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商品描述
Large language models (LLMs) are not just shaping the trajectory of AI, they're also unveiling a new era of security challenges. This practical book takes you straight to the heart of these threats. Author Steve Wilson, chief product officer at Exabeam, focuses exclusively on LLMs, eschewing generalized AI security to delve into the unique characteristics and vulnerabilities inherent in these models.
Complete with collective wisdom gained from the creation of the OWASP Top 10 for LLMs list--a feat accomplished by more than 400 industry experts--this guide delivers real-world guidance and practical strategies to help developers and security teams grapple with the realities of LLM applications. Whether you're architecting a new application or adding AI features to an existing one, this book is your go-to resource for mastering the security landscape of the next frontier in AI.
You'll learn:
- Why LLMs present unique security challenges
- How to navigate the many risk conditions associated with using LLM technology
- The threat landscape pertaining to LLMs and the critical trust boundaries that must be maintained
- How to identify the top risks and vulnerabilities associated with LLMs
- Methods for deploying defenses to protect against attacks on top vulnerabilities
- Ways to actively manage critical trust boundaries on your systems to ensure secure execution and risk minimization
商品描述(中文翻譯)
大型語言模型(LLMs)不僅在塑造人工智慧的發展軌跡,還揭示了一個新的安全挑戰時代。本書將帶您深入這些威脅的核心。作者 Steve Wilson,Exabeam 的首席產品官,專注於 LLMs,避免泛泛而談的人工智慧安全問題,深入探討這些模型固有的獨特特徵和脆弱性。
本書結合了來自超過 400 位業界專家的智慧,創建了 LLMs 的 OWASP Top 10 名單,提供了實際的指導和策略,幫助開發人員和安全團隊應對 LLM 應用的現實挑戰。無論您是在設計一個新應用程式,還是為現有應用程式添加人工智慧功能,本書都是您掌握人工智慧下一個前沿安全環境的最佳資源。
您將學到:
- 為什麼 LLMs 會帶來獨特的安全挑戰
- 如何應對使用 LLM 技術所涉及的多種風險條件
- 與 LLMs 相關的威脅環境及必須維持的關鍵信任邊界
- 如何識別與 LLMs 相關的主要風險和脆弱性
- 部署防禦措施以保護免受對主要脆弱性的攻擊的方法
- 如何主動管理系統上的關鍵信任邊界,以確保安全執行和風險最小化