AI Security Engineering: LLM Hacking & Prompt Injection

🤖 AI + SECURITY

AI Model Hacking • Prompt Injection • Jailbreaks • RAG & Agent Exploitation

AI Security Prompt Injection LLM Hacking RAG Attacks AI Red Team

🎯 Course Objective

  • Understand how modern AI & LLM systems actually work internally
  • Exploit prompt injection, jailbreaks & instruction bypass techniques
  • Attack real-world AI apps, RAG systems & AI agents
  • Defend AI models used in startups & enterprises
  • Become industry-ready as an AI Security / Red Team professional

🚀 What You Will Be Able To Do

  • Hack AI chatbots without touching source code
  • Extract hidden system prompts & internal instructions
  • Poison RAG knowledge bases silently
  • Abuse AI agents & tool-calling features
  • Secure AI systems against real-world attacks

📘 Course Modules (20 Modules)

Module 1 – Introduction to AI & LLM Security

  • AI vs ML vs LLM explained simply
  • Why AI security is the next hacking frontier
  • Real-world AI breaches

Module 2 – How Large Language Models Work

  • Transformers & attention basics
  • Tokens, embeddings & inference
  • Why hallucination happens

Module 3 – Real-World AI Application Architecture

  • Chatbot & API-based systems
  • RAG pipelines
  • AI agents & tools

Module 4 – Threat Modeling AI Systems

  • OWASP Top 10 for LLMs
  • Trust boundaries in AI apps
  • Attacker mindset

Module 5 – Prompt Engineering (Offensive View)

  • System vs developer vs user prompts
  • Instruction hierarchy abuse
  • Role manipulation

Module 6 – Prompt Injection Fundamentals

  • Direct prompt injection
  • Instruction override attacks
  • Why filters fail

Module 7 – Advanced Prompt Injection & Jailbreaks

  • Multi-step jailbreaks
  • Role-play exploits
  • Context confusion attacks

Module 8 – Data Leakage & System Prompt Extraction

  • Hidden system prompt leaks
  • Training data exposure risks
  • Sensitive data extraction

Module 9 – Indirect Prompt Injection

  • Injection via PDFs & documents
  • Stored prompt injection
  • Web-based attacks

Module 10 – RAG (Retrieval Augmented Generation) Attacks

  • RAG internals explained
  • Knowledge base poisoning
  • Context hijacking

Module 11 – AI Agent Hacking

  • Agent architecture
  • Tool abuse & command execution
  • Privilege escalation via agents

Module 12 – Plugin & Tool Exploitation

  • Third-party tool risks
  • Permission abuse
  • API chaining attacks

Module 13 – AI API Attacks

  • Rate-limit bypass
  • Token abuse
  • Cost exhaustion attacks

Module 14 – Model Inversion & Privacy Attacks

  • Membership inference
  • Training data recovery
  • Privacy risks

Module 15 – Adversarial Prompt Attacks

  • Adversarial inputs
  • Prompt obfuscation
  • Model confusion techniques

Module 16 – AI Malware & Weaponization Risks

  • AI-generated malware concepts
  • Automation of cybercrime
  • Defensive awareness

Module 17 – Defending Against Prompt Injection

  • Prompt hardening
  • Output validation
  • Defense-in-depth

Module 18 – AI Security Monitoring

  • Detecting malicious prompts
  • Logging & alerting
  • AI SOC concepts

Module 19 – AI Red Teaming & Bug Bounty

  • AI red team methodology
  • Safe testing practices
  • Reporting AI vulnerabilities

Module 20 – Capstone Project (Expert)

  • Build a vulnerable AI app
  • Exploit & defend it end-to-end
  • Create a professional AI security report
⚠️ All demonstrations are performed in isolated lab environments only. This course focuses on responsible AI security research and defense. Unauthorized testing on live systems is illegal.

🧰 Tools & Technologies Covered

  • OpenAI / Anthropic / Gemini style LLM APIs
  • Custom AI Chatbot & LLM Applications
  • LangChain (Agents, Tools, Chains)
  • LlamaIndex (RAG pipelines)
  • Vector Databases (FAISS, Chroma, Pinecone concepts)
  • Prompt Injection Testing Frameworks
  • Python for AI Security Testing
  • Burp Suite for AI API Testing
  • Postman for LLM API Abuse
  • Custom Prompt Fuzzers & Payloads
  • RAG Poisoning Scripts
  • AI Agent Tool Abuse Labs
  • Logging & Monitoring Tools for AI Systems
  • Basic ML Model Evaluation Utilities
  • GitHub AI Security Research Repositories

🎓 Career Outcomes After This Course

  • AI Security Researcher
  • AI Red Team Engineer
  • Prompt Injection Specialist
  • LLM Security Analyst
  • AI Application Security Consultant
  • Bug Bounty Hunter (AI Programs)
  • AI Risk & Compliance Analyst
  • Security Engineer for AI Startups
  • AI Product Security Engineer
  • Independent AI Security Researcher

🔥 Why This AI Security Course Is Different

  • Not theory – real attack & defense labs
  • Designed for hackers & security professionals
  • Covers both offensive & defensive AI security
  • Rare niche with massive future demand
  • Perfect addon with Ethical Hacking & SS7 skills
  • Industry-ready & research-oriented syllabus

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