AI Security: Preventing Prompt Injection

As large language models (LLMs) become integrated into more applications, they introduce new and unique security risks. The most common and critical vulnerability is prompt injection, where a malicious user can hijack the model's behavior. This course provides a foundational understanding of AI security, focusing on the practical skills needed to defend against these attacks. You will learn to think like an attacker to spot vulnerabilities and then build robust defenses, or 'guardrails', to protect your LLM-powered applications and their users. What you'll learn: - Understand the core principles of prompt injection and its variants, including jailbreaking. - Learn to identify common attack patterns in user-facing applications. - Apply defensive techniques such as input sanitization, instructional defense, and output filtering. - Practice building secure wrappers for API calls to large language models. - Configure basic monitoring and logging to detect potential security threats. - Discover the broader landscape of AI security risks, including data privacy concerns. The course begins with the fundamental concepts of prompt engineering and the mechanics of injection attacks, then progresses to hands-on defense strategies and best practices for building secure systems. This course is designed for developers, security analysts, and tech professionals who are new to the security challenges of AI. No prior experience in AI security is required, though a basic understanding of software development concepts is helpful. Begin your journey into AI security and learn to build more resilient applications.