AI Red Teaming and Agent Security Testing Training in Copenhagen, Denmark

This training provides a comprehensive understanding of AI red teaming and agent security testing. Participants will learn to identify vulnerabilities in AI agents, simulate adversarial attacks, and implement defensive strategies. The course covers prompt injection, model poisoning, data extraction, and agent manipulation techniques, preparing professionals to secure AI systems in production environments.

Introduction to AI Red Teaming and Agent Security

• Overview of AI red teaming and its importance
• Understanding AI agent architectures and attack surfaces
• Ethical and legal considerations
• Red teaming frameworks and methodologies

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Threat Modeling for AI Systems

• Identifying assets and threat actors
• Mapping attack vectors for AI agents
• Risk assessment and prioritization
• Creating threat models for agent interactions

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Prompt Injection and Jailbreak Techniques

• Direct and indirect prompt injection
• Jailbreaking large language models
• Defensive prompt engineering
• Testing for injection resilience

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Model Poisoning and Backdoor Attacks

• Data poisoning techniques
• Backdoor insertion in models
• Detection and mitigation strategies
• Evaluating model integrity

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Data Extraction and Inversion Attacks

• Membership inference attacks
• Model inversion and extraction
• Data leakage prevention
• Privacy-preserving techniques

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Agent Manipulation and Adversarial Attacks

• Manipulating agent behavior
• Adversarial examples for AI agents
• Evasion and impersonation attacks
• Testing agent robustness

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Security Controls and Defensive Strategies

• Input validation and sanitization
• Rate limiting and anomaly detection
• Secure agent deployment practices
• Incident response for AI systems

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Red Teaming Tools and Automation

• Overview of open-source red teaming tools
• Automating attack simulations
• Reporting and documentation
• Continuous security testing

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Case Studies and Real-World Scenarios

• Analyzing real-world AI security incidents
• Applying red teaming to industry use cases
• Lessons learned and best practices
• Developing a security roadmap for AI agents

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Final Assessment and Review

• Comprehensive review of key concepts
• Practical red teaming challenge
• Feedback and Q&A
• Next steps for continued learning

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