{"product_id":"9798868829574","title":"Jailbreaking LLMs: Protecting the Future of Enterprise Security","description":"\u003ch1\u003eJailbreaking LLMs: Protecting the Future of Enterprise Security\u003c\/h1\u003e \u003ch2\u003eNeelakrishnan, Priyanka\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp class=\"MsoNormal\" style=\"margin-bottom: 8.0pt; line-height: 116%;\"\u003eAs Large Language Models (LLMs) become deeply integrated into enterprise applications, customer support systems, internal workflows, and decision-making platforms, they also introduce a rapidly expanding attack surface. \u003cem\u003eJailbreaking LLMs \u003c\/em\u003eexplores how modern AI systems can be manipulated through prompt injections, adversarial attacks, context manipulation, data poisoning, and jailbreak techniques — and why organizations must treat these threats as critical security risks rather than theoretical concerns. With two-thirds of enterprises now deploying generative AI systems in production, the stakes have never been higher.\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"margin-bottom: 11.0pt; line-height: 116%;\"\u003eThrough real-world examples, practical frameworks, and enterprise-focused security strategies, this book equips readers to design, secure, monitor, and defend LLM-powered systems at scale. Readers will learn to identify vulnerabilities, implement secure AI architectures, conduct red-teaming exercises, establish governance controls, and build resilient AI environments that align innovation with security, compliance, and responsible AI practices.\u003c\/p\u003e\n\u003ch1 style=\"margin: 6.0pt 0in 4.0pt 0in;\"\u003eWhat you will learn\u003c\/h1\u003e\n\u003cp class=\"MsoListParagraph\" style=\"text-indent: -14.0pt; line-height: 108%; mso-list: l0 level1 lfo1; margin: 0in 0in 2.0pt 21.0pt;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"color: #8b1f2f; mso-bidi-font-weight: bold;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e▪\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e      \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003eUnderstand the risks and mechanics of LLM jailbreaking — prompt injection, adversarial inputs, data poisoning, and context manipulation\u003c\/p\u003e\n\u003cp class=\"MsoListParagraph\" style=\"text-indent: -14.0pt; line-height: 108%; mso-list: l0 level1 lfo1; margin: 0in 0in 2.0pt 21.0pt;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"color: #8b1f2f; mso-bidi-font-weight: bold;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e▪\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e      \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003eIdentify and mitigate prompt injection and adversarial attacks\u003c\/p\u003e\n\u003cp class=\"MsoListParagraph\" style=\"text-indent: -14.0pt; line-height: 108%; mso-list: l0 level1 lfo1; margin: 0in 0in 2.0pt 21.0pt;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"color: #8b1f2f; mso-bidi-font-weight: bold;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e▪\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e      \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003eDesign secure and enterprise-ready LLM architectures\u003c\/p\u003e\n\u003cp class=\"MsoListParagraph\" style=\"text-indent: -14.0pt; line-height: 108%; mso-list: l0 level1 lfo1; margin: 0in 0in 2.0pt 21.0pt;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"color: #8b1f2f; mso-bidi-font-weight: bold;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e▪\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e      \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003eBuild monitoring, detection, and AI security response workflows\u003c\/p\u003e\n\u003cp class=\"MsoListParagraph\" style=\"text-indent: -14.0pt; line-height: 108%; mso-list: l0 level1 lfo1; margin: 0in 0in 2.0pt 21.0pt;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"color: #8b1f2f; mso-bidi-font-weight: bold;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e▪\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e      \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003eApply red-teaming and defensive testing strategies for LLM systems\u003c\/p\u003e\n\u003cp class=\"MsoListParagraph\" style=\"text-indent: -14.0pt; line-height: 108%; mso-list: l0 level1 lfo1; margin: 0in 0in 2.0pt 21.0pt;\"\u003e\u003c!-- [if !supportLists]--\u003e\u003cspan style=\"color: #8b1f2f; mso-bidi-font-weight: bold;\"\u003e\u003cspan style=\"mso-list: Ignore;\"\u003e▪\u003cspan style=\"font: 7.0pt 'Times New Roman';\"\u003e      \u003c\/span\u003e\u003c\/span\u003e\u003c\/span\u003e\u003c!--[endif]--\u003eEmbed ethical AI governance and regulatory considerations into deployment models\u003c\/p\u003e\n\u003ch1 style=\"margin: 10.0pt 0in 4.0pt 0in;\"\u003eWho this book is for\u003c\/h1\u003e\n\u003cp class=\"MsoNormal\" style=\"margin-bottom: 10.0pt; line-height: 116%;\"\u003eThis book is for cybersecurity professionals, AI\/ML engineers, enterprise architects, IT leaders, and security-conscious executives responsible for designing, deploying, or securing systems powered by Large Language Models. It is also valuable for security analysts, incident responders, and platform teams seeking practical guidance for anticipating, detecting, and mitigating AI-related threats in enterprise environments.\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Apress\u003c\/p\u003e \u003cp\u003ePublication Date: 2026-12-14\u003c\/p\u003e \u003cp\u003eFormat: Paperback\u003c\/p\u003e \u003cp\u003eISBN-13: 9798868829574\u003c\/p\u003e \u003cp\u003eDOI: \u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: \u003c\/p\u003e ","brand":"Apress","offers":[{"title":"Default Title","offer_id":50185185296524,"sku":"9798868829574","price":53.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/lateknightbooks.com\/products\/9798868829574","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}