AI-Powered Deception and Exploitation

This unit examines how advanced AI and automation amplify social engineering and deceptive influence—covering LLM-driven conversational relays, bots, Monte Carlo optimization, synthetic media (deepfakes, voice cloning, imagery), and physical robotics. Students will analyze technical attack vectors, simulate and optimize deceptive campaigns, and practice detection, mitigation, and ethical/legal assessment to prepare for defensive and policy-focused work in the final unit.

Learning Objectives

• Analyze how transformers, large language models, conversational relays, and automated bots are combined to create scalable deceptive conversations and influence operations, citing system components and threat scenarios
• Apply Monte Carlo simulation and basic statistical modeling to optimize social engineering parameters (message variants, timing, and targeting) in a controlled lab exercise and interpret results for decision-making
• Evaluate synthetic-media and technical exploitation vectors—deepfake imagery, voice synthesis, deliberate software flaws, and physical robotics—using likelihood, detectability, and impact criteria to produce a prioritized threat assessment
• Demonstrate detection and mitigation strategies to remain under the radar or to counter automated deception, including heuristic detection, counter-bot measures, incident-response playbooks, and consideration of legal/ethical constraints in role-play scenarios