What you'll learn:
- Learn the OWASP Top 10 for LLMs
- Explore the foundational principles of the Open Web Application Security Project.
- Understand the core architecture, functionality, and risks associated with Large Language Models.
- Learn to identify and mitigate vulnerabilities from malicious inputs that can alter LLM behavior.
- Ensure safe handling and rendering of LLM outputs to prevent unintended data leaks.
- Prevent and respond to attacks aiming to corrupt the data used to train LLMs.
- Tackle threats that aim to overload or disrupt LLM services, ensuring availability.
- Address risks introduced through third-party services and dependencies.
- Prevent unintended exposure of sensitive data through LLM interactions.
- Securely design and implement plugins or extensions.
- Manage and limit the autonomous decision-making capabilities of LLMs.
- Educate on the risks and limitations of over-dependence on LLM.
- Protect LLM intellectual property from unauthorized access and duplication.
This course contains the use of artificial intelligence.
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.
This course contains promotional materials.
