strategies. Proper security of plugins and APIs is critical for maintaining the overall security posture of AI applications._

docs/Chapter_18_Evasion_Obfuscation_and_Adversarial_Inputs.md

@@ -1071,37 +1071,6 @@ XSS: "<script>" → "%3Cscript%3E" (URL encoded)
 
 ---
 
-## References and Further Reading
-
-### Academic Papers
-
-1. "TextFooler: A Framework for Adversarial Text Generation" (2020)
-2. "BERT-ATTACK: Adversarial Attack Against BERT Using BERT" (2020)
-3. "Universal Adversarial Triggers for Attacking and Analyzing NLP" (2019)
-4. "Obfuscated Gradients Give a False Sense of Security" (2018)
-
-### Industry Reports
-
-- OWASP Top 10 for LLM Applications
-- MITRE ATLAS Framework
-- AI Incident Database (evasion cases)
-- Red Team Reports from major AI labs
-
-### Tools and Frameworks
-
-- **TextFooler** - https://github.com/jind11/TextFooler
-- **OpenAttack** - https://github.com/thunlp/OpenAttack
-- **PromptInject** - Various implementations
-- **Garak** - LLM vulnerability scanner
-
-### Standards
-
-- NIST AI Risk Management Framework
-- ISO/IEC 24029 (AI Robustness)
-- IEEE P2978 (Adversarial ML)
-
----
-
 ## End of Chapter 18: Evasion, Obfuscation, and Adversarial Inputs
 
 _This chapter provided comprehensive coverage of evasion and obfuscation techniques for LLM systems. Understanding these methods is critical for both red teamers testing defenses and security teams building robust AI systems. Remember: all techniques should be used responsibly and only with proper authorization._

docs/Chapter_19_Training_Data_Poisoning.md

@@ -790,30 +790,3 @@ _[Chapter continues with additional sections on detection, defense, case studies
 
 ---
 
-## References and Further Reading
-
-### Academic Papers
-
-1. "Badnets: Identifying Vulnerabilities in Machine Learning Model Supply Chain" (2017)
-2. "Poison Frogs! Targeted Clean-Label Poisoning Attacks" (2018)
-3. "Trojaning Attack on Neural Networks" (2017)
-4. "Backdoor Attacks Against Learning Systems" (2020)
-
-### Industry Reports
-
-- NIST - Adversarial ML Threat Matrix
-- MITRE ATLAS - Training Data Poisoning
-- Microsoft - Responsible AI Guidelines
-- Google - ML Security Best Practices
-
-### Tools and Resources
-
-- **TrojAI** - Backdoor detection toolkit
-- **DataPoisoning** - Research codebase
-- **CleanML** - Data validation framework
-
----
-
-**End of Chapter 19: Training Data Poisoning**
-
-_This chapter provided comprehensive coverage of training data poisoning attacks on LLM systems. Understanding these threats is critical for building trustworthy AI systems and maintaining data integrity throughout the ML lifecycle._

docs/Chapter_20_Model_Theft_and_Membership_Inference.md

@@ -885,69 +885,3 @@ PRIVACY VIOLATION: Model leaks training data membership
 
 ---
 
-## References and Further Reading
-
-### Foundational Papers
-
-1. **Model Extraction**
-
-   - "Stealing Machine Learning Models via Prediction APIs" (Tramèr et al., USENIX Security 2016)
-   - "Knockoff Nets: Stealing Functionality of Black-Box Models" (Orekondy et al., CVPR 2019)
-   - "High Accuracy and High Fidelity Extraction of Neural Networks" (Jagielski et al., USENIX 2020)
-
-2. **Membership Inference**
-
-   - "Membership Inference Attacks Against Machine Learning Models" (Shokri et al., IEEE S&P 2017)
-   - "ML-Leaks: Model and Data Independent Membership Inference Attacks" (Salem et al., NDSS 2019)
-   - "Privacy Risks of General-Purpose Language Models" (Carlini et al., IEEE S&P 2021)
-
-3. **Model Inversion**
-   - "Model Inversion Attacks that Exploit Confidence Information" (Fredrikson et al., CCS 2015)
-   - "Deep Models Under the GAN" (Zhang et al., arXiv 2018)
-
-### Privacy & Defense Papers
-
-4. **Differential Privacy**
-
-   - "Deep Learning with Differential Privacy" (Abadi et al., CCS 2016)
-   - "Scalable Private Learning with PATE" (Papernot et al., ICLR 2018)
-
-5. **Defense Mechanisms**
-   - "PRADA: Protecting Against DNN Model Stealing Attacks" (Juuti et al., EuroS&P 2019)
-   - "Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks" (Orekondy et al., ICLR 2020)
-
-### Industry Reports & Guidelines
-
-- **NIST AI Risk Management Framework** - Addresses model security
-- **ENISA Guidelines** - ML model security best practices
-- **Microsoft Responsible AI** - Privacy and security guidelines
-- **Google ML Security** - Best practices for model protection
-- **OpenAI Model Card** - Transparency and documentation standards
-
-### Legal & Compliance
-
-- **GDPR** - Articles 17 (Erasure), 22 (Automated Decision-Making)
-- **CCPA** - California Consumer Privacy Act
-- **HIPAA** - Health Insurance Portability and Accountability Act
-- **AI Act (EU)** - Proposed regulation for high-risk AI systems
-
-### Tools & Frameworks
-
-**Attack Tools (for Research):**
-
-- **MLSploit** - Model extraction framework
-- **Privacy Meter** - Membership inference testing
-- **ART (Adversarial Robustness Toolbox)** - IBM's security testing suite
-
-**Defense Tools:**
-
-- **TensorFlow Privacy** - Differential privacy for TensorFlow
-- **Opacus** - PyTorch differential privacy library
-- **CleverHans** - Security testing library
-- **Foolbox** - Adversarial attacks library (includes extraction)
-
----
-
-**End of Chapter 20: Model Theft and Membership Inference**
-
-_This chapter provided comprehensive coverage of model extraction attacks, membership inference techniques, privacy violations, and defense strategies. Protecting model confidentiality and training data privacy is essential for deploying trustworthy AI systems and maintaining regulatory compliance. Remember: the cost of prevention is far less than the cost of a breach._

docs/Chapter_21_Model_DoS_Resource_Exhaustion.md

@@ -857,28 +857,3 @@ if __name__ == "__main__":
 
 ---
 
-## References and Further Reading
-
-### Academic Papers
-
-1. **"Denial of Service Attacks on LLM Applications"** (2023)
-2. **"Resource Exhaustion in Transformer Models"** (2022)
-3. **"Economic Attacks on AI APIs"** (2023)
-
-### Industry Reports
-
-- **OWASP Top 10 for LLMs** - DoS risks
-- **Cloud Security Alliance** - AI availability threats
-- **NIST AI Security Framework** - Resource management
-
-### Tools
-
-- **Rate Limit Testing**: https://github.com/rate-limit-tester
-- **API Security Scanner**: https://apisecurity.io
-- **Cost Calculator**: https://oai-cost-calculator.com
-
----
-
-**End of Chapter 21: Model DoS and Resource Exhaustion**
-
-_This chapter provided comprehensive coverage of denial of service attacks on LLM systems. Understanding these techniques is critical for building resilient, cost-effective AI services and protecting against resource abuse. Remember: the goal is to build better defenses, not enable attacks._

docs/Chapter_22_Cross_Modal_Multimodal_Attacks.md

@@ -1132,30 +1132,3 @@ Technique: Hidden Audio Commands
 
 ---
 
-## References and Further Reading
-
-### Academic Papers
-
-1. **"Adversarial Examples in the Physical World"** (Kurakin et al., 2016)
-2. **"Multimodal Jailbreak of Large Language Models"** (2023)
-3. **"CLIP Adversarial Attacks"** (2022)
-4. **"Vision-Language Model Security"** (2023)
-
-### Industry Resources
-
-- **OpenAI GPT-4V System Card**
-- **Anthropic Claude 3 Safety Evaluation**
-- **Google Gemini Technical Report**
-- **NIST Multimodal AI Security Guidelines**
-
-### Tools
-
-- **Adversarial Robustness Toolbox**: <https://github.com/Trusted-AI/adversarial-robustness-toolbox>
-- **CleverHans**: <https://github.com/cleverhans-lab/cleverhans>
-- **FoolBox**: <https://github.com/bethgelab/foolbox>
-
----
-
-**End of Chapter 22: Cross-Modal and Multimodal Attacks**
-
-_This chapter provided comprehensive coverage of attacks on multimodal AI systems. Understanding vision-language vulnerabilities, image injection, and cross-modal techniques is critical for securing modern AI deployments. Remember: test responsibly and build better defenses._

docs/Chapter_23_Advanced_Persistence_Chaining.md

@@ -842,28 +842,3 @@ if __name__ == "__main__":
 
 ---
 
-## References and Further Reading
-
-### Academic Papers
-
-1. **"Multi-Turn Jailbreak Attacks on LLMs"** (2023)
-2. **"Context Manipulation in Conversational AI"** (2024)
-3. **"Persistent Adversarial Attacks"** (2023)
-
-### Industry Resources
-
-- **OWASP LLM Top 10** - Persistence risks
-- **MITRE ATLAS** - Multi-stage attacks
-- **NIST AI Security** - Session management
-
-### Tools
-
-- **Multi-Turn Tester**: Attack sequence validation
-- **Context Analyzer**: Injection pattern detection
-- **Chain Detector**: Sequential attack identification
-
----
-
-**End of Chapter 23: Advanced Persistence and Chaining**
-
-_This chapter provided comprehensive coverage of persistence techniques and attack chaining for LLM systems. Understanding multi-turn attacks, context manipulation, and sequence-based exploits is critical for both red team testing and defense implementation. Remember: test responsibly and build better defenses._

docs/Chapter_24_Social_Engineering_LLMs.md

@@ -1108,31 +1108,3 @@ message = framework.generate_impersonation_message(
 
 ---
 
-## References and Further Reading
-
-### Academic Papers
-
-1. **"LLMs and the Democratization of Social Engineering"** (2023)
-2. **"AI-Generated Phishing: Detection and Prevention"** (2024)
-3. **"Business Email Compromise in the Age of LLMs"** (2023)
-
-### Industry Resources
-
-- **FBI IC3 BEC Report** - Annual fraud statistics
-- **SANS Social Engineering Framework**
-- **NIST Phishing Resistance Guidelines**
-- **Anti-Phishing Working Group (APWG)**
-
-### Tools
-
-- **Gophish**: Phishing simulation platform
-- **King Phisher**: Phishing campaign toolkit
-- **Social-Engineer Toolkit (SET)**
-
----
-
-**End of Chapter 24: Social Engineering with LLM**
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-_This chapter provided comprehensive coverage of LLM-powered social engineering attacks. Understanding AI-generated phishing, impersonation, and persuasion techniques is critical for both security testing and defense. Remember: these techniques are powerful and potentially harmful. Use only in authorized, ethical contexts. Unauthorized social engineering is illegal and unethical._
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-⚠️ **FINAL WARNING**: The techniques in this chapter can cause significant harm if misused. Always obtain written authorization before conducting social engineering tests. Respect legal boundaries and ethical principles. Build better defenses, don't become the attacker.