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Eğitim İçeriği
Day 1: Foundations and Core Threats
Module 1: Introduction to OWASP GenAI Security Project (1 hour)
Learning Objectives:
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Understand the evolution from OWASP Top 10 to GenAI-specific security challenges
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Explore the OWASP GenAI Security Project ecosystem and resources
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Identify key differences between traditional application security and AI security
Topics Covered:
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Overview of OWASP GenAI Security Project mission and scope
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Introduction to the Threat Defense COMPASS framework
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Understanding the AI security landscape and regulatory requirements
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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:
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Master the first five critical LLM vulnerabilities
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Understand attack vectors and exploitation techniques
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Apply practical mitigation strategies
Topics Covered:
LLM01: Prompt Injection
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Direct and indirect prompt injection techniques
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Hidden instruction attacks and cross-prompt contamination
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Practical examples: Jailbreaking chatbots and bypassing safety measures
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Defense strategies: Input sanitization, prompt filtering, differential privacy
LLM02: Sensitive Information Disclosure
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Training data extraction and system prompt leakage
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Model behavior analysis for sensitive information exposure
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Privacy implications and regulatory compliance considerations
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Mitigation: Output filtering, access controls, data anonymization
LLM03: Supply Chain Vulnerabilities
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Third-party model dependencies and plugin security
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Compromised training datasets and model poisoning
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Vendor risk assessment for AI components
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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
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Training data manipulation techniques
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Model behavior modification through poisoned inputs
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Backdoor attacks and data integrity verification
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Prevention: Data validation pipelines, provenance tracking
LLM05: Improper Output Handling
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Insecure processing of LLM-generated content
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Code injection through AI-generated outputs
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Cross-site scripting via AI responses
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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
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Autonomous decision-making risks and boundary violations
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Agent authority and permission management
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Unintended system interactions and privilege escalation
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Implementing guardrails and human oversight controls
LLM07: System Prompt Leakage
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System instruction exposure vulnerabilities
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Credential and logic disclosure through prompts
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Attack techniques for extracting system prompts
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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:
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Understand cutting-edge AI security threats
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Implement advanced detection and prevention techniques
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Design resilient AI systems against sophisticated attacks
Topics Covered:
LLM08: Vector and Embedding Weaknesses
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RAG system vulnerabilities and vector database security
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Embedding poisoning and similarity manipulation attacks
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Adversarial examples in semantic search
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Securing vector stores and implementing anomaly detection
LLM09: Misinformation and Model Reliability
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Hallucination detection and mitigation
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Bias amplification and fairness considerations
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Fact-checking and source verification mechanisms
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Content validation and human oversight integration
LLM10: Unbounded Consumption
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Resource exhaustion and denial-of-service attacks
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Rate limiting and resource management strategies
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Cost optimization and budget controls
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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:
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Understand the unique security challenges of autonomous AI agents
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Apply the OWASP Agentic AI taxonomy to real-world systems
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Implement security controls for multi-agent environments
Topics Covered:
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Introduction to Agentic AI and autonomous systems
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OWASP Agentic AI Threat Taxonomy: Agent Design, Memory, Planning, Tool Use, Deployment
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Multi-agent system security and coordination risks
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Tool misuse, memory poisoning, and goal hijacking attacks
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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:
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Master the practical application of Threat Defense COMPASS
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Integrate AI threat assessment into organizational security programs
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Develop comprehensive AI risk management strategies
Topics Covered:
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Deep dive into Threat Defense COMPASS methodology
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OODA Loop integration: Observe, Orient, Decide, Act
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Mapping threats to MITRE ATT&CK and ATLAS frameworks
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Building AI Threat Resilience Strategy Dashboards
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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:
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Design secure AI architectures from the ground up
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Implement monitoring and incident response for AI systems
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Create governance frameworks for AI security
Topics Covered:
Secure AI Development Lifecycle:
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Security-by-design principles for AI applications
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Code review practices for LLM integrations
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Testing methodologies and vulnerability scanning
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Deployment security and production hardening
Monitoring and Detection:
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AI-specific logging and monitoring requirements
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Anomaly detection for AI systems
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Incident response procedures for AI security events
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Forensics and investigation techniques
Governance and Compliance:
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AI risk management frameworks and policies
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Regulatory compliance considerations (GDPR, AI Act, etc.)
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Third-party risk assessment for AI vendors
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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:
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Evaluate and implement AI security tools
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Understand the current AI security solutions landscape
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Build practical detection and prevention capabilities
Topics Covered:
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AI security tool ecosystem and vendor landscape
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Open-source security tools: Garak, PyRIT, Giskard
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Commercial solutions for AI security and monitoring
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Integration patterns and deployment strategies
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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:
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Understand emerging threats and future security challenges
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Develop continuous learning and improvement strategies
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Create action plans for organizational AI security programs
Topics Covered:
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Emerging threats: Deepfakes, advanced prompt injection, model inversion
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Future OWASP GenAI project developments and roadmap
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Building AI security communities and knowledge sharing
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Continuous improvement and threat intelligence integration
Action Planning Exercise: Develop a 90-day action plan for implementing OWASP GenAI security practices in participants' organizations
Kurs İçin Gerekli Önbilgiler
- General understanding of web application security principles
- Basic familiarity with AI/ML concepts
- Experience with security frameworks or risk assessment methodologies preferred
Audience
- Cybersecurity professionals
- AI developers
- System architects
- Compliance officers
- Security practitioners
14 Saat
