AI Security and Vulnerabilities: Jailbreaking, Injections, and Guardrails

Artificial intelligence is transforming how we build software, but large language models introduce entirely new security risks and vulnerabilities. Understanding how these models can be manipulated is essential for anyone developing, deploying, or interacting with modern AI systems. This course prepares you to recognize and mitigate critical AI vulnerabilities. You will transition from a general AI user to a security-aware practitioner capable of analyzing prompt injections, preventing jailbreaks, and implementing robust defenses to secure LLM-based applications. What you'll learn: - Understand the foundational mechanics of LLMs and why they are susceptible to manipulation. - Identify common security risks including prompt injections, jailbreaking, and data poisoning. - Analyze how hallucinations occur and how to minimize their impact on application outputs. - Apply defensive prompting techniques and explore modern guardrail frameworks to secure AI inputs and outputs. - Explore the OWASP Top 10 vulnerabilities for LLM applications to align with industry security standards. - Practice crafting and analyzing security-focused prompts through interactive written exercises. You will start by exploring core AI security concepts and terminology before diving into real-world exploit scenarios. Through clear written explanations and structured analysis exercises, you will learn how to design safer AI interactions and implement effective guardrails. This course is designed for beginners, developers, and security enthusiasts looking to understand the security landscape of artificial intelligence. No prior experience in cybersecurity or AI engineering is required, as we build all concepts from the ground up. Start reading today to build a safer, more resilient foundation for your AI projects.