OWASP GenAI Security Training Course

Day 1: Foundations and Core Threats

Module 1: Introduction to OWASP GenAI Security Project (1 hour)

Learning Objectives:

• Understand the evolution from OWASP Top 10 to GenAI-specific security challenges

• Explore the OWASP GenAI Security Project ecosystem and resources

• Identify key differences between traditional application security and AI security

Topics Covered:

• Overview of OWASP GenAI Security Project mission and scope

• Introduction to the Threat Defense COMPASS framework

• Understanding the AI security landscape and regulatory requirements

• AI attack surfaces vs traditional web application vulnerabilities

Practical Exercise: Setting up the OWASP Threat Defense COMPASS tool and performing initial threat assessment

Module 2: OWASP Top 10 for LLMs - Part 1 (2.5 hours)

Learning Objectives:

• Master the first five critical LLM vulnerabilities

• Understand attack vectors and exploitation techniques

• Apply practical mitigation strategies

Topics Covered:

LLM01: Prompt Injection

• Direct and indirect prompt injection techniques

• Hidden instruction attacks and cross-prompt contamination

• Practical examples: Jailbreaking chatbots and bypassing safety measures

• Defense strategies: Input sanitization, prompt filtering, differential privacy

LLM02: Sensitive Information Disclosure

• Training data extraction and system prompt leakage

• Model behavior analysis for sensitive information exposure

• Privacy implications and regulatory compliance considerations

• Mitigation: Output filtering, access controls, data anonymization

LLM03: Supply Chain Vulnerabilities

• Third-party model dependencies and plugin security

• Compromised training datasets and model poisoning

• Vendor risk assessment for AI components

• Secure model deployment and verification practices

Practical Exercise: Hands-on lab demonstrating prompt injection attacks against vulnerable LLM applications and implementing defensive measures

Module 3: OWASP Top 10 for LLMs - Part 2 (2 hours)

Topics Covered:

LLM04: Data and Model Poisoning

• Training data manipulation techniques

• Model behavior modification through poisoned inputs

• Backdoor attacks and data integrity verification

• Prevention: Data validation pipelines, provenance tracking

LLM05: Improper Output Handling

• Insecure processing of LLM-generated content

• Code injection through AI-generated outputs

• Cross-site scripting via AI responses

• Output validation and sanitization frameworks

Practical Exercise: Simulating data poisoning attacks and implementing robust output validation mechanisms

Module 4: Advanced LLM Threats (1.5 hours)

Topics Covered:

LLM06: Excessive Agency

• Autonomous decision-making risks and boundary violations

• Agent authority and permission management

• Unintended system interactions and privilege escalation

• Implementing guardrails and human oversight controls

LLM07: System Prompt Leakage

• System instruction exposure vulnerabilities

• Credential and logic disclosure through prompts

• Attack techniques for extracting system prompts

• Securing system instructions and external configuration

Practical Exercise: Designing secure agent architectures with appropriate access controls and monitoring

Day 2: Advanced Threats and Implementation

Module 5: Emerging AI Threats (2 hours)

Learning Objectives:

• Understand cutting-edge AI security threats

• Implement advanced detection and prevention techniques

• Design resilient AI systems against sophisticated attacks

Topics Covered:

LLM08: Vector and Embedding Weaknesses

• RAG system vulnerabilities and vector database security

• Embedding poisoning and similarity manipulation attacks

• Adversarial examples in semantic search

• Securing vector stores and implementing anomaly detection

LLM09: Misinformation and Model Reliability

• Hallucination detection and mitigation

• Bias amplification and fairness considerations

• Fact-checking and source verification mechanisms

• Content validation and human oversight integration

LLM10: Unbounded Consumption

• Resource exhaustion and denial-of-service attacks

• Rate limiting and resource management strategies

• Cost optimization and budget controls

• Performance monitoring and alerting systems

Practical Exercise: Building a secure RAG pipeline with vector database protection and hallucination detection

Module 6: Agentic AI Security (2 hours)

Learning Objectives:

• Understand the unique security challenges of autonomous AI agents

• Apply the OWASP Agentic AI taxonomy to real-world systems

• Implement security controls for multi-agent environments

Topics Covered:

• Introduction to Agentic AI and autonomous systems

• OWASP Agentic AI Threat Taxonomy: Agent Design, Memory, Planning, Tool Use, Deployment

• Multi-agent system security and coordination risks

• Tool misuse, memory poisoning, and goal hijacking attacks

• Securing agent communication and decision-making processes

Practical Exercise: Threat modeling exercise using OWASP Agentic AI taxonomy on a multi-agent customer service system

Module 7: OWASP Threat Defense COMPASS Implementation (2 hours)

Learning Objectives:

• Master the practical application of Threat Defense COMPASS

• Integrate AI threat assessment into organizational security programs

• Develop comprehensive AI risk management strategies

Topics Covered:

• Deep dive into Threat Defense COMPASS methodology

• OODA Loop integration: Observe, Orient, Decide, Act

• Mapping threats to MITRE ATT&CK and ATLAS frameworks

• Building AI Threat Resilience Strategy Dashboards

• Integration with existing security tools and processes

Practical Exercise: Complete threat assessment using COMPASS for a Microsoft Copilot deployment scenario

Module 8: Practical Implementation and Best Practices (2.5 hours)

Learning Objectives:

• Design secure AI architectures from the ground up

• Implement monitoring and incident response for AI systems

• Create governance frameworks for AI security

Topics Covered:

Secure AI Development Lifecycle:

• Security-by-design principles for AI applications

• Code review practices for LLM integrations

• Testing methodologies and vulnerability scanning

• Deployment security and production hardening

Monitoring and Detection:

• AI-specific logging and monitoring requirements

• Anomaly detection for AI systems

• Incident response procedures for AI security events

• Forensics and investigation techniques

Governance and Compliance:

• AI risk management frameworks and policies

• Regulatory compliance considerations (GDPR, AI Act, etc.)

• Third-party risk assessment for AI vendors

• Security awareness training for AI development teams

Practical Exercise: Design a complete security architecture for an enterprise AI chatbot including monitoring, governance, and incident response procedures

Module 9: Tools and Technologies (1 hour)

Learning Objectives:

• Evaluate and implement AI security tools

• Understand the current AI security solutions landscape

• Build practical detection and prevention capabilities

Topics Covered:

• AI security tool ecosystem and vendor landscape

• Open-source security tools: Garak, PyRIT, Giskard

• Commercial solutions for AI security and monitoring

• Integration patterns and deployment strategies

• Tool selection criteria and evaluation frameworks

Practical Exercise: Hands-on demonstration of AI security testing tools and implementation planning

Module 10: Future Trends and Wrap-up (1 hour)

Learning Objectives:

• Understand emerging threats and future security challenges

• Develop continuous learning and improvement strategies

• Create action plans for organizational AI security programs

Topics Covered:

• Emerging threats: Deepfakes, advanced prompt injection, model inversion

• Future OWASP GenAI project developments and roadmap

• Building AI security communities and knowledge sharing

• Continuous improvement and threat intelligence integration

Action Planning Exercise: Develop a 90-day action plan for implementing OWASP GenAI security practices in participants' organizations