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GenAI Security Study Plan (GenAI/LLM)

This study plan covers all the topics, concepts, blogs, videos, books, videos, newsletters etc. by keeping GenAI security in mind.

GenAI Security Study Plan

It should take 6-9 months to be good at GenAI security so that you can do one or more of the below listed things:

  1. LLM pentesting
  2. GenAI security assessment
  3. Design and implement secure GenAI/LLM architectures for organizations.
  4. Understanding of GenAI from GRC perspective
  5. Knowledge of different GenAI security frameworks
  6. AI enabled Threat Modeling or Threat Modeling of AI systems
  7. Good grip on LLM safety, LLM Guardrails, Responsible AI, AI ethic etc.

It would help you in your current work as well as finding a new work using GenAI security skills.

Note: I am not writing anything that would require core AI/ML skills. It's all are done after keeping security focus in mind.

Important

This field is still evolving, so our repo would too! Stay tuned!

Organizational Capabilities that you can be job ready after going through mentioned study plan

Security Assessments & Audits

  • Conduct comprehensive GenAI security assessments using OWASP LLM Top 10 framework
  • Perform LLM application penetration testing and vulnerability assessments
  • Audit RAG (Retrieval Augmented Generation) implementations for security risks
  • Evaluate prompt injection and jailbreaking vulnerabilities
  • Assess model security, including adversarial attacks and data poisoning risks
  • Review AI/ML supply chain security (model provenance, dependencies, third-party APIs)

Governance, Risk & Compliance (GRC)

  • Develop GenAI security policies and procedures aligned with NIST AI RMF
  • Create AI governance frameworks and risk management strategies
  • Implement compliance controls for AI regulations (EU AI Act, etc.)
  • Establish AI ethics and responsible AI practices
  • Design AI security awareness training programs for employees
  • Create incident response plans specifically for AI/ML security incidents

Architecture & Implementation

  • Design secure AI/ML pipelines and infrastructure
  • Implement secure GenAI architectures (secure RAG, fine-tuning, inference)
  • Deploy AI security tools (LLM Guard, model scanning, prompt filtering)
  • Establish secure model deployment and MLOps practices
  • Design data privacy controls for AI training and inference data
  • Implement monitoring and logging for AI systems

Risk Management & Threat Modeling

  • Conduct AI/ML specific threat modeling exercises
  • Assess business risks associated with GenAI implementations
  • Develop risk mitigation strategies for AI adoption
  • Create AI security metrics and KPIs for organizational reporting
  • Establish AI risk registers and continuous monitoring processes

Security Engineering & DevSecOps

  • Integrate AI security into CI/CD pipelines
  • Implement security testing for AI/ML models and applications
  • Design secure model training environments and data handling processes
  • Establish model version control and security scanning practices
  • Create automated security testing for prompt injection and other LLM vulnerabilities

Incident Response & Forensics

  • Investigate AI/ML security incidents and breaches
  • Develop playbooks for AI-specific security incidents
  • Perform forensic analysis on compromised AI systems
  • Create incident classification systems for AI/ML security events

Consulting & Advisory Services

  • Provide GenAI security consulting to organizations
  • Conduct security reviews of vendor AI solutions
  • Advise on secure AI procurement and third-party risk management
  • Lead AI security transformation initiatives
  • Mentor and train internal security teams on AI security

Note

ToC will highlight GenAI based concepts and learning reqources as and when we come across some awesome learning materials.

Study Plan ToC:

  1. GenAI/LLM Fundamental Concepts - 4 weeks
  2. Prompt Engineering - 1 week
  3. RAG (Retrieval Augmented Generation) - 1-2 weeks
  4. Fine Tuning - 2 weeks
  5. AI Agents - 1 week
  6. Agentic AI - 1 week
  7. MCP (Model Context Protocol) - 1 week
  8. Certifications - on your bandwidth and wish
  9. GenAI Interview Questions
  10. GenAI Security Tools

GenAI Fundamental Concepts

Duration: 4 weeks

Week 1: AI/ML Foundations & LLM Basics

Week 2: LLM Security Fundamentals

  • OWASP LLM Top 10

    • OWASP Top 10 for LLM Applications
    • LLM01: Prompt Injection
    • LLM02: Insecure Output Handling
    • LLM03: Training Data Poisoning
    • LLM04: Model Denial of Service
    • LLM05: Supply Chain Vulnerabilities
    • LLM06: Sensitive Information Disclosure
    • LLM07: Insecure Plugin Design
    • LLM08: Excessive Agency
    • LLM09: Overreliance
    • LLM10: Model Theft

  • Common Attack Vectors

Week 3: AI Governance & Compliance

Week 4: Threat Modeling & Risk Assessment

Hands-on Practice:

Prompt Engineering

Duration: 1 week

Understanding Prompt Engineering

  • Prompt Engineering Fundamentals

    • What is prompt engineering and why it matters for security
    • Types of prompts: zero-shot, few-shot, chain-of-thought
    • Prompt structure and best practices
    • Context window limitations and management

  • Security-Focused Prompt Engineering

    • Defensive prompt engineering techniques
    • Input validation through prompts
    • Output sanitization strategies
    • Prompt injection prevention techniques

Advanced Prompt Techniques

  • Prompt Injection Attacks

    • Direct prompt injection
    • Indirect prompt injection
    • Jailbreaking techniques
    • Prompt leaking attacks

  • Defensive Strategies

    • Prompt templates and parameterization
    • Input filtering and validation
    • Output monitoring and filtering
    • Role-based prompt design

Hands-on Practice:

  • Practice prompt injection techniques on safe platforms
  • Design secure prompt templates
  • Test prompt robustness against various attack vectors

RAG (Retrieval Augmented Generation)

Duration: 1-2 weeks

Week 1: RAG Fundamentals

  • Understanding RAG Architecture

  • RAG Implementation Patterns

    • Simple RAG vs Advanced RAG
    • Multi-step reasoning with RAG
    • Hybrid search approaches
    • RAG with fine-tuned models

Week 2: RAG Security (Optional - for deeper understanding)

Hands-on Practice:

  • Build a simple RAG system with security controls
  • Test for information leakage vulnerabilities
  • Implement access controls for knowledge bases

Fine Tuning

Duration: 2 weeks

Week 1: Fine-Tuning Fundamentals

  • Understanding Fine-Tuning

    • Pre-training vs fine-tuning vs prompt engineering
    • Types of fine-tuning: full, parameter-efficient (LoRA, QLoRA)
    • When to use fine-tuning vs other approaches
    • Data requirements and preparation

  • Fine-Tuning Techniques

    • Supervised fine-tuning (SFT)
    • Reinforcement Learning from Human Feedback (RLHF)
    • Constitutional AI approaches
    • Domain-specific fine-tuning

Week 2: Fine-Tuning Security

  • Security Considerations in Fine-Tuning

    • Training data security and privacy
    • Model poisoning through fine-tuning
    • Backdoor attacks in fine-tuned models
    • Model extraction risks

  • Secure Fine-Tuning Practices

    • Data sanitization and validation
    • Secure training environments
    • Model versioning and provenance
    • Testing fine-tuned models for security

Hands-on Practice:

  • Fine-tune a small model with security considerations
  • Test for data leakage in fine-tuned models
  • Implement secure fine-tuning pipelines

AI Agents

Duration: 1 week

Understanding AI Agents

  • AI Agent Fundamentals

    • What are AI agents and how they differ from simple LLMs
    • Agent architectures: ReAct, Plan-and-Execute, Multi-agent systems
    • Tool use and function calling
    • Memory and state management in agents

  • Types of AI Agents

    • Conversational agents
    • Task-specific agents
    • Autonomous agents
    • Multi-agent systems and collaboration

AI Agent Security

  • Security Risks with AI Agents

    • Excessive agency and unauthorized actions
    • Tool misuse and privilege escalation
    • Agent-to-agent communication security
    • Persistent memory security risks

  • Securing AI Agents

    • Principle of least privilege for agents
    • Action validation and approval workflows
    • Monitoring agent behavior and decisions
    • Secure tool integration patterns

Hands-on Practice:

  • Build a simple AI agent with security controls
  • Test agent behavior under various scenarios
  • Implement monitoring for agent actions

Agentic AI

Duration: 1 week

Advanced Agentic Systems

  • Agentic AI Concepts

    • Autonomous decision-making systems
    • Goal-oriented AI behavior
    • Planning and reasoning in agentic systems
    • Human-AI collaboration patterns

  • Agentic AI Architectures

    • Multi-agent orchestration
    • Hierarchical agent systems
    • Distributed agentic networks
    • Agent communication protocols

Security in Agentic AI

  • Unique Security Challenges

    • Emergent behaviors in agentic systems
    • Goal misalignment and specification gaming
    • Inter-agent security and trust
    • Scalability of security controls

  • Governance for Agentic AI

    • Establishing boundaries and constraints
    • Monitoring and auditing agentic behavior
    • Human oversight and intervention mechanisms
    • Ethical considerations in autonomous systems

Hands-on Practice:

  • Design security controls for agentic systems
  • Analyze case studies of agentic AI failures
  • Develop monitoring strategies for autonomous agents

MCP (Model Context Protocol)

Duration: 1 week

Understanding MCP

  • MCP Fundamentals

    • What is Model Context Protocol
    • MCP architecture and components
    • Client-server communication patterns
    • Resource management and sharing

  • MCP Implementation

    • Setting up MCP servers and clients
    • Resource discovery and access
    • Tool integration through MCP
    • Context sharing between applications

MCP Security

  • Security Considerations

    • Authentication and authorization in MCP
    • Resource access control
    • Data privacy in context sharing
    • Network security for MCP communications

  • Best Practices

    • Secure MCP server deployment
    • Client-side security measures
    • Monitoring MCP interactions
    • Incident response for MCP systems

Hands-on Practice:

  • Set up a secure MCP environment
  • Implement access controls for MCP resources
  • Test MCP security configurations

Certifications

Duration: Based on your bandwidth and goals

AI/ML Security Certifications

  • Certified AI/ML Pentester

  • Cloud AI Security Certifications

    • AWS Machine Learning Specialty
    • Google Cloud Professional ML Engineer
    • Azure AI Engineer Associate
    • Focus on security aspects of cloud AI services

Vendor-Specific Certifications

  • OpenAI Safety and Alignment
  • Anthropic Constitutional AI
  • Microsoft Responsible AI
  • Google AI Ethics

Preparation Resources:

GenAI Interview Questions

Technical Questions

  • LLM Fundamentals

    • Explain the transformer architecture and its security implications
    • What are the key differences between GPT, BERT, and T5 models?
    • How do attention mechanisms work and what security risks do they pose?
    • Describe the training process of large language models

  • Security-Specific Questions

    • Walk through the OWASP LLM Top 10 and provide examples
    • How would you test an LLM application for prompt injection vulnerabilities?
    • Explain the difference between direct and indirect prompt injection
    • What are the main security considerations when implementing RAG?
    • How would you secure a fine-tuning pipeline?

Scenario-Based Questions

  • Risk Assessment Scenarios

    • "A company wants to implement a customer service chatbot using GPT-4. What security risks would you identify?"
    • "How would you conduct a security assessment of an existing LLM application?"
    • "Design a secure architecture for a RAG-based document Q&A system"

  • Incident Response Scenarios

    • "An LLM application is leaking sensitive customer data. How would you investigate?"
    • "Users report that the chatbot is providing inappropriate responses. What's your approach?"
    • "A competitor seems to have extracted your fine-tuned model. How do you respond?"

Governance and Compliance

  • Regulatory Questions
    • How does the EU AI Act impact LLM deployments?
    • What are the key components of NIST AI RMF?
    • How would you implement AI governance in an organization?
    • What metrics would you use to measure AI security posture?

Hands-on Technical Challenges

  • Practical Exercises
    • Demonstrate prompt injection techniques
    • Show how to implement LLM Guard or similar tools
    • Explain model scanning and vulnerability detection
    • Design monitoring and alerting for LLM applications

GenAI Security Tools

Open Source Security Tools

  • LLM Guard by ProtectAI

    • GitHub Repository
    • Playground
    • Input/output filtering and sanitization
    • Prompt injection detection
    • Sensitive data detection and redaction

  • Model Scanning Tools

    • ModelScan by ProtectAI
    • Scans AI/ML models for security vulnerabilities
    • Detects malicious code in model files
    • Supports multiple model formats

  • AI/ML Exploit Tools

    • AI Exploits by ProtectAI
    • Collection of AI/ML security exploits
    • Educational and testing purposes
    • Demonstrates common attack vectors

Commercial Security Platforms

  • Lakera Guard

    • Real-time LLM security monitoring
    • Prompt injection detection
    • Content filtering and moderation
    • API-based integration

  • Robust Intelligence

    • AI security and monitoring platform
    • Model validation and testing
    • Continuous monitoring for drift and attacks
    • Enterprise-grade security controls

  • WhyLabs

    • ML monitoring and observability
    • Data drift detection
    • Model performance monitoring
    • Security-focused analytics

Testing and Assessment Tools

  • Garak

    • LLM vulnerability scanner
    • Automated testing for various attack types
    • Extensible framework for custom tests
    • Community-driven development

  • PromptFoo

    • LLM evaluation and testing framework
    • Security-focused test cases
    • Automated red teaming capabilities
    • Integration with CI/CD pipelines

Bug Bounty and Research Platforms

Monitoring and Observability

  • LangSmith

    • LLM application monitoring
    • Trace analysis and debugging
    • Performance and security metrics
    • Integration with LangChain

  • Weights & Biases

    • ML experiment tracking
    • Model monitoring and versioning
    • Security-focused metrics and alerts
    • Team collaboration features

Cloud-Native Security Tools

  • AWS Bedrock Guardrails

    • Content filtering and safety controls
    • Custom guardrail policies
    • Real-time monitoring and blocking
    • Integration with AWS services

  • Azure AI Content Safety

    • Content moderation and filtering
    • Custom classification models
    • API-based integration
    • Multi-language support

  • Google Cloud AI Platform Security

    • Model security scanning
    • Access controls and IAM
    • Audit logging and monitoring
    • Compliance reporting

Implementation Checklist

  • Evaluate tools based on your specific use case
  • Set up monitoring and alerting for LLM applications
  • Implement input/output filtering and validation
  • Deploy model scanning in CI/CD pipelines
  • Establish incident response procedures
  • Regular security assessments and penetration testing
  • Stay updated with latest tools and techniques

Additional Resources

  1. Courses & University Materials

  2. Security Guides & Checklists

  3. Articles & Blogs

  4. Tools & Platforms

  5. Challenges & CTFs

  6. Videos