As large language models (LLMs) revolutionize the AI landscape, it is crucial to understand and address the unique security challenges they present. This comprehensive course is designed to equip you with the knowledge and skills needed to identify, mitigate, and prevent vulnerabilities in your LLM applications. Through a series of in-depth lessons, you will:
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As large language models (LLMs) revolutionize the AI landscape, it is crucial to understand and address the unique security challenges they present. This comprehensive course is designed to equip you with the knowledge and skills needed to identify, mitigate, and prevent vulnerabilities in your LLM applications. Through a series of in-depth lessons, you will:
As large language models (LLMs) revolutionize the AI landscape, it is crucial to understand and address the unique security challenges they present. This comprehensive course is designed to equip you with the knowledge and skills needed to identify, mitigate, and prevent vulnerabilities in your LLM applications. Through a series of in-depth lessons, you will:
- Explore common security threats, such as model theft, prompt injection, and sensitive information disclosure
- Learn techniques to prevent attackers from exploiting vulnerabilities and compromising your AI systems
- Discover best practices for secure plugin design, input validation, and sanitization
- Understand the importance of actively monitoring dependencies for security updates and vulnerabilities
- Gain insights into effective strategies for protecting against unauthorized access and data breaches
Whether you are a developer, data scientist, or AI enthusiast, this course will provide you with the essential tools to ensure the integrity and safety of your LLM applications. By the end of the course, you will be well-versed in the latest security measures and be able to confidently deploy robust, secure AI solutions.
Don't let vulnerabilities undermine the potential of your LLM applications. Join us today and take the first step towards becoming an expert in LLM security. Enroll now and unlock the knowledge you need to safeguard your AI projects in an increasingly complex digital landscape.
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What's inside
Learning objectives
- Identifying llm security vulnerabilities and attack vectors
- Mitigating model replication and shadowing attacks
- Recognizing insecure output handling and prompt injection
- Preventing model theft and excessive agency issues
- Implementing strategies for secure plugin design
- Redacting sensitive information using apis and regex
- Monitoring and updating dependencies for security
- Analyzing generative ai application types and architectures
- Understanding multi-model applications and specialized models
- Comparing api-based, embedded, and multi-model applications
- Identifying llm security vulnerabilities and attack vectors
- Mitigating model replication and shadowing attacks
- Recognizing insecure output handling and prompt injection
- Preventing model theft and excessive agency issues
- Implementing strategies for secure plugin design
- Redacting sensitive information using apis and regex
- Monitoring and updating dependencies for security
- Analyzing generative ai application types and architectures
- Understanding multi-model applications and specialized models
- Comparing api-based, embedded, and multi-model applications
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Activities
Practice LLM security attacks
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Practice common LLM security attacks to identify and understand potential threats.
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AI Security
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Review common LLM security attack vectors
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Develop a list of potential attack scenarios
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Implement attacks and analyze the vulnerabilities
Build a secure LLM application
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Design and build a secure LLM application to implement various security measures.
Show steps
-
Identify security requirements and threat model
-
Design and implement secure plugin interfaces
-
Validate and sanitize inputs to prevent malicious attacks
Show all two activities
Practice LLM security attacks
Show steps
Practice common LLM security attacks to identify and understand potential threats.
Browse courses on
AI Security
Show steps
- Review common LLM security attack vectors
- Develop a list of potential attack scenarios
- Implement attacks and analyze the vulnerabilities
Build a secure LLM application
Show steps
Design and build a secure LLM application to implement various security measures.
Show steps
- Identify security requirements and threat model
- Design and implement secure plugin interfaces
- Validate and sanitize inputs to prevent malicious attacks
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