Course Outline
Day 1: Foundational Concepts and Primary Threat Vectors
Module 1: Overview of the OWASP Generative AI Security Initiative (1 hour)
Learning Objectives:
- Analyze the transition from the OWASP Top 10 to security challenges specific to generative AI.
- Navigate the OWASP GenAI Security Project ecosystem and available resources.
- Distinguish between conventional application security protocols and artificial intelligence security requirements.
Topics Covered:
- Mission statement and operational scope of the OWASP GenAI Security Project.
- Introduction to the Threat Defense COMPASS framework.
- Assessment of the AI security environment and applicable regulatory mandates.
- Comparison of attack surfaces in AI systems versus traditional web application vulnerabilities.
Practical Exercise: Configuration of the OWASP Threat Defense COMPASS utility and execution of an initial threat assessment for government use.
Module 2: OWASP Top 10 for Large Language Models - Section 1 (2.5 hours)
Learning Objectives:
- Demonstrate proficiency in the first five critical vulnerabilities affecting LLMs.
- Analyze attack vectors and exploitation methodologies.
- Execute practical mitigation strategies to address identified risks.
Topics Covered:
LLM01: Prompt Injection
- Techniques for direct and indirect prompt injection.
- Mechanisms for hidden instruction attacks and cross-prompt contamination.
- Case studies involving jailbreaking chatbots and circumvention of safety controls.
- Defense mechanisms: Input sanitization, prompt filtering, and differential privacy applications.
LLM02: Unauthorized Disclosure of Sensitive Information
- Extraction of training data and leakage of system prompts.
- Analysis of model behavior to identify potential information exposure.
- Evaluation of privacy implications and compliance with regulatory standards.
- Mitigation strategies: Output filtering, access control implementation, and data anonymization.
LLM03: Supply Chain Vulnerabilities
- Risks associated with third-party model dependencies and plugin security.
- Risks related to compromised training datasets and model poisoning.
- Risk assessment methodologies for AI component vendors.
- Best practices for secure model deployment and integrity verification.
Practical Exercise: Laboratory simulation of prompt injection attacks against vulnerable LLM applications and implementation of corresponding defensive measures.
Module 3: OWASP Top 10 for Large Language Models - Section 2 (2 hours)
Topics Covered:
LLM04: Data and Model Poisoning
- Methods for manipulating training data.
- Tactics for modifying model behavior via poisoned inputs.
- Backdoor attack vectors and data integrity verification processes.
- Preventive measures: Data validation pipelines and provenance tracking.
LLM05: Improper Handling of Outputs
- Insecure processing of content generated by LLMs.
- Risks of code injection through AI-generated content.
- Potential for cross-site scripting via AI responses.
- Frameworks for output validation and sanitization.
Practical Exercise: Simulation of data poisoning attacks and implementation of robust output validation mechanisms for government systems.
Module 4: Advanced LLM Threat Vectors (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 risks.
- Implementation of guardrails and human oversight controls.
LLM07: System Prompt Leakage
- Vulnerabilities leading to exposure of system instructions.
- Risks of credential and logic disclosure through prompts.
- Attack techniques designed to extract system prompts.
- Methods for securing system instructions and external configurations.
Practical Exercise: Design of secure agent architectures incorporating appropriate access controls and monitoring protocols.
Day 2: Advanced Threat Analysis and Implementation Strategies
Module 5: Emerging Artificial Intelligence Threats (2 hours)
Learning Objectives:
- Analyze cutting-edge security threats within the AI domain.
- Implement advanced detection and prevention techniques.
- Design resilient AI systems capable of withstanding sophisticated attacks for government operations.
Topics Covered:
LLM08: Vector and Embedding Weaknesses
- Vulnerabilities in Retrieval-Augmented Generation (RAG) systems and vector database security.
- Embedding poisoning and manipulation of similarity metrics.
- Adversarial examples in semantic search environments.
- Securing vector stores and deploying anomaly detection systems.
LLM09: Misinformation and Model Reliability
- Detection and mitigation of hallucinations.
- Management of bias amplification and fairness considerations.
- Mechanisms for fact-checking and source verification.
- Content validation and integration of human oversight.
LLM10: Unbounded Consumption
- Resource exhaustion and denial-of-service attack vectors.
- Strategies for rate limiting and resource management.
- Cost optimization and budget control measures.
- Performance monitoring and alerting system design.
Practical Exercise: Construction of a secure RAG pipeline with vector database protection and hallucination detection capabilities.
Module 6: Security for Agentic AI (2 hours)
Learning Objectives:
- Comprehend the distinct security challenges posed by autonomous AI agents.
- Apply the OWASP Agentic AI taxonomy to real-world infrastructure.
- Implement security controls for multi-agent environments relevant to government workflows.
Topics Covered:
- Introduction to Agentic AI and autonomous systems architecture.
- OWASP Agentic AI Threat Taxonomy: Agent Design, Memory, Planning, Tool Use, Deployment.
- Security and coordination risks in multi-agent systems.
- Risks involving tool misuse, memory poisoning, and goal hijacking.
- Securing agent communication channels and decision-making processes.
Practical Exercise: Threat modeling exercise utilizing the OWASP Agentic AI taxonomy on a multi-agent customer service system.
Module 7: Implementation of OWASP Threat Defense COMPASS (2 hours)
Learning Objectives:
- Demonstrate proficiency in the practical application of Threat Defense COMPASS.
- Integrate AI threat assessment into organizational security programs.
- Develop comprehensive AI risk management strategies for government use.
Topics Covered:
- Comprehensive analysis of the Threat Defense COMPASS methodology.
- Integration with the OODA Loop: Observe, Orient, Decide, Act.
- Mapping threats to MITRE ATT&CK and ATLAS frameworks.
- Development of AI Threat Resilience Strategy Dashboards.
- Integration with existing security tools and operational processes.
Practical Exercise: Execution of a complete threat assessment using COMPASS for a Microsoft Copilot deployment scenario.
Module 8: Implementation Best Practices and Operational Security (2.5 hours)
Learning Objectives:
- Design secure AI architectures based on foundational principles.
- Implement monitoring and incident response protocols for AI systems.
- Establish governance frameworks for AI security in the public sector.
Topics Covered:
Secure AI Development Lifecycle:
- Security-by-design principles for AI applications.
- Code review practices for LLM integrations.
- Testing methodologies and vulnerability scanning procedures.
- Deployment security and production environment hardening.
Monitoring and Detection:
- Logging and monitoring requirements specific to AI systems.
- Anomaly detection mechanisms for AI infrastructure.
- Incident response procedures for AI security events.
- Forensics and investigation techniques for AI-related incidents.
Governance and Compliance:
- AI risk management frameworks and policy development.
- Regulatory compliance considerations (e.g., GDPR, EU AI Act).
- Third-party risk assessment for AI vendors.
- Security awareness training programs for AI development teams.
Practical Exercise: Design of a comprehensive security architecture for an enterprise AI chatbot, including monitoring, governance, and incident response procedures for government deployment.
Module 9: Tools and Technological Ecosystems (1 hour)
Learning Objectives:
- Evaluate and implement AI security tools suitable for government use.
- Analyze the current landscape of AI security solutions.
- Establish practical detection and prevention capabilities.
Topics Covered:
- AI security tool ecosystem and vendor landscape analysis.
- 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: Demonstration of AI security testing tools and planning for implementation within government environments.
Module 10: Future Trends and Conclusion (1 hour)
Learning Objectives:
- Analyze emerging threats and future security challenges in the AI domain.
- Develop strategies for continuous learning and organizational improvement.
- Create actionable plans for advancing organizational AI security programs.
Topics Covered:
- Emerging threats: Deepfakes, advanced prompt injection, and model inversion attacks.
- Future developments and roadmap for the OWASP GenAI project.
- Building AI security communities and facilitating knowledge sharing.
- Continuous improvement and integration of threat intelligence.
Action Planning Exercise: Development of a 90-day action plan for implementing OWASP GenAI security practices within participant organizations.
Requirements
- Comprehensive knowledge of web application security standards
- Fundamental understanding of artificial intelligence and machine learning principles
- Preference for candidates with prior experience implementing security frameworks or risk assessment methodologies
Target Audience
- Cybersecurity professionals
- Artificial intelligence developers
- System architects
- Compliance officers
- Security practitioners
Testimonials (1)
I really enjoyed learning about AI attacks and the tools out there to begin practicing and actively using for security testing. I took a lot of knowledge away which I didn't have at the beginning and the course met what I hoped it would be. My favorite part shown from the training was Comet Browser and was amazed at what it could do. Definitely something will be looking into more. Overall it was a great course and enjoyed learning all OWASP GenAI Top 10.
