Learn how adversarial AI testing helps identify prompt injection, psychological manipulation, and large language model (LLM) security vulnerabilities. Explore how attackers influence AI behavior and how to evaluate and strengthen AI resilience against sophisticated threats.

Designed for learners interested in AI security, LLM safety, and adversarial testing, this course examines anthropomorphic manipulation, reciprocity-based prompts, gaslighting directives, and persona adoption attacks. Understand how psychological strategies challenge AI safety boundaries and instruction adherence. Explore logic-based prompt injection, ambiguous instructions, foot-in-the-door manipulation, and diagnostic authority spoofing. Learn to recognize hidden attack objectives, analyze multi-turn exploitation, and assess reasoning vulnerabilities. Examine psychological resilience metrics, over-constrained jailbreaks, and cultural context exploitation to evaluate AI safety consistency and defensive strategies. By the end of this course, you'll be able to identify adversarial AI attacks, assess LLM vulnerabilities, interpret resilience metrics, and recommend improvements to AI security. What makes this course distinctive is its integrated focus on psychological manipulation, strategic prompt injection, and systematic AI resilience evaluation, helping you make informed AI safety decisions.