LLM Attack Surface Defense: Protecting AI Applications from Adversarial Exploits and Advanced Threats

Your LLM application passed every standard security review - and the attacker is already in the retrieval pipeline. Most AI security guides tell you what the risks are. This book tells you exactly how to close them, layer by layer, in the production systems you are actually building today. LLM Attack Surface Defense delivers the CIPHER Framework - a six-phase methodology covering Classify, Isolate, Probe, Harden, Examine, and Respond - applied to the full attack surface of real LLM applications: direct and indirect prompt injection, RAG data poisoning, output leakage, agentic privilege escalation, supply chain compromise, and AI governance. Each chapter uses documented incidents from 2023 to 2026, including the Samsung source code leak, the Air Canada chatbot liability ruling, the Microsoft Copilot email exfiltration attack, and the March 2026 TeamPCP supply chain compromise, to show exactly how defenses fail and what would have stopped them. What you will learn: - Map the complete attack surface of any LLM-powered application in under two hours - Design architectural instruction-to-data plane separation that stops prompt injection where filters fail - Harden RAG pipelines against retrieval-time poisoning using provenance tracking and sentinel monitoring - Classify and scope every trust boundary in agentic multi-agent architectures - Implement runtime behavioral anomaly detection with a calibrated baseline - Audit third-party LLM integrations for model provenance, API security, and vendor risk - Build a red team workflow that finds vulnerabilities specific to your system, not generic ones - Prevent PII, credential, and system prompt leakage through layered output guardrails - Execute the six-step 48-hour LLM incident response playbook >The CIPHER Framework is structured to be applied incrementally - classify first, isolate the highest-risk boundaries, then probe and harden. Each phase produces a deliverable your security team, legal team, and board can evaluate. No specialized tooling required for the core methodology. For security engineers, DevSecOps practitioners, and AI developers who need a systematic, tested playbook - not vendor whitepapers or academic abstracts. If your team is shipping LLM-powered applications, the attack surface is already there. This book shows you how to defend it.

Pages: 141, Paperback, Independently published