ISBN: 9798868829673 - breaking the model context protocol: agentic attacks and defenses for mcp-powered ai systems (12 resultados)

Condición: New.

Paperback. Condición: New. As AI agents plug into more tools and internal systems, the Model Context Protocol (MCP) is becoming a core part of how modern platforms work. With this shift comes a fast-growing challenge: understanding the new attack surfaces created when probabilistic models interact with real APIs, data, and networks. This book gives practitioners a clear, practical guide to navigating that emerging threat landscape by showing how MCP architectures behave in production and where hidden risks often appear.The book begins by mapping today's MCP trust boundaries and explaining why traditional security assumptions don't hold when the "client" is an LLM. You'll explore real attack stories and hands-on labs demonstrating tool-poisoning techniques, signature cloaking, and sampling-based abuses. You'll then learn how attackers target the surrounding environment through DNS rebinding, malicious MCP servers, and confused-deputy patterns that turn over-permissioned tools into high-impact attack paths.From there, the book provides defensive approaches built on schemas, contracts, monitoring, least privilege, and continuous red-team testing. Each chapter helps you apply the ideas to real deployments. Drawing on active MCP security research and real-world agent testing, this book offers a focused roadmap for securing the next generation of AI systems.What You Will LearnUnderstand how MCP architectures function in real AI agent systemsIdentify trust boundaries and map emerging attack surfacesUse sampling-based and elicitation-based techniques to assess model behaviorProtect MCP environments from DNS rebinding and confused-deputy risksWho This Book is ForThis book is for security engineers, AI platform teams, red-teamers, DevSecOps practitioners, MCP implementers, agent-framework developers, and technical leaders responsible for securing AI-driven systems and LLM-powered applications.…

Paperback. Condición: New. As AI agents plug into more tools and internal systems, the Model Context Protocol (MCP) is becoming a core part of how modern platforms work. With this shift comes a fast-growing challenge: understanding the new attack surfaces created when probabilistic models interact with real APIs, data, and networks. This book gives practitioners a clear, practical guide to navigating that emerging threat landscape by showing how MCP architectures behave in production and where hidden risks often appear.The book begins by mapping today's MCP trust boundaries and explaining why traditional security assumptions don't hold when the "client" is an LLM. You'll explore real attack stories and hands-on labs demonstrating tool-poisoning techniques, signature cloaking, and sampling-based abuses. You'll then learn how attackers target the surrounding environment through DNS rebinding, malicious MCP servers, and confused-deputy patterns that turn over-permissioned tools into high-impact attack paths.From there, the book provides defensive approaches built on schemas, contracts, monitoring, least privilege, and continuous red-team testing. Each chapter helps you apply the ideas to real deployments. Drawing on active MCP security research and real-world agent testing, this book offers a focused roadmap for securing the next generation of AI systems.What You Will LearnUnderstand how MCP architectures function in real AI agent systemsIdentify trust boundaries and map emerging attack surfacesUse sampling-based and elicitation-based techniques to assess model behaviorProtect MCP environments from DNS rebinding and confused-deputy risksWho This Book is ForThis book is for security engineers, AI platform teams, red-teamers, DevSecOps practitioners, MCP implementers, agent-framework developers, and technical leaders responsible for securing AI-driven systems and LLM-powered applications.…

Paperback. Condición: new. Paperback. As AI agents plug into more tools and internal systems, the Model Context Protocol (MCP) is becoming a core part of how modern platforms work. With this shift comes a fastgrowing challenge: understanding the new attack surfaces created when probabilistic models interact with real APIs, data, and networks. This book gives practitioners a clear, practical guide to navigating that emerging threat landscape by showing how MCP architectures behave in production and where hidden risks often appear.The book begins by mapping todays MCP trust boundaries and explaining why traditional security assumptions dont hold when the client is an LLM. Youll explore real attack stories and handson labs demonstrating toolpoisoning techniques, signature cloaking, and samplingbased abuses. Youll then learn how attackers target the surrounding environment through DNS rebinding, malicious MCP servers, and confuseddeputy patterns that turn overpermissioned tools into highimpact attack paths.From there, the book provides defensive approaches built on schemas, contracts, monitoring, least privilege, and continuous redteam testing. Each chapter helps you apply the ideas to real deployments. Drawing on active MCP security research and realworld agent testing, this book offers a focused roadmap for securing the next generation of AI systems.What You Will LearnUnderstand how MCP architectures function in real AI agent systemsIdentify trust boundaries and map emerging attack surfacesUse samplingbased and elicitationbased techniques to assess model behaviorProtect MCP environments from DNS rebinding and confuseddeputy risksWho This Book is ForThis book is for security engineers, AI platform teams, redteamers, DevSecOps practitioners, MCP implementers, agentframework developers, and technical leaders responsible for securing AIdriven systems and LLMpowered applications. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -As AI agents plug into more tools and internal systems, the Model Context Protocol (MCP) is becoming a core part of how modern platforms work. With this shift comes a fast growing challenge: understanding the new attack surfaces created when probabilistic models interact with real APIs, data, and networks. This book gives practitioners a clear, practical guide to navigating that emerging threat landscape by showing how MCP architectures behave in production and where hidden risks often appear.The book begins by mapping today s MCP trust boundaries and explaining why traditional security assumptions don t hold when the client is an LLM. You ll explore real attack stories and hands on labs demonstrating tool poisoning techniques, signature cloaking, and sampling based abuses. You ll then learn how attackers target the surrounding environment through DNS rebinding, malicious MCP servers, and confused deputy patterns that turn over permissioned tools into high impact attack paths.From there, the book provides defensive approaches built on schemas, contracts, monitoring, least privilege, and continuous red team testing. Each chapter helps you apply the ideas to real deployments.Drawing on active MCP security research and real world agent testing, this book offers a focused roadmap for securing the next generation of AI systems.What You Will LearnUnderstand how MCP architectures function in real AI agent systemsIdentify trust boundaries and map emerging attack surfacesUse sampling based and elicitation based techniques to assess model behaviorProtect MCP environments from DNS rebinding and confused deputy risksWho This Book is ForThis book is for security engineers, AI platform teams, red teamers, DevSecOps practitioners, MCP implementers, agent framework developers, and technical leaders responsible for securing AI driven systems and LLM powered applications. 277 pp. Englisch. …

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As AI agents plug into more tools and internal systems, the Model Context Protocol (MCP) is becoming a core part of how modern platforms work. With this shift comes a fast growing challenge: understanding the new attack surfaces created when probabilistic models interact with real APIs, data, and networks. This book gives practitioners a clear, practical guide to navigating that emerging threat landscape by showing how MCP architectures behave in production and where hidden risks often appear.The book begins by mapping today s MCP trust boundaries and explaining why traditional security assumptions don t hold when the client is an LLM. You ll explore real attack stories and hands on labs demonstrating tool poisoning techniques, signature cloaking, and sampling based abuses. You ll then learn how attackers target the surrounding environment through DNS rebinding, malicious MCP servers, and confused deputy patterns that turn over permissioned tools into high impact attack paths.From there, the book provides defensive approaches built on schemas, contracts, monitoring, least privilege, and continuous red team testing. Each chapter helps you apply the ideas to real deployments.Drawing on active MCP security research and real world agent testing, this book offers a focused roadmap for securing the next generation of AI systems.What You Will LearnUnderstand how MCP architectures function in real AI agent systemsIdentify trust boundaries and map emerging attack surfacesUse sampling based and elicitation based techniques to assess model behaviorProtect MCP environments from DNS rebinding and confused deputy risksWho This Book is ForThis book is for security engineers, AI platform teams, red teamers, DevSecOps practitioners, MCP implementers, agent framework developers, and technical leaders responsible for securing AI driven systems and LLM powered applications.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Paperback. Condición: new. Paperback. As AI agents plug into more tools and internal systems, the Model Context Protocol (MCP) is becoming a core part of how modern platforms work. With this shift comes a fastgrowing challenge: understanding the new attack surfaces created when probabilistic models interact with real APIs, data, and networks. This book gives practitioners a clear, practical guide to navigating that emerging threat landscape by showing how MCP architectures behave in production and where hidden risks often appear.The book begins by mapping todays MCP trust boundaries and explaining why traditional security assumptions dont hold when the client is an LLM. Youll explore real attack stories and handson labs demonstrating toolpoisoning techniques, signature cloaking, and samplingbased abuses. Youll then learn how attackers target the surrounding environment through DNS rebinding, malicious MCP servers, and confuseddeputy patterns that turn overpermissioned tools into highimpact attack paths.From there, the book provides defensive approaches built on schemas, contracts, monitoring, least privilege, and continuous redteam testing. Each chapter helps you apply the ideas to real deployments. Drawing on active MCP security research and realworld agent testing, this book offers a focused roadmap for securing the next generation of AI systems.What You Will LearnUnderstand how MCP architectures function in real AI agent systemsIdentify trust boundaries and map emerging attack surfacesUse samplingbased and elicitationbased techniques to assess model behaviorProtect MCP environments from DNS rebinding and confuseddeputy risksWho This Book is ForThis book is for security engineers, AI platform teams, redteamers, DevSecOps practitioners, MCP implementers, agentframework developers, and technical leaders responsible for securing AIdriven systems and LLMpowered applications. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -As AI agents plug into more tools and internal systems, the Model Context Protocol (MCP) is becoming a core part of how modern platforms work. With this shift comes a fast¿growing challenge: understanding the new attack surfaces created when probabilistic models interact with real APIs, data, and networks. This book gives practitioners a clear, practical guide to navigating that emerging threat landscape by showing how MCP architectures behave in production and where hidden risks often appear.The book begins by mapping today's MCP trust boundaries and explaining why traditional security assumptions don't hold when the 'client' is an LLM. You'll explore real attack stories and hands¿on labs demonstrating tool¿poisoning techniques, signature cloaking, and sampling¿based abuses. You'll then learn how attackers target the surrounding environment through DNS rebinding, malicious MCP servers, and confused¿deputy patterns that turn over¿permissioned tools into high¿impact attack paths.From there, the book provides defensive approaches built on schemas, contracts, monitoring, least privilege, and continuous red¿team testing. Each chapter helps you apply the ideas to real deployments. Drawing on active MCP security research and real¿world agent testing, this book offers a focused roadmap for securing the next generation of AI systems.What You Will LearnUnderstand how MCP architectures function in real AI agent systemsIdentify trust boundaries and map emerging attack surfacesUse sampling¿based and elicitation¿based techniques to assess model behaviorProtect MCP environments from DNS rebinding and confused¿deputy risksWho This Book is ForThis book is for security engineers, AI platform teams, red¿teamers, DevSecOps practitioners, MCP implementers, agent¿framework developers, and technical leaders responsible for securing AI¿driven systems and LLM¿powered applications.Springer Nature Customer Service Center GmbH, Europaplatz 3,69115 Heidelberg, Germany, 69115 Heidelberg 304 pp. Englisch.…

Paperback. Condición: new. Paperback. As AI agents plug into more tools and internal systems, the Model Context Protocol (MCP) is becoming a core part of how modern platforms work. With this shift comes a fastgrowing challenge: understanding the new attack surfaces created when probabilistic models interact with real APIs, data, and networks. This book gives practitioners a clear, practical guide to navigating that emerging threat landscape by showing how MCP architectures behave in production and where hidden risks often appear.The book begins by mapping todays MCP trust boundaries and explaining why traditional security assumptions dont hold when the client is an LLM. Youll explore real attack stories and handson labs demonstrating toolpoisoning techniques, signature cloaking, and samplingbased abuses. Youll then learn how attackers target the surrounding environment through DNS rebinding, malicious MCP servers, and confuseddeputy patterns that turn overpermissioned tools into highimpact attack paths.From there, the book provides defensive approaches built on schemas, contracts, monitoring, least privilege, and continuous redteam testing. Each chapter helps you apply the ideas to real deployments. Drawing on active MCP security research and realworld agent testing, this book offers a focused roadmap for securing the next generation of AI systems.What You Will LearnUnderstand how MCP architectures function in real AI agent systemsIdentify trust boundaries and map emerging attack surfacesUse samplingbased and elicitationbased techniques to assess model behaviorProtect MCP environments from DNS rebinding and confuseddeputy risksWho This Book is ForThis book is for security engineers, AI platform teams, redteamers, DevSecOps practitioners, MCP implementers, agentframework developers, and technical leaders responsible for securing AIdriven systems and LLMpowered applications. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…