Prompt Injection Defense with Python : Protect LLM Apps, RAG Pipelines, AI Agents, and Tool-Calling Workflows from Real-World Prompt Attacks
Language: English
Published by Independently Published Mai 2026, 2026
Series: Book 4 of 4 - AI & Law Series
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 73.44
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Add to basketItem description from seller
Seller Inventory # 9798196154553
- Title
- Prompt Injection Defense with Python : Protect LLM Apps, RAG Pipelines, AI Agents, and Tool-Calling Workflows from Real-World Prompt Attacks
- Author
- Leandro Calado
- Publisher
- Independently Published Mai 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798196154553
- Item weight
- 429 grams
- Dimensions
- 229x152x17 mm
- Series
- Book 4 of 4: AI & Law Series
Most developers are building LLM apps, RAG pipelines, and AI agents without a real security layer. This book shows you how to fix that with practical Python projects you can build, test, deploy, and turn into paid services.
Prompt Injection Defense with Python is a hands-on guide for developers, AI engineers, freelancers, and technical founders who want to secure modern LLM applications using Python 3.11, FastAPI, ChromaDB, SQLite, and Docker.
Inside, you will build practical AI security projects such as:
- Prompt Firewall API — a FastAPI middleware that detects risky prompts, blocks malicious inputs, and assigns threat scores.
- Secure RAG Pipeline — document ingestion with malicious content detection, retrieval validation, and safer context handling.
- Agent Approval Gateway — a permission and audit system for AI agents that call sensitive tools and APIs.
- Prompt Injection Scanner — a testing library with attack payloads and JSON vulnerability reports.
- Security Dashboard — a lightweight monitoring system for attacks, logs, metrics, and incidents using SQLite.
You will also learn how to package your skills into paid AI security services, including LLM app audits, RAG security reviews, agent risk assessments, and subscription-based security tooling.
If you want to build safer AI applications and turn LLM security into a practical business opportunity, this book gives you the projects, architecture, and code patterns to start.
"Synopsis" may belong to another edition of this title.
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