
Understand why llms face unique security challenges and master the OWASP top ten risks for LLMs, from prompt injection to data poisoning, with practical mitigations for secure integration.
Examine input handling vulnerabilities in llms, including prompt injection and system prompt leakage, and explore how inadequate input validation and weak boundary enforcement threaten security and trust.
Examine how output handling can disclose sensitive data, and how misinformation, hallucinations, and improper output handling undermine trust, and how robust output filtering mitigates risk.
Secure output handling for llms with multi-layer filtering, fact checking, least-privilege data access, and human review to prevent data leakage and hallucinations, grounding responses with retrieval augmented generation.
Secure data and models for LLM applications by validating training data, monitoring outputs, and cryptographically verifying data integrity across the supply chain.
As Large Language Models (LLMs) become integral components of modern applications—from customer service chatbots to content generation tools—they introduce unique security challenges that traditional cybersecurity approaches don't fully address. This comprehensive course dives into the OWASP Top 10 for LLMs, a specialized framework designed to address emerging vulnerabilities specific to AI language models.
In this course, you'll learn why securing LLMs requires different strategies than conventional software. We'll explore how these AI systems amplify familiar threats like injection attacks while introducing new risks such as prompt manipulation, data poisoning, and misinformation generation (hallucinations).
What You'll Learn:
Understanding the full OWASP Top 10 risk list for LLMs and their real-world implications
Implementing robust input validation to prevent prompt injection attacks
Applying the principle of least privilege to limit LLM capabilities and prevent excessive agency
Creating effective barriers to prevent system prompt leakage
Monitoring LLM activity through comprehensive logging and real-time dashboards
Enforcing proper output handling to prevent sensitive information disclosure
Protecting against data and model poisoning attacks
Through detailed explanations and practical examples, you'll learn how these vulnerabilities can compromise AI systems and how to implement effective mitigation strategies. The course emphasizes a holistic security approach that addresses both the AI model itself and the surrounding application architecture.
By the end of this course, you'll be equipped with the knowledge to identify potential security risks in your LLM implementations and implement robust safeguards to protect against them. Whether you're a developer integrating LLMs into applications, a security professional tasked with securing AI systems, or an IT manager overseeing AI projects, this course provides essential guidance for responsible LLM deployment.
Don't let security be an afterthought in your AI journey. Master the unique security challenges of LLMs and build safer, more trustworthy AI applications with this essential OWASP Top 10 for LLMs course.