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OWASP Top 10 for LLMs - Generative AI Security
Rating: 4.3 out of 5(28 ratings)
1,375 students

OWASP Top 10 for LLMs - Generative AI Security

Master LLM security - Learn to identify, prevent, and mitigate unique security risks in Large Language Models
Last updated 10/2025
English

What you'll learn

  • Identify and understand the OWASP Top 10 security risks specific to Large Language Models
  • Implement robust input validation and sanitization techniques to prevent prompt injection attacks
  • Apply the principle of least privilege to protect LLM systems from excessive agency vulnerabilities
  • Develop effective strategies to prevent system prompt leakage and mitigate data poisoning risks

Course content

5 sections11 lectures40m total length
  • Course Overview and Why LLM Security Matters3:48

    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.

  • LLMs vs Traditional Software Security1:51
  • Introduction to OWASP Top 10 for LLMs6:53

Requirements

  • Basic familiarity with AI concepts and cybersecurity principles is helpful but not required

Description

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.

Who this course is for:

  • Software developers, security professionals, AI engineers, and IT managers who are implementing or managing LLM-based applications and want to ensure they're built with proper security safeguards.