OWASP GenAI Security Training Course

Day 1: Foundations and Core Threats

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

Learning Objectives:

• Understand the evolution from the OWASP Top 10 to the specific security challenges of Generative AI (GenAI).
• Explore the resources and ecosystem provided by the OWASP GenAI Security Project.
• Identify key differences between traditional application security and AI-specific security concerns.

Topics Covered:

• Overview of the mission and scope of the OWASP GenAI Security Project.
• Introduction to the Threat Defense COMPASS framework for AI security.
• Understanding the regulatory requirements and the broader AI security landscape.
• Comparison of AI attack surfaces with traditional web application vulnerabilities.

Practical Exercise: Setting up the OWASP Threat Defense COMPASS tool and conducting an initial threat assessment for government use cases.

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

Learning Objectives:

• Master the first five critical vulnerabilities in Large Language Models (LLMs).
• Understand attack vectors and exploitation techniques for these vulnerabilities.
• Apply practical mitigation strategies to address these threats.

Topics Covered:

LLM01: Prompt Injection

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

LLM02: Sensitive Information Disclosure

• Extraction of training data and system prompts.
• Analysis of model behavior to identify sensitive information exposure.
• Privacy implications and regulatory compliance considerations.
• Mitigation techniques, such as output filtering, access controls, and data anonymization.

LLM03: Supply Chain Vulnerabilities

• Security of third-party models and plugins.
• Risks associated with compromised training datasets and model poisoning.
• Vendor risk assessment for AI components.
• Best practices for secure model deployment and verification.

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

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

LLM05: Improper Output Handling

• Insecure processing of LLM-generated content.
• Code injection through AI-generated outputs.
• Cross-site scripting via AI responses.
• Frameworks for output validation and sanitization.

Practical Exercise: Simulating data poisoning attacks and implementing robust output validation mechanisms for government systems.

Module 4: Advanced LLM Threats (1.5 hours)

Topics Covered:

LLM06: Excessive Agency

• Risks associated with autonomous decision-making and boundary violations.
• Management of agent authority and permissions.
• Unintended system interactions and privilege escalation.
• Implementation of guardrails and human oversight controls.

LLM07: System Prompt Leakage

• Vulnerabilities related to the exposure of system instructions.
• Techniques for credential and logic disclosure through prompts.
• Methods for extracting system prompts.
• Strategies for securing system instructions and external configurations.

Practical Exercise: Designing secure agent architectures with appropriate access controls and monitoring for government applications.

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 for these threats.
• Design resilient AI systems to withstand sophisticated attacks.

Topics Covered:

LLM08: Vector and Embedding Weaknesses

• Vulnerabilities in RAG systems and vector database security.
• Embedding poisoning and similarity manipulation attacks.
• Adversarial examples in semantic search.
• Techniques for securing vector stores and implementing anomaly detection.

LLM09: Misinformation and Model Reliability

• Detection and mitigation of hallucination in AI models.
• Considerations for bias amplification and fairness.
• Mechanisms for fact-checking and source verification.
• Integration of content validation and human oversight.

LLM10: Unbounded Consumption

• Risks of resource exhaustion and denial-of-service attacks.
• Strategies for rate limiting and resource management.
• Cost optimization and budget controls.
• Performance monitoring and alerting systems.

Practical Exercise: Building a secure RAG pipeline with vector database protection and hallucination detection for government applications.

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 for government use.
• Implement security controls in multi-agent environments.

Topics Covered:

• Introduction to Agentic AI and autonomous systems.
• OWASP Agentic AI Threat Taxonomy, covering Agent Design, Memory, Planning, Tool Use, and Deployment.
• Security risks in multi-agent system coordination.
• Attacks involving tool misuse, memory poisoning, and goal hijacking.
• Strategies for securing agent communication and decision-making processes.

Practical Exercise: Threat modeling exercise using the OWASP Agentic AI taxonomy on a multi-agent customer service system for government operations.

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

Learning Objectives:

• Master the practical application of the Threat Defense COMPASS methodology.
• Integrate AI threat assessment into organizational security programs for government agencies.
• 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 frameworks such as MITRE ATT&CK and ATLAS.
• Building AI Threat Resilience Strategy Dashboards.
• Integration with existing security tools and processes for government agencies.

Practical Exercise: Complete threat assessment using COMPASS for a Microsoft Copilot deployment scenario in a government setting.

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

Learning Objectives:

• Design secure AI architectures from the ground up for government applications.
• Implement monitoring and incident response strategies for AI systems in government environments.
• Create governance frameworks to ensure AI security compliance in government operations.

Topics Covered:

Secure AI Development Lifecycle:

• Security-by-design principles for AI applications in government settings.
• Code review practices for LLM integrations in government systems.
• Testing methodologies and vulnerability scanning techniques.
• Deployment security and production hardening strategies for government AI systems.

Monitoring and Detection:

• Logging and monitoring requirements specific to AI systems in government environments.
• Anomaly detection techniques for AI systems used by government agencies.
• Incident response procedures tailored to AI security events in government operations.
• Forensics and investigation techniques for government AI systems.

Governance and Compliance:

• Risk management frameworks and policies for AI security in government agencies.
• Considerations for regulatory compliance, such as GDPR and the AI Act, in government operations.
• Third-party risk assessment for AI vendors used by government entities.
• Security awareness training programs for government AI development teams.

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

Module 9: Tools and Technologies (1 hour)

Learning Objectives:

• Evaluate and implement AI security tools suitable for government applications.
• Understand the current landscape of AI security solutions.
• Build practical detection and prevention capabilities for government AI systems.

Topics Covered:

• Overview of the AI security tool ecosystem and vendor landscape.
• Open-source security tools, such as Garak, PyRIT, and Giskard.
• Commercial solutions for AI security and monitoring in government environments.
• Integration patterns and deployment strategies for government use cases.
• Criteria and evaluation frameworks for selecting AI security tools.

Practical Exercise: Hands-on demonstration of AI security testing tools and implementation planning for government systems.

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

Learning Objectives:

• Understand emerging threats and future security challenges in AI for government applications.
• Develop continuous learning and improvement strategies for government AI security programs.
• Create action plans to implement OWASP GenAI security practices in government organizations.

Topics Covered:

• Emerging threats, including deepfakes, advanced prompt injection, and model inversion.
• Future developments and roadmap for the OWASP GenAI project.
• Building AI security communities and knowledge sharing platforms for government agencies.
• Strategies for continuous improvement and threat intelligence integration in government operations.

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