Courses OWASP Top 10 for LLM Applications (2025)

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What you'll learn​

• Understand the top 10 security risks in LLM-based applications, as defined by the OWASP LLM Top 10 (2025).
• Identify real-world vulnerabilities like prompt injection, model poisoning, and sensitive data exposure — and how they appear in production systems.
• Learn practical, system-level defense strategies to protect LLM apps from misuse, overuse, and targeted attacks.
• Gain hands-on knowledge of emerging threats such as agent-based misuse, vector database leaks, and embedding inversion.
• Explore best practices for secure prompt design, output filtering, plugin sandboxing, and rate limiting.
• Stay ahead of AI-related regulations, compliance challenges, and upcoming security frameworks.
• Build the mindset of a secure LLM architect — combining threat modeling, secure design, and proactive monitoring.

Course content​

12 sections • 73 lectures • 6h 10m total length

\---OWASP Top 10 for LLM Applications 2025
| HTDark.CoM.txt
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+---1 - Module 1 Introduction to LLM Application Security
| 1 -Introduction to LLMs and their applications .mp4
| 2 -Overview of security challenges specific to LLM applications .mp4
| 3 -Introduction to the OWASP Top 10 LLM Applications list .mp4
| 4 -Importance of secure LLM development and deployment .mp4
| 5 -Real-world case studies of successfulunsuccessful LLM implementations .mp4
| 6 -Common LLM application architectures (e.g., RAG) .mp4
| 7 -The threat landscape motivations of attackers targeting LLM applications .mp4
|
+---10 - Module 10 LLM092025 û Misinformation
| 1 -The issue of misinformation generated by LLMs .mp4
| 2 -Causes and potential impacts of misinformation .mp4
| 3 -Prevention and mitigation strategies .mp4
| 4 -The spectrum of misinformation .mp4
| 5 -Impact on specific domains .mp4
| 6 -Detection and mitigation techniques .mp4
|
+---11 - Module 11 LLM102025 û Unbounded Consumption
| 1 -Risks associated with excessive and uncontrolled LLM usage .mp4
| 2 -Vulnerabilities that can lead to denial of service, economic losses, etc .mp4
| 3 -Prevention and mitigation strategies .mp4
| 4 -Economic denial of service .mp4
| 5 -Rate limiting strategies .mp4
| 6 -Model extraction defenses .mp4
|
+---12 - Module 12 Best Practices and Future Trends in LLM Security
| 1 -Summary of key security principles for LLM applications .mp4
| 2 -Emerging trends and future challenges in LLM security .mp4
| 3 -Resources and further learning .mp4
| 5 -Emerging technologies .mp4
| 6 -The role of standards and regulations .mp4
|
+---2 - Module 2 LLM012025 û Prompt Injection
| 1 -Detailed explanation of prompt injection vulnerabilities .mp4
| 2 -Types of prompt injection (direct and indirect) .mp4
| 3 -Potential impacts of prompt injection attacks .mp4
| 4 -Prevention and mitigation strategies .mp4
| 5 -Evolution of prompt injection techniques and their increasing sophistication .mp4
| 6 -Impact deep dive specific examples .mp4
| 7 -Defense-in-depth combining input validation, output filtering, and human review .mp4
|
+---3 - Module 3 LLM022025 û Sensitive Information Disclosure
| 4 -Data minimization importance of minimizing sensitive data collection .mp4
| 5 -Privacy-enhancing technologies - PET .mp4
| 6 -Legal and compliance legal implications of sensitive data disclosure .mp4
|
+---4 - Module 4 LLM032025 û Supply Chain
| 1 -Supply chain vulnerabilities in LLM development and deployment .mp4
| 2 -Prevention and mitigation strategies for supply chain risks .mp4
| 3 -SBOMs in detail explanation of Software Bill of Materials (SBOMs) and their imp .mp4
| 4 -Model provenance challenges difficulties in verifying the origin and integrity .mp4
| 5 -Governance and policy importance of clear policies for using third-party LLMs .mp4
|
+---5 - Module 5 LLM042025 û Data and Model Poisoning
| 1 -Understanding data and model poisoning attacks .mp4
| 2 -How poisoning can impact LLM behavior and security .mp4
| 3 -Prevention and mitigation strategies .mp4
| 4 -Poisoning scenarios across the lifecycle poisoning in training and fine-tuning .mp4
| 5 -Backdoor attacks detail on how backdoors are inserted .mp4
| 6 -Robustness testing need for rigorous testing to detect poisoning effects .mp4
|
+---6 - Module 6 LLM052025 û Improper Output Handling
| 1 -Risks associated with improper handling of LLM outputs .mp4
| 2 -Vulnerabilities such as XSS, SQL injection, and remote code execution .mp4
| 3 -Prevention and mitigation strategies .mp4
| 4 -Output encoding examples code examples for different contexts (e.g., HTML, SQL) .mp4
| 5 -Real-world exploits detail cases where improper output handling led to breaches .mp4
|
+---7 - Module 7 LLM062025 û Excessive Agency
| 1 -The concept of agency in LLM systems and associated risks .mp4
| 2 -Risks of excessive functionality, permissions, and autonomy .mp4
| 3 -Prevention and mitigation strategies .mp4
| 4 -Agentic systems explanation of LLM agents, their benefits, and risks .mp4
| 5 -Least privilege in depth detailed guidance on implementing least privilege .mp4
| 6 -Authorization frameworks best practices for managing authorization in LLM .mp4
|
+---8 - Module 8 LLM072025 û System Prompt Leakage
| 1 -Vulnerability of system prompt leakage .mp4
| 2 -Risks associated with exposing system prompts .mp4
| 3 -Prevention and mitigation strategies .mp4
| 4 -Prompt engineering risks how prompt engineering can extract system prompts .mp4
| 5 -Defense in depth for prompts .mp4
| 6 -Secure design principles .mp4
|
\---9 - Module 9 LLM082025 û Vector and Embedding Weaknesses
1 -Vulnerabilities related to vector and embedding usage in LLM applications .mp4
2 -Risks of unauthorized access, data leakage, and poisoning .mp4
3 -Prevention and mitigation strategies .mp4
4 -Embedding security details on securing vector databases and embeddings .mp4
5 -RAG security best practices .mp4
6 -Emerging research .mp4

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Requirements​

• No deep security background is required — just basic familiarity with how LLM applications work.
• Ideal for developers, architects, product managers, and AI engineers working with or integrating large language models.
• Some understanding of prompts, APIs, or tools like GPT, LangChain, or vector databases is helpful — but not mandatory.
• Curiosity about LLM risks and a desire to build secure AI systems is all you really need.
• Comfort with reading or writing basic prompt examples, or experience using LLMs like ChatGPT, Claude, or similar tools.
• A general understanding of how software applications interact with APIs or user input will make concepts easier to grasp.

Description​

Large Language Models (LLMs) like GPT-4, Claude, Mistral, and open-source alternatives are transforming the way we build applications. They’re powering chatbots, copilots, retrieval systems, autonomous agents, and enterprise search — quickly becoming central to everything from productivity tools to customer-facing platforms.

But with that innovation comes a new generation of risks — subtle, high-impact vulnerabilities that don’t exist in traditional software architectures. We’re entering a world where inputs look like language, exploits hide inside documents, and attackers don’t need code access to compromise your system.

This course is built around the OWASP Top 10 for LLM Applications (2025) — the most comprehensive and community-vetted security framework for generative AI systems available today.

Whether you're working with OpenAI’s APIs, Anthropic’s Claude, open-source LLMs via Hugging Face, or building proprietary models in-house, this course will teach you how to secure your LLM-based architecture from design through deployment.

You’ll go deep into the vulnerabilities that matter most:

• How prompt injection attacks hijack model behavior with just a few well-placed words.
• How data and model poisoning slip through fine-tuning pipelines or vector stores.
• How sensitive information leaks, not through bugs, but through prediction.
• How models can be tricked into using tools, calling APIs, or consuming resources far beyond what you intended.
• And how LLM systems can be scraped, cloned, or manipulated without ever touching your backend.

But more importantly — you’ll learn how to stop these risks before they start.

This isn’t a high-level overview or a dry list of threats. It’s a practical, story-driven, security-focused deep dive into how modern LLM apps fail — and how to build ones that don’t.

Who this course is for:​

• AI developers and engineers building or integrating LLMs into real-world applications.
• Security professionals looking to understand how traditional threat models evolve in the context of AI.
• Product managers, architects, and tech leads who want to make informed decisions about deploying LLMs safely.
• Startup founders and CTOs working on AI-driven products who need to get ahead of risks before they scale.
• Anyone curious about the vulnerabilities behind large language models — and how to build systems that can stand up to real-world threats.
• AI/ML developers working with GPT, Claude, or open-source LLMs who want to understand and prevent security risks in their applications.
• Security engineers and AppSec teams who need to expand their threat models to include prompt injection, model misuse, and AI supply chain risks.
• Product managers and tech leads overseeing LLM-integrated products — including chatbots, copilots, agents, and retrieval-based systems.
• Software architects and solution designers who want to build secure-by-default LLM pipelines from the ground up.
• DevOps and MLOps professionals responsible for deployment, monitoring, and safe rollout of AI capabilities across cloud platforms.
• AI startup founders, CTOs, and engineering managers who want to avoid high-cost mistakes as they scale their LLM offerings.
• Security researchers and red teamers interested in exploring the new attack surfaces introduced by generative AI tools.
• Regulatory, privacy, or risk teams trying to understand where LLM behavior intersects with legal and compliance obligations.
• Educators, analysts, and advanced learners who want a practical understanding of the OWASP Top 10 for LLMs — beyond the headlines.
• Anyone responsible for designing, deploying, or defending LLM-powered systems — regardless of whether you write code yourself.

https://www.udemy.com/course/owasp-top-10-for-llm-applications-2025

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