OWASP Top 10 for LLM Applications (2025)

OWASP Top 10 for LLM Applications (2025) | Udemy
English | Size: 3.9 GB
Genre: eLearning

LLM Security in Practice

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.

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.

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