Owasp Top 10 For Llm Applications (2025)

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.

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.

Overview

Section 1: Module 1: Introduction to LLM Application Security

Lecture 1 Introduction to LLMs and their applications

Lecture 2 Overview of security challenges specific to LLM applications

Lecture 3 Introduction to the OWASP Top 10 LLM Applications list

Lecture 4 Importance of secure LLM development and deployment

Lecture 5 Real-world case studies of successful/unsuccessful LLM implementations

Lecture 6 Common LLM application architectures (e.g., RAG)

Lecture 7 The threat landscape: motivations of attackers targeting LLM applications.

Section 2: Module 2: LLM01:2025 – Prompt Injection

Lecture 8 Detailed explanation of prompt injection vulnerabilities

Lecture 9 Types of prompt injection (direct and indirect)

Lecture 10 Potential impacts of prompt injection attacks

Lecture 11 Prevention and mitigation strategies

Lecture 12 Evolution of prompt injection techniques and their increasing sophistication.

Lecture 13 Impact deep dive: specific examples

Lecture 14 Defense-in-depth: combining input validation, output filtering, and human review

Section 3: Module 3: LLM02:2025 – Sensitive Information Disclosure

Lecture 15 Common examples of vulnerabilities(PII leakage, proprietary algorithm exposure.)

Lecture 16 Understanding the risks of sensitive information disclosure in LLM applications

Lecture 17 Prevention and mitigation strategies (sanitization, access controls, etc.)

Lecture 18 Data minimization: importance of minimizing sensitive data collection.

Lecture 19 Privacy-enhancing technologies - PET

Lecture 20 Legal and compliance: legal implications of sensitive data disclosure

Section 4: Module 4: LLM03:2025 – Supply Chain

Lecture 21 Supply chain vulnerabilities in LLM development and deployment

Lecture 22 Prevention and mitigation strategies for supply chain risks

Lecture 23 SBOMs in detail: explanation of Software Bill of Materials (SBOMs) and their imp

Lecture 24 Model provenance challenges: difficulties in verifying the origin and integrity

Lecture 25 Governance and policy: importance of clear policies for using third-party LLMs

Section 5: Module 5: LLM04:2025 – Data and Model Poisoning

Lecture 26 Understanding data and model poisoning attacks

Lecture 27 How poisoning can impact LLM behavior and security

Lecture 28 Prevention and mitigation strategies

Lecture 29 Poisoning scenarios across the lifecycle: poisoning in training and fine-tuning

Lecture 30 Backdoor attacks: detail on how backdoors are inserted

Lecture 31 Robustness testing: need for rigorous testing to detect poisoning effects.

Section 6: Module 6: LLM05:2025 – Improper Output Handling

Lecture 32 Risks associated with improper handling of LLM outputs

Lecture 33 Vulnerabilities such as XSS, SQL injection, and remote code execution

Lecture 34 Prevention and mitigation strategies

Lecture 35 Output encoding examples: code examples for different contexts (e.g., HTML, SQL)

Lecture 36 Real-world exploits: detail cases where improper output handling led to breaches

Section 7: Module 7: LLM06:2025 – Excessive Agency

Lecture 37 The concept of agency in LLM systems and associated risks

Lecture 38 Risks of excessive functionality, permissions, and autonomy

Lecture 39 Prevention and mitigation strategies

Lecture 40 Agentic systems: explanation of LLM agents, their benefits, and risks.

Lecture 41 Least privilege in depth: detailed guidance on implementing least privilege.

Lecture 42 Authorization frameworks: best practices for managing authorization in LLM

Section 8: Module 8: LLM07:2025 – System Prompt Leakage

Lecture 43 Vulnerability of system prompt leakage

Lecture 44 Risks associated with exposing system prompts

Lecture 45 Prevention and mitigation strategies

Lecture 46 Prompt engineering risks: how prompt engineering can extract system prompts.

Lecture 47 Defense in depth for prompts

Lecture 48 Secure design principles

Section 9: Module 9: LLM08:2025 – Vector and Embedding Weaknesses

Lecture 49 Vulnerabilities related to vector and embedding usage in LLM applications

Lecture 50 Risks of unauthorized access, data leakage, and poisoning

Lecture 51 Prevention and mitigation strategies

Lecture 52 Embedding security: details on securing vector databases and embeddings.

Lecture 53 RAG security best practices

Lecture 54 Emerging research

Section 10: Module 10: LLM09:2025 – Misinformation

Lecture 55 The issue of misinformation generated by LLMs

Lecture 56 Causes and potential impacts of misinformation

Lecture 57 Prevention and mitigation strategies

Lecture 58 The spectrum of misinformation

Lecture 59 Impact on specific domains

Lecture 60 Detection and mitigation techniques

Section 11: Module 11: LLM10:2025 – Unbounded Consumption

Lecture 61 Risks associated with excessive and uncontrolled LLM usage

Lecture 62 Vulnerabilities that can lead to denial of service, economic losses, etc.

Lecture 63 Prevention and mitigation strategies

Lecture 64 Economic denial of service

Lecture 65 Rate limiting strategies

Lecture 66 Model extraction defenses

Section 12: Module 12: Best Practices and Future Trends in LLM Security

Lecture 67 Summary of key security principles for LLM applications

Lecture 68 Emerging trends and future challenges in LLM security

Lecture 69 Resources and further learning

Lecture 70 Secure LLM development lifecycle: integrating security into every stage.

Lecture 71 Emerging technologies

Lecture 72 The role of standards and regulations

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.