Overview What You Will Learn Curriculum Instructor

Course Overview

This course provides a comprehensive introduction to Large Language Model (LLM) application security, an increasingly important discipline as organizations integrate generative AI, RAG architectures, and agentic systems into business and cybersecurity workflows. It focuses on understanding how LLM applications can introduce security, privacy, operational, and governance risks while equipping learners with practical knowledge to identify and mitigate these risks using the OWASP Top 10 for LLM Applications framework.

This course begins with an introduction to LLM application security, common LLM architectures, the threat landscape, and OWASP’s LLM security risks. It then explores prompt injection, sensitive information disclosure, supply chain vulnerabilities, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation, and unbounded consumption. Hands-on labs reinforce each topic through attack simulations, detection exercises, mitigation techniques, RAG security testing, authorization controls, and defensive practices. The course concludes with secure LLM development lifecycle practices, emerging technologies, future security challenges, standards, and regulations.

By completing this course, you will gain practical skills to identify, assess, exploit, and mitigate key security risks across modern LLM applications and deployments.

What You Will 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.

Program Curriculum

  • Course Trailer
  • Chapter 1 Quiz

  • Introduction to LLMs and Their Applications
  • Overview of Security Challenges Specific to LLM Applications
  • Introduction to the OWASP Top 10 LLM Applications List
  • Importance of Secure LLM Development and Deployment
  • Real-world Case Studies of Successful / Unsuccessful LLM Implementations
  • Common LLM Application Architectures (e.g., RAG)
  • The Threat Landscape: Motivations of Attackers Targeting LLM Applications
  • Lab 1 - Playground Rules
  • Lab 2 - OWASP AI Sampler
  • Lab 3 - RAG Threat Mapping
  • Chapter 2 Quiz

  • Detailed Explanation of Prompt Injection Vulnerabilities
  • Types of Prompt Injection (Direct and Indirect)
  • Potential Impacts of Prompt Injection Attacks
  • Prevention And Mitigation Strategies
  • Evolution of Prompt Injection Techniques and Their Increasing Sophistication
  • Impact Deep Dive: Specific Examples
  • Defense-in-Depth: Combining Input Validation, Output Filtering, and Human Review
  • LLM01:2025 - Prompt Injection - Lab 1
  • LLM01:2025 - Prompt Injection - Lab 2
  • LLM01:2025 - Prompt Injection - Lab 3
  • Chapter 3 Quiz

  • Common Examples of Vulnerabilities (PII Leakage, Proprietary Algorithm Exposure)
  • Understanding The Risks of Sensitive Information Disclosure in LLM Applications
  • Prevention And Mitigation Strategies (Sanitization, Access Controls, etc.)
  • Data Minimization: Importance of Minimizing Sensitive Data Collection
  • Privacy-enhancing Technologies - PET
  • Legal and Compliance: Legal Implications of Sensitive Data Disclosure
  • Lab 1 - Detecting Sensitive Information Leakage
  • Lab 2 - Sanitization & Access Control Mitigation
  • Lab 3 - Defense-in-Depth & Compliance Audit
  • Chapter 4 Quiz

  • Supply Chain Vulnerabilities in LLM Development and Deployment
  • Risks Associated with Third-party Models, Data, and Components
  • Prevention and Mitigation Strategies for Supply Chain Risks
  • SBOMs In Detail: Explanation of Software Bill of Materials (SBOMs) and Their Imp
  • Model Provenance Challenges: Difficulties in Verifying the Origin and Integrity
  • Governance and Policy: Importance Of Clear Policies for Using Third-party LLMs
  • Lab 1 - Dependency Model Integrity Check
  • Lab 2 - Third-party Risk Simulation
  • Lab 3 - SBOM Provenance Verification
  • Chapter 5 Quiz

  • Understanding Data and Model Poisoning Attacks
  • How Poisoning Can Impact LLM Behavior and Security
  • Prevention and Mitigation Strategies
  • Poisoning Scenarios Across the Lifecycle: Poisoning in Training and Fine-tuning
  • Backdoor Attacks: Detail on How Backdoors Are Inserted
  • Robustness Testing: Need for Rigorous Testing to Detect Poisoning Effects
  • Lab 1 - Data Poisoning Warm-up
  • Lab 2 - Trigger-based Backdoor Poisoning
  • Lab 3 - Lifecycle Data Poisoning Simulation
  • Lab 4 - Robustness Testing & Poison Detection
  • Chapter 6 Quiz

  • Risks Associated with Improper Handling of LLM Outputs
  • Vulnerabilities Such as XSS, SQL Injection, and Remote Code Execution
  • Prevention and Mitigation Strategies
  • Output Encoding Examples: Code Examples for Different Contexts (e.g., HTML, SQL)
  • Real-world Exploits: Detail Cases Where Improper Output Handling Led to Breaches
  • Lab 1 - XSS via LLM Output (Improper HTML Handling)
  • Lab 2 - SQL Injection via LLM Output
  • Lab 3 - Command RCE-style Output Handling
  • Chapter 7 Quiz

  • The Concept of Agency in LLM Systems and Associated Risks
  • Risks of Excessive Functionality, Permissions, and Autonomy
  • Prevention and Mitigation Strategies
  • Agentic Systems: Explanation of LLM Agents, Their Benefits, and Risks
  • Least Privilege in Depth: Detailed Guidance on Implementing Least Privilege
  • Authorization Frameworks: Best Practices for Managing Authorization in LLM
  • Lab 1 - Agent Permission Escalation
  • Lab 2 - Safeguarded Agent Actions
  • Lab 3 - Command RCE-style Output Handling
  • Chapter 8 Quiz

  • Vulnerability of System Prompt Leakage
  • Risks Associated with Exposing System Prompts
  • Prevention and Mitigation Strategies
  • Prompt Engineering Risks: How Prompt Engineering Can Extract System Prompts
  • Defense-in-Depth for Prompts
  • Secure Design Principles
  • Lab 1 - Direct System Prompt Leakage
  • Lab 2 - Preventing System Prompt Leakage
  • Lab 3 - Secure Prompt Architecture
  • Chapter 9 Quiz

  • Vulnerabilities Related to Vector and Embedding Usage in LLM Applications
  • Risks of Unauthorized Access, Data Leakage, and Poisoning
  • Prevention and Mitigation Strategies
  • Embedding Security: Details on Securing Vector Databases and Embeddings
  • RAG Security Best Practices
  • Emerging Research
  • Lab 1 - Leaky RAG Vector-based Data Exposure
  • Lab 2 - Vector Poisoning Incident
  • Lab 3 - Secure RAG Hardening Vector Retrieval
  • Chapter 10 Quiz

  • The Issue of Misinformation Generated by LLMs
  • Causes and Potential Impacts of Misinformation
  • Prevention and Mitigation Strategies
  • The Spectrum of Misinformation
  • Impact on Specific Domains
  • Detection and Mitigation Techniques
  • Lab 1 - Hallucination & False Confidence
  • Lab 2 - Deliberate Misinformation Injection
  • Lab 3 - Defending Against Misinformation
  • Chapter 11 Quiz

  • Risks Associated with Excessive and Uncontrolled LLM Usage
  • Vulnerabilities That Can Lead to Denial of Service, Economic Losses, etc.
  • Prevention and Mitigation Strategies
  • Economic Denial of Service
  • Rate Limiting Strategies
  • Model Extraction Defenses
  • Lab 1 - Unbounded Consumption
  • Lab 2 - Economic Denial of Service (EDoS)
  • Lab 3 - Defenses Rate Limits, Quotas & Budget Enforcement
  • Chapter 12 Quiz

  • Summary of Key Security Principles for LLM Applications
  • Emerging Trends and Future Challenges in LLM Security
  • Resources and Further Learning
  • Secure LLM Development Lifecycle: Integrating Security into Every Stage
  • Emerging Technologies
  • The Role of Standards and Regulations
  • Chapter 13 Quiz
Load more modules

Instructor

Anand Rao Nednur

Anand Rao is a senior technical instructor and cloud consultant with approximately 15 years of experience working with large enterprises. He has a wide range of technologies in his portfolio and is adept at cloud platforms including Azure, AWS, and GCP. He is also well-versed in IAM, security, and automation using PowerShell and Python. In addition, Anand has been developing and updating content for various courses and assisting engineers with lab examinations and professional certifications.

Anand Rao has delivered instructor-led training across several states in India as well as in countries including the USA, Bahrain, Kenya, and the UAE. He has also worked globally as a Microsoft Certified Trainer for major corporate clients. Anand is a seasoned certified professional holding certifications in Microsoft Certified Trainer (MCT), CompTIA Security+ (SY0-401), Scrum Certified Master, ITIL V3, Certified Network Defender (CND – EC-Council), Certified Ethical Hacker (CEH – EC-Council), 70-640 Microsoft Active Directory, 70-533 Microsoft Azure Administration, 70-534 Microsoft Azure Architecture, AWS Certified Solutions Architect – Associate, AWS Certified SysOps Administrator – Associate, Google Cloud Platform – Cloud Architect (GCP), and Certified Cloud Security Knowledge (CCSK).

Anand also manages the “AWS Blackboard” channel on YouTube, where he shares content related to AWS and Azure. He encourages learners to explore, share, learn, and subscribe to the channel for more cloud and technology resources.

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