Quantifying AI Vulnerabilities: A Synthesis of Complexity, Dynamical Systems, and Game Theory

AI systems need better security evaluation tools: The paper introduces three new metrics (System Complexity Index, Lyapunov Exponent for AI Stability, and Nash Equilibrium Robustness) designed to measure different vulnerabilities in AI systems, addressing a gap in how we currently assess whether AI is secure enough for critical applications like healthcare or finance.

Complexity and stability are distinct security concerns: The framework distinguishes between how complicated an AI system is and how stable it remains when faced with unexpected changes or attacks—recognizing that a complex system isn't necessarily unstable, and vice versa, which helps identify different types of risks.

AI systems can be manipulated through strategic attacks: The Nash Equilibrium Robustness metric specifically evaluates how well AI systems resist adversarial manipulation, acknowledging that bad actors may strategically exploit AI weaknesses rather than attack randomly.

Interdisciplinary approaches are essential for AI security: The paper combines insights from complexity science, dynamical systems theory, and game theory, suggesting that responsible AI development requires collaboration across multiple fields rather than relying on any single discipline.

Current security evaluation methods are insufficient: The authors argue that existing techniques for evaluating AI security are inadequate for critical applications, implying that organizations deploying AI in high-stakes domains should demand more rigorous and comprehensive security assessments before implementation.