AI-Against-AI Conflict in Distributed Tactical Autonomy - Imani, Mahdi; Lan, Tian (Proceedings of the AAAI Symposium Series, 2026)
Distributed autonomy is becoming the dominant architecturalparadigm for multi-agent and multi-Uncrewed Aerial Systems(UAS) that must sense, decide, and act under uncertainty,communication constraints, and partial observability. Asautonomy pipelines become increasingly learned, adaptive,and coordination-driven, safe collective behavior dependson the integrity of local information and coordinationinterfaces through which independently acting agentscompose team-level behavior. This paper argues that AIadvances are not just enhancing our operations; adversariesare leveraging AI against these systems, introducingAI-against-AI conflict, in which AI-enabled,coordinated adversaries strategically shape observations,communication, and outcome feedback - through deception,information denial, communication disruption, and physicalor cyber-induced perturbations - across the autonomypipeline to induce cascading team-level coordinationfailures. At an autonomy-level abstraction spanning cyberand physical influence, we describe how coordinated,sequential, and stealth-constrained manipulation cancompound across decision cycles and act as a forcemultiplier, producing abrupt team-level breakdowns withoutovert component failure. We conclude that future researchin tactical autonomy must move beyond robustness to noiseand faults, and instead develop foundations for preservingsafe collective behavior under strategic, adaptiveadversarial influence.