As AI technology expands across both benign and malicious applications, our understanding of the attack surface must evolve to account for emergent properties in complex systems. In large language models, these emergent behaviors create novel classes of vulnerabilities that are not only unpatched, but largely unrecognized. By systematically manipulating the model’s limited perception of reality, attackers can induce cascading failures that go far beyond traditional filter bypasses, exposing fundamental weaknesses in the internal logic and contextual binding of these systems. This session will unpack how these vulnerabilities work, walk through real examples, and explore the far-reaching implications for AI security, governance, and safety.