Strategic Robustness in Artificial Intelligence: A Conditional Failure-Chain Framework for Adversarial Robustness

This upload contains a research-style conceptual paper titled “Strategic Robustness in Artificial Intelligence: A Conditional Failure-Chain Framework for Adversarial Robustness.” The paper proposes a conditional failure-chain framework for adversarial robustness in AI systems. It explains how adversarial or adversarial-like inputs can move through relevance detection, risk appraisal, authority assignment, task framing, decision gates, output generation, tool action, logging, and future system adjustment. The central operational concept is authority assignment: the process by which a system treats content as instruction, evidence, permission, user intent, persistent memory, or policy-relevant signal. The paper includes a defensive adversarial pattern taxonomy, benchmark-design guidance, illustrative scenarios, release/action boundary controls, implementation tiers, operational checklists, and evidence-maturity guidance. The taxonomy is presented as a conceptual and operational framework for red teaming, incident analysis, safety evaluation, deployment review, and safer implementation of AI systems involving retrieval, tools, memory, external content, or workflow actions. This paper is a standalone Strategic Robustness paper. It is related to the author’s prior Signal-Time-Authority records as architectural background for release/action boundary and runtime controllability concepts, but it is not part of the STA core series, not STA Paper 6, and does not claim empirical validation from the STA records.