Udemy - OWASP Top 10 for LLM Applications (2025)

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Udemy - OWASP Top 10 for LLM Applications (2025) (Size: 3.9 GB)
  1 -Common examples of vulnerabilities(PII leakage, proprietary algorithm exposure.).mp4 78.7 MB
  1 -Detailed explanation of prompt injection vulnerabilities.mp4 42.3 MB
  1 -Introduction to LLMs and their applications.mp4 53.1 MB
  1 -Risks associated with excessive and uncontrolled LLM usage.mp4 55.2 MB
  1 -Risks associated with improper handling of LLM outputs.mp4 49.2 MB
  1 -Summary of key security principles for LLM applications.mp4 37.2 MB
  1 -Supply chain vulnerabilities in LLM development and deployment.mp4 109 MB
  1 -The concept of agency in LLM systems and associated risks.mp4 57.3 MB
  1 -The issue of misinformation generated by LLMs.mp4 78.8 MB
  1 -Understanding data and model poisoning attacks.mp4 29.2 MB
  1 -Vulnerabilities related to vector and embedding usage in LLM applications.mp4 44 MB
  1 -Vulnerability of system prompt leakage.mp4 34.4 MB
  2 -Causes and potential impacts of misinformation.mp4 96.4 MB
  2 -Emerging trends and future challenges in LLM security.mp4 48.6 MB
  2 -How poisoning can impact LLM behavior and security.mp4 44.7 MB
  2 -Overview of security challenges specific to LLM applications.mp4 28.2 MB
  2 -Prevention and mitigation strategies for supply chain risks.mp4 50.5 MB
  2 -Risks associated with exposing system prompts.mp4 36.6 MB
  2 -Risks of excessive functionality, permissions, and autonomy.mp4 52.6 MB
  2 -Risks of unauthorized access, data leakage, and poisoning.mp4 53.3 MB
  2 -Types of prompt injection (direct and indirect).mp4 52.5 MB
  2 -Understanding the risks of sensitive information disclosure in LLM applications.mp4 102.2 MB
  2 -Vulnerabilities such as XSS, SQL injection, and remote code execution.mp4 41.6 MB
  2 -Vulnerabilities that can lead to denial of service, economic losses, etc.mp4 60.5 MB
  3 -Introduction to the OWASP Top 10 LLM Applications list.mp4 39.8 MB
  3 -Potential impacts of prompt injection attacks.mp4 60.1 MB
  3 -Prevention and mitigation strategies (sanitization, access controls, etc.).mp4 60.6 MB
  3 -Prevention and mitigation strategies.mp4 47.7 MB
  3 -Resources and further learning.mp4 38.1 MB
  3 -SBOMs in detail explanation of Software Bill of Materials (SBOMs) and their imp.mp4 52.3 MB
  4 -Agentic systems explanation of LLM agents, their benefits, and risks.mp4 41.4 MB
  4 -Data minimization importance of minimizing sensitive data collection.mp4 44.8 MB
  4 -Economic denial of service.mp4 42.4 MB
  4 -Embedding security details on securing vector databases and embeddings.mp4 48.4 MB
  4 -Importance of secure LLM development and deployment.mp4 45.8 MB
  4 -Model provenance challenges difficulties in verifying the origin and integrity.mp4 46 MB
  4 -Output encoding examples code examples for different contexts (e.g., HTML, SQL).mp4 41.6 MB
  4 -Poisoning scenarios across the lifecycle poisoning in training and fine-tuning.mp4 47.6 MB
  4 -Prevention and mitigation strategies.mp4 59.2 MB
  4 -Prompt engineering risks how prompt engineering can extract system prompts.mp4 40.9 MB
  4 -Secure LLM development lifecycle integrating security into every stage.mp4 45.9 MB
  4 -The spectrum of misinformation.mp4 82 MB
  5 -Backdoor attacks detail on how backdoors are inserted.mp4 40.6 MB
  5 -Defense in depth for prompts.mp4 54.6 MB
  5 -Emerging technologies.mp4 57.1 MB
  5 -Evolution of prompt injection techniques and their increasing sophistication.mp4 53.4 MB
  5 -Governance and policy importance of clear policies for using third-party LLMs.mp4 46.7 MB
  5 -Impact on specific domains.mp4 99.4 MB
  5 -Least privilege in depth detailed guidance on implementing least privilege.mp4 64 MB
  5 -Privacy-enhancing technologies - PET.mp4 58.8 MB
  5 -RAG security best practices.mp4 58 MB
  5 -Rate limiting strategies.mp4 44.1 MB
  5 -Real-world case studies of successfulunsuccessful LLM implementations.mp4 68.9 MB
  5 -Real-world exploits detail cases where improper output handling led to breaches.mp4 53.2 MB
  6 -Authorization frameworks best practices for managing authorization in LLM.mp4 44.6 MB
  6 -Common LLM application architectures (e.g., RAG).mp4 69.2 MB
  6 -Detection and mitigation techniques.mp4 112.6 MB
  6 -Emerging research.mp4 57.9 MB
  6 -Impact deep dive specific examples.mp4 64.9 MB
  6 -Legal and compliance legal implications of sensitive data disclosure.mp4 68.8 MB
  6 -Model extraction defenses.mp4 52 MB
  6 -Robustness testing need for rigorous testing to detect poisoning effects.mp4 45.1 MB
  6 -Secure design principles.mp4 52.4 MB
  6 -The role of standards and regulations.mp4 49.8 MB
  7 -Defense-in-depth combining input validation, output filtering, and human review.mp4 58.3 MB
  7 -The threat landscape motivations of attackers targeting LLM applications.mp4 58 MB
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  ▲ 74 total files

Description


OWASP Top 10 for LLM Applications (2025)

https://WebToolTip.com

Published 5/2025
Created by Cyberdefense Learning
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 72 Lectures ( 6h 5m ) | Size: 3.86 GB

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.

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