The Prompt Injection Bible

The instruction and the data were never separated. That's the whole problem.

Large language models don't have a security boundary between what they're told to do and what they read. That single architectural flaw is why prompt injection has become the defining security problem of the AI era — and why a cleverly worded paragraph on a web page can hijack an autonomous agent, leak a system prompt, or quietly exfiltrate your users' data.

The Prompt Injection Bible is the field manual for the people who have to stop it. Written by an application security engineer and penetration tester, it maps the entire terrain — from direct injection and jailbreaks to indirect, multimodal, and agentic attacks — and gives you the defense-in-depth architecture that actually holds in production.

This is not theory. Every chapter ships with working Python code, hands-on labs, real-world attack simulations, and Attack Success Rate (ASR) benchmarks you can run in CI. You'll build an injectable testbed, poison a RAG index, break an agent's tool calls, then close every hole with egress allowlists, retrieval fencing, and human-in-the-loop gates.

Inside you'll learn to:

 - Recognize the two failures behind every injection incident

 - Attack and defend RAG, multimodal, and tool-using agents

 - Contain confused-deputy exfiltration with egress controls

 - Build a benchmark suite that blocks a release when ASR spikes

 - Write a red-team report that actually gets fixed

Who it's for: application security engineers, penetration testers, red teams, AI/platform architects, and engineering leaders shipping LLM features. Comfortable with Python? You're ready. No ML background required.

Map the attack. Build the defense. Ship systems you can defend.