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所在平台: Udemy |
课程主页: https://www.udemy.com/course/owasp-top-10-for-llms/
课程评论：没有评论
课程名称：大型语言模型的OWASP前十名 课程概述：由Christopher Nett设计的《大型语言模型的OWASP前十名》是一个精心组织的Coursera课程，旨在帮助IT专业人士掌握大型语言模型（LLMs）的OWASP前十名，以构建、保护和利用这些模型。该课程系统地引导学习者从基础知识到高级概念，使学员能够深入理解OWASP前十名在安全领域的重要性。通过学习此课程，您将获得攻击和保护大型语言模型的专业知识，这是当前网络安全领域广泛认可的复杂主题。 课程关键收益： - OWASP基础：探讨开放网页应用安全项目的基础原则。 - LLMs基础：理解大型语言模型的核心架构、功能及相关风险。 - 提示注入：学习对手如何通过恶意输入操纵AI模型，以及保护提示完整性的缓解策略。 - 敏感信息泄露：了解在AI交互中意外数据暴露的风险，以及如何防止机密信息泄露。 - 供应链安全：探讨与AI供应链相关的安全问题，包括对外部数据源、模型和第三方集成的依赖。 - 数据和模型中毒：深入研究数据和模型中毒攻击的风险，对手如何操纵训练数据以影响AI行为。 - 不当输出处理：学习如何不当处理AI生成的响应可能导致的安全漏洞、错误信息或政策违反。 - 过度自主性：理解AI系统在超出其预期范围和控制时可能采取的意外自主行动的危险。 - 系统提示泄露：探讨攻击者如何提取系统提示和指令，暴露内部逻辑和安全漏洞。 - 向量和嵌入弱点：识别向量数据库和嵌入中的漏洞，这些漏洞可能被对手利用来操纵AI的输出。 - 错误信息：分析AI模型如何生成或放大错误信息，并制定提高内容准确性和可靠性的策略。 - 无限制消费：理解AI应用程序中过度资源消耗的风险，以及如何实施防范滥用的保护措施。 本课程深入探讨与AI及大型语言模型相关的主要安全风险和漏洞。通过研究现实世界的攻击技术和缓解策略，您将学习如何保护AI应用程序，防止对抗性操纵，并确保负责任的AI部署。
OWASP Top 10 for LLMs by Christopher Nett is a meticulously organized Udemy course designed for IT professionals aiming to master the OWASP Top 10 for LLMs to build, protect and exploit Large Language Models. This course systematically guides you from the basis to advanced concepts of the OWASP Top 10 for LLMs.By mastering the OWASP Top 10 for LLMs, you're developing expertise in essential topics in today's cybersecurity landscape. Through this course, you'll develop expertise in attacking and securing LLMs, a comprehensive and complex topic widely recognized in the industry.This deep dive into the OWASP Top 10 for LLMs equips you with the skills necessary for a cutting-edge career in cybersecurity.Key Benefits for you:OWASP Basics: Explore the foundational principles of the Open Web Application Security Project.LLMs Basics: Understand the core architecture, functionality, and risks associated with Large Language Models.Prompt Injection: Learn how adversaries manipulate AI models through malicious inputs and explore mitigation strategies to safeguard prompt integrity.Sensitive Information Disclosure: Understand the risks of unintended data exposure in AI interactions and how to prevent the leakage of confidential information.Supply Chain: Explore security concerns related to AI supply chains, including dependencies on external data sources, models, and third-party integrations.Data and Model Poisoning: Dive into the risks of data and model poisoning attacks, where adversaries manipulate training data to influence AI behavior.Improper Output Handling: Learn how mishandling AI-generated responses can lead to security vulnerabilities, misinformation, or policy violations.Excessive Agency: Understand the dangers of AI systems taking unintended autonomous actions beyond their intended scope and control.System Prompt Leakage: Explore how attackers can extract system prompts and instructions, exposing internal logic and security vulnerabilities.Vector and Embedding Weaknesses: Identify vulnerabilities in vector databases and embeddings that adversaries can exploit to manipulate AI outputs.Misinformation: Analyze how AI models can generate or amplify misinformation and develop strategies to enhance content accuracy and reliability.Unbound Consumption: Understand the risks of excessive resource consumption in AI applications and how to implement safeguards against abuse.This course provides a deep dive into key security risks and vulnerabilities associated with AI and large language models (LLMs). By exploring real-world attack techniques and mitigation strategies, you will learn how to secure AI applications, prevent adversarial manipulation, and ensure responsible AI deployment.
