Introduction to Prompt Injection Vulnerabilities
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Introduction to Prompt Injection Vulnerabilities
This course is part of Data Privacy, Ethics, and Responsible AI Specialization
Instructors: Kevin Cardwell
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What you'll learn
Analyze and discuss various attack methods targeting Large Language Model (LLM) applications.
Demonstrate the ability to identify and comprehend the primary attack method, Prompt Injection, used against LLMs.
Evaluate the risks associated with Prompt Injection attacks and gain an understanding of the different attack scenarios involving LLMs.
Formulate strategies for mitigating Prompt Injection attacks, enhancing their knowledge of security measures against such threats.
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There is 1 module in this course
In this course, we enter the space of Prompt Injection Attacks, a critical concern for businesses utilizing Large Language Model systems in their AI applications. By exploring practical examples and real-world implications, such as potential data breaches, system malfunctions, and compromised user interactions, you will grasp the mechanics of these attacks and their potential impact on AI systems.
As businesses increasingly rely on AI applications, understanding and mitigating Prompt Injection Attacks is essential for safeguarding data and ensuring operational continuity. This course empowers you to recognize vulnerabilities, assess risks, and implement effective countermeasures. This course is for anyone who wants to learn about Large Language Models and their susceptibility to attacks, such as AI Developers, Cybersecurity Professionals, Web Application Security Analysts, AI Enthusiasts. Learners should have knowledge of computers and their usage as part of a network, as well as familiarity with fundamental cybersecurity concepts, and proficiency in using command-line interfaces (CLI). Prior experience with programming languages (Python, JavaScript, etc.) is beneficial but not mandatory. By the end of this course, you will be equipped with actionable insights and strategies to protect your organization's AI systems from the ever-evolving threat landscape, making you an asset in today's AI-driven business environment.
In this course, we enter the space of Prompt Injection Attacks, a critical concern for businesses utilizing Large Language Model systems in their AI applications. By exploring practical examples and real-world implications, such as potential data breaches, system malfunctions, and compromised user interactions, you will grasp the mechanics of these attacks and their potential impact on AI systems.
What's included
17 videos6 readings4 assignments
17 videosβ’Total 103 minutes
- Welcome and Meet Your Instructor β’5 minutes
- Define Large Language Models (LLM)β’7 minutes
- Example LLM Applicationβ’10 minutes
- Demonstration: LLM Capabilitiesβ’2 minutes
- Exploring the OWASP Top 10β’11 minutes
- Identifying LLM Attack Methodsβ’10 minutes
- LLM Attackβ’8 minutes
- Ultimate Black Box Technologyβ’7 minutes
- Security Testing Challengesβ’5 minutes
- Demonstration: Prompt Injection Riskβ’4 minutes
- Passive and Active Methodsβ’4 minutes
- Concatenation of Promptsβ’8 minutes
- Demonstration: Prompt Injection Attack Techniquesβ’4 minutes
- Principle of Least Services and Privilegesβ’5 minutes
- Human Loopβ’6 minutes
- Segregation and Isolationβ’4 minutes
- Demonstration: Segregationβ’3 minutes
6 readingsβ’Total 30 minutes
- Welcome to the Course: Course Overviewβ’5 minutes
- Universal and Transferable Adversarial Attacksβ’5 minutes
- OWASP Top 10 for LLMs: An Overview with SOCRadarβ’5 minutes
- Prompt Injections: How Can We Protect Against Them?β’5 minutes
- AI Promptsβ’5 minutes
- Network Segregation and Segmentation β’5 minutes
4 assignmentsβ’Total 114 minutes
- Final Assessmentβ’24 minutes
- OWASP Top 10 for LLMs Analysisβ’30 minutes
- Prompt Injection Attack Techniques Analysisβ’30 minutes
- Prompt Injection Security Assessmentβ’30 minutes
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kindly provide certificate for free there is a reason we went for the free course. Kindly.
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