Top 10 LLM Security Risks with Solutions

Price : $ 2500

Duration: 4 Days

Technology: Security

Delivery Method: Online Live

Audience: Intermediate–advanced developers, architects, and security engineers

Level: Advanced

Course Description

This intensive, 4-day hands-on workshop provides a deep dive into the evolving threat landscape of Large Language Models (LLMs), focusing on the updated 2025 OWASP Top 10 for LLM Applications. The course bridges the gap between traditional application security and AI-native security, covering risks from input manipulation to architectural vulnerabilities. Participants will learn to identify, exploit, and mitigate critical flaws in LLM-powered applications, including RAG systems and autonomous agents. The training emphasizes a practical “offensive-defensive” approach, featuring labs for prompt injection, data poisoning, excessive agency, and securing output handlers.

Course Objectives

By the end of this course, participants will be able to:

Analyze and mitigate the 2025 OWASP Top 10 for LLM risks (LLM01-LLM10).
Defend against Direct, Indirect, and Multi-hop Prompt Injections.
Secure against Sensitive Information Disclosure in training and conversation data.
Apply Threat Modeling to LLM+Tool+RAG architectures.
Protect the AI Supply Chain (tampered models, compromised tokenizers).
Prevent Data and Model Poisoning in fine-tuning and RAG sources.
Implement secure Output Handling to stop downstream injections.
Constrain Excessive Agency in autonomous agents.
Conduct Red Teaming for system prompt leakage.
Mitigate Vector/Embedding Weaknesses and Unbounded Consumption.
Design a Layered Defense for Enterprise LLM Architecture.

Course Audience

Security Engineers & Application Security Specialists looking to expand into AI Security.
AI/ML Engineers & Data Scientists responsible for securing model deployment.
DevSecOps Engineers & Architects designing RAG systems and Agentic workflows.
Technical Product Managers needing to understand AI risk management.

Lesson 1: Foundations: LLM Security Architecture & Threat Landscape

Learning Objectives

Understand LLM system architecture and attack surfaces
Identify where LLMs break traditional security assumptions

Topics

LLM pipelines and components
Threat surfaces unique to LLMs
Overview of the Top 10 risks

Threat Modeling for LLM Systems

Learning Objectives

Build threat models for LLM‑enabled applications
Identify systemic weaknesses

Topics

STRIDE for LLMs
Data flow diagrams for AI systems
Trust boundaries in agentic architectures

Lesson 2: LLM01: Prompt Injection

Direct, indirect, multi‑stage injection
Multi‑agent interference
Jailbreak taxonomies

Lab: Break a tool‑enabled agent

Lesson 3: LLM02:2025 Sensitive Information Disclosure

Training data vs conversation data vs system data
Prompt‑based extraction and reconstruction
Memorization risks

Lab: Extract seeded secrets from a demo model/app

Lesson 4: Threat Modeling (Cross Risk)

Learning Objectives

Understand how LLM output becomes an attack vector
Prevent downstream systems from blindly trusting model output

Topics

LLM‑generated SQL, code, commands
Output injection into downstream systems
Output validation patterns

Lesson 5: LLM03: Training Data Poisoning

Learning Objectives

Risks in model weights, tokenizers, libraries, datasets
Third‑party model hubs and pre‑built agents
Trust boundaries and provenance

Topics

Compromised tokenizers
Dependency attacks in AI pipelines

Lab: Analyze a “tampered” model/toolchain and spot anomalies

Lesson 6: LLM04: Data and Model Poisoning - Training Data Poisoning

Learning Objectives

Understand poisoning vectors in fine‑tuning, RAG, and embeddings
Detect and mitigate poisoning attempts

Topics

Direct poisoning (fine‑tuning datasets)
Indirect poisoning (RAG sources, public data)
Poisoning indicators

Lesson 7: LLM05:2025 Improper Output Handling

LLM output as untrusted data
Injection into SQL, shell, HTML, other downstream systems
Output validation, schemas, and policy layers

Lab: Exploit improper output handling in a mock app

Lesson 8: LLM06:2025 Excessive Agency

Over‑delegation to autonomous agents
Dangerous tool combinations and action chains
Guardrails, approvals, and human‑in‑the‑loop patterns
Lab: Exploit an over‑powered agent and then constrain it

Lesson 9: LLM07:2025 System Prompt Leakage

System prompts, hidden instructions, and configuration secrets
Leakage via logs, error messages, and model responses
Red‑teaming for prompt leakage
Lab: Extract system prompt fragments from a demo app

Lesson 10: LLM08:2025 Vector and Embedding Weaknesses

How embeddings work conceptually
Adversarial documents and embedding collisions
Poisoning and evasion via vector space manipulation
Lab: Insert adversarial content into a vector store and bypass filters

Lesson 11: LLM09:2025 Misinformation

Hallucinations vs targeted misinformation
Abuse of LLMs to generate or amplify false content
Verification, grounding, and fact‑checking layers
Lab: Drive a model into misinformation and then constrain it with checks

Lesson 12: LLM10:2025 Unbounded Consumption

Token amplification and long‑context abuse
Cost, latency, and resource exhaustion
Rate limiting, quotas, and guardrails on tool calls
Lab: Trigger unbounded consumption patterns and implement limits

Lesson 13: Enterprise Grade LLM Security Architecture (Cross Risk)

Layered defenses across all LLM01–LLM10:2025
Secure prompt pipelines, policy engines, monitoring
Zero‑trust mindset for LLMs and agents

Course Prerequisites

To maximize the value of this advanced course, participants should have:

Basic Understanding of LLMs: Familiarity with prompting, tokenization, and RAG architectures (retrieval-augmented generation).
Programming Experience: Proficiency in Python is required for lab exercises.
Security Fundamentals: A foundational understanding of web application security (e.g., HTTP, SQL injection, API security) is essential.

Course Schedule

Course Name	Date	Time
Course NameTop 10 LLM Security Risks with Solutions	Date05/04/2026 - 05/08/2026	Time09:00 AM-05:00 PM

Upcoming Training

CompTIA Network+ Certification

Mar 09-13

Tableau Fundamentals

Mar 10-14

CompTIA Network+ Certification

Apr 13-17

Angular - Converting..

Apr 13-13

Angular - Converting..

Apr 16-16