API Security for AI Applications: Practical Defense Strategies for LLMs, Prompt Injection, and Data Leakage
Sankar Srinivasan
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API Security for AI Applications: Practical Defense Strategies for LLMs, Prompt Injection, and Data Leakage
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API Security for AI Applications 4
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Introduction 4
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Chapter 1 : The AI Security Wake-Up Call 11
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Chapter 2 : How AI APIs Actually Work And Where They Break 27
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Chapter 3: Threat Modeling for AI Applications 52
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Chapter 4 : Prompt Injection Attacks 79
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Chapter 5 : Data Leakage: The Silent Killer 112
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Chapter 6: Tool Misuse and Agent Exploits 139
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Chapter 7 : Authentication and Authorization for AI APIs 165
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Chapter 8: Securing API Gateways for AI Systems 191
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Chapter 9: Input Output Filtering and Validation for LLMs 214
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Chapter 10: Rate Limiting, Abuse Prevention and Cost Attacks 253
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Chapter 11: Secure Deployment with Docker and CI CD 278
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Chapter 12: Logging, Monitoring and Incident Response 302
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Chapter 13: Red Teaming AI Systems — Breaking Your Own Product 329
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Chapter 14: Security Checklists and Production Playbooks 355
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Chapter 15: The Future of AI Security 377
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