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 the traditional OWASP Top 10 to GenAI-specific security challenges.
• Explore the ecosystem and resources available through the OWASP GenAI Security Project.
• Identify the key differences between traditional application security and AI security.

Topics Covered:

• An overview of the mission and scope of the OWASP GenAI Security Project.
• Introduction to the Threat Defense COMPASS framework.
• Navigating the AI security landscape and understanding regulatory requirements.
• Comparing AI attack surfaces with traditional web application vulnerabilities.

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

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

Learning Objectives:

• Master the first five critical vulnerabilities specific to LLMs.
• Understand various attack vectors and exploitation techniques.
• Apply practical mitigation strategies.

Topics Covered:

LLM01: Prompt Injection

• Techniques for direct and indirect prompt injection.
• Hidden instruction attacks and cross-prompt contamination.
• Practical examples, including jailbreaking chatbots and bypassing safety measures.
• Defense strategies such as input sanitization, prompt filtering, and differential privacy.

LLM02: Sensitive Information Disclosure

• Extracting training data and system prompt leakage.
• Analyzing model behavior to identify sensitive information exposure risks.
• Considering privacy implications and regulatory compliance.
• Mitigation techniques including output filtering, access controls, and data anonymization.

LLM03: Supply Chain Vulnerabilities

• Managing third-party model dependencies and plugin security.
• Risks associated with compromised training datasets and model poisoning.
• Conducting vendor risk assessments for AI components.
• Best practices for secure model deployment and verification.

Practical Exercise: A 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

• Techniques for manipulating training data.
• Modifying model behavior through poisoned inputs.
• Backdoor attacks and verifying data integrity.
• Prevention strategies including data validation pipelines and provenance tracking.

LLM05: Improper Output Handling

• Insecure processing of content generated by LLMs.
• Code injection risks arising from AI-generated outputs.
• Cross-site scripting (XSS) vulnerabilities via AI responses.
• Frameworks for output validation and sanitization.

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

• Risks associated with autonomous decision-making and boundary violations.
• Managing agent authority and permissions.
• Preventing unintended system interactions and privilege escalation.
• Implementing guardrails and human oversight controls.

LLM07: System Prompt Leakage

• Vulnerabilities related to the exposure of system instructions.
• Credential and logic disclosure risks through prompts.
• Techniques used to extract system prompts during attacks.
• Securing system instructions and external configurations.

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

Day 2: Advanced Threats and Implementation

Module 5: Emerging AI Threats (2 hours)

Learning Objectives:

• Understand cutting-edge security threats in the AI domain.
• Implement advanced detection and prevention techniques.
• Design resilient AI systems capable of withstanding sophisticated attacks.

Topics Covered:

LLM08: Vector and Embedding Weaknesses

• Vulnerabilities in RAG systems and vector database security.
• Embedding poisoning and attacks involving similarity manipulation.
• Adversarial examples within semantic search contexts.
• Securing vector stores and implementing anomaly detection.

LLM09: Misinformation and Model Reliability

• Detecting and mitigating hallucinations.
• Addressing bias amplification and ensuring fairness.
• Mechanisms for fact-checking and source verification.
• Integrating content validation with human oversight.

LLM10: Unbounded Consumption

• Resource exhaustion and denial-of-service (DoS) attacks.
• Strategies for rate limiting and resource management.
• Cost optimization and budget controls.
• Implementing 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 posed by 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.
• The OWASP Agentic AI Threat Taxonomy covering Agent Design, Memory, Planning, Tool Use, and Deployment.
• Risks associated with security and coordination in multi-agent systems.
• Attacks involving tool misuse, memory poisoning, and goal hijacking.
• Securing agent communication channels and decision-making processes.

Practical Exercise: Conducting a threat modeling exercise using the 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 the Threat Defense COMPASS framework.
• Integrate AI threat assessment into broader organizational security programs.
• Develop comprehensive AI risk management strategies.

Topics Covered:

• In-depth exploration of the Threat Defense COMPASS methodology.
• Integration with the OODA Loop: Observe, Orient, Decide, Act.
• Mapping threats to MITRE ATT&CK and ATLAS frameworks.
• Building AI Threat Resilience Strategy Dashboards.
• Integrating with existing security tools and processes.

Practical Exercise: Performing a 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 protocols for AI systems.
• Create governance frameworks to support 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 techniques 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 (e.g., GDPR, AI Act).
• Third-party risk assessment for AI vendors.
• Security awareness training for AI development teams.

Practical Exercise: Designing 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 landscape of AI security solutions.
• Build practical detection and prevention capabilities.

Topics Covered:

• The 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.
• Criteria for tool selection 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 strategies for continuous learning and improvement.
• Create action plans for organizational AI security programs.

Topics Covered:

• Emerging threats: Deepfakes, advanced prompt injection, model inversion.
• Future developments and roadmap for the OWASP GenAI project.
• Building AI security communities and facilitating knowledge sharing.
• Continuous improvement and integrating threat intelligence.

Action Planning Exercise: Developing a 90-day action plan to implement OWASP GenAI security practices within participants' organizations.