OWASP Top 10 for LLMs - Generative AI Security

Name: OWASP Top 10 for LLMs - Generative AI Security
Rating: 4.3 (31 reviews)

Master LLM security - Learn to identify, prevent, and mitigate unique security risks in Large Language Models

Created byNextGen Learning

Last updated 10/2025

English

English [Auto],

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 sections • 11 lectures • 40m 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
Compare lms with traditional software, focusing on probabilistic outputs, black box debugging, and expanded attack surfaces from continual learning. Highlight data privacy and misinformation risks with high amplification.
Introduction to OWASP Top 10 for LLMs6:53
Explore the OWASP top ten for LLMs, a framework highlighting critical security risks—prompt injection, sensitive information disclosure, data and model poisoning, input handling vulnerabilities, and more—to guide secure AI applications.

Input Handling Vulnerabilities6:14
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.
Output Handling Vulnerabilities5:27
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.
Data and Model Integrity Risks5:30
Explore how data and model integrity risks threaten LLM systems, including data and model poisoning, backdoors, and vector embedding weaknesses, with practical defenses.

Securing Input Handling1:56
Implement robust input validation and sanitization to filter malicious prompts before LM processing. Enforce strict boundaries between system and user inputs, and apply defense in depth with rate limiting.
Securing Output Handling1:44
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.
Securing Data and Models3:38
Secure data and models for LLM applications by validating training data, monitoring outputs, and cryptographically verifying data integrity across the supply chain.

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.

OWASP Top 10 for LLMs - Generative AI Security

What you'll learn

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Course content

Introduction to LLM Security3 lectures • 13min

Key Vulnerability Categories3 lectures • 17min

Mitigation Strategies3 lectures • 7min

Conclusion1 lecture • 4min

Additional Study Materials1 lecture • 1min

Requirements

Description

Who this course is for: