We study a network interdiction problem arising out of illicit human trafficking in the U.S. Southwest. Motivated by the significant amount of trafficking data shared with us by a large metropolitan police department, we modeled the interdiction problem as a Repeated One-Sum Game where, day after day, the Human Traffickers (HT) attempt to transport victims from one city to another and Law Enforcement Agencies (LEA) attempt to thwart the HT effort. Since the data indicated that most of the trafficking is taking place through road transportation, we built a network from the U.S. Interstate Highway System and called it U.S. Interstate Highway Network. If LEAs are successful, they win otherwise HTs win. If a HT wishes to transport victims from a city C_i to another city C_j , the strategy set for the HT is the set of paths going from C_i to C_j . Since a LEA can set up a check point on any segment of any path, the strategy set for the LEA is the union of all the path segments of all the paths connecting C_i to C_j . The path segments are the edges of the graph. If a HT selects a path P_k for transportation, a LEA can thwart that attempt only if a checkpoint is set up on at least one of the edges of P_k . If the total number of edges is E = {e_1, … , e_r} , the LEA is interested in finding the probability vector P = [p_1, p_2, … , p_r] that maximizes the expected payoff of the LEA, if the edge e_i is interdicted with probability p_i for all i, 1 ≤ i ≤ r . We show how to compute this probability vector P. Our extensive experimentation support our analytical results.
In this work, we propose a probabilistic and predictive framework to strategize against zero-day attacks in Autonomous Ground Robots (AGRs) operating safety-critical missions using ROS2-based autonomy stacks. Zero-day attacks represent a severe threat in Advanced Persistent Threat (APT) scenarios, as they exploit previously unknown vulnerabilities that remain hidden from defenders, enabling undetected intrusions that can compromise the robots mission and safety. The physical security of the AGR is based on an endpoint anomaly detection system that can prevent catastrophic outcomes. Our approach models this security scenario using a two-phase game-theoretic framework. In Phase I, we model the defender's incomplete knowledge of the attack tree using a Bayesian extensive form game-theoretic formulation of a network penetration attack, in which the defender maintains a prior belief over possible zero-day nodes and samples augmented attack tree realizations incorporating these hypothesized vulnerabilities. This enables computation of a robust spot-check policy by distributing defensive actions across known nodes informed by sampled structural uncertainties, thus increasing the likelihood of intercepting attackers traversing concealed or zero-day paths. The resulting mixed-strategy equilibrium defense policy efficiently allocates detection and response resources towards high-risk areas, improving system resilience against unforeseen attack vectors. In Phase II, we analyze the attack and defense strategies for an adversarial takeover of the low-level Model Predictive Controller node achieved through a successful Phase I attack. We model this scenario as a two-player Stackelberg game in the context of an end-point Active Inference (AIF) based Anomaly Detection System (ADS) that relies on dead-reckoning to monitor the robot trajectories and enforces an emergency shutdown if a significant deviation from nominal behavior is detected. The attacker seeks to cause a crash of the AGR by injecting malicious command velocities into the nominal command velocities, while the ADS seeks to detect the anomaly before the crash occurs.
In this article, we study cooperative deception in swarms, in the context of a two-player zero-sum game played over a directed acyclic graph. A swarm of agents must navigate to goal destinations while misleading an intelligent adversary that observes only partial, aggregated signals and updates its belief over time to optimize disruptive actions. This interaction is formalized as a dynamic game with one-sided partial observability, capturing both coordinated swarm behavior and adaptive adversarial inference over a finite horizon. To compute the equilibrium strategies, we propose an algorithm based on fictitious play, where best responses are computed via linear programming. To address the exponential complexity of multi-stage planning, we introduce a compression technique that maps observation histories into compact information states, ensuring that our algorithm remains effective with such compressed states. This reduction enables efficient equilibrium computation even in long-horizon settings. Simulations demonstrate how swarm-level deception can strategically reduce adversarial effectiveness and support theoretical results on the algorithms convergence and complexity.
The rise of Agentic AI, driven by advances in Large Language Models (LLMs), has enabled the design of autonomous multi-agent systems capable of strategic coordination in adversarial environments. This paper introduces a Gestalt games-in-games framework for modeling and orchestrating agentic AI workflows in cyber operations, particularly emphasizing cyber deception. The proposed framework captures two interwoven layers of decision-making: a workflow-level coordination game among agents assigned to interdependent tasks, and task-level adversarial games where agents confront strategic attackers. We formalize this structure using a layered stochastic game model and introduce the Gestalt-Nash Equilibrium, a joint solution concept that unifies local adversarial reasoning with global workflow optimization. To enable reasoning and coordination within this framework, we develop LLM-assisted decision algorithms that integrate prompt-based reasoning, rollout planning, and utility-guided adaptation. We demonstrate the practical value of this approach through a detailed case study on Mirai botnet deception in a software-defined networking (SDN) environment. Our results show that the LLM-enabled algorithm significantly improves deception effectiveness, reduces compromise rates, and increases attacker uncertainty and wasted effort over time. This work establishes a principled foundation for the design of modular, adaptive, and strategically aligned agentic AI systems in cybersecurity.
Reasoning about strategic ability in multi-agent systems typically relies on strategic logics such as Coalition Logic and Alternating-time Temporal Logics, which model what groups of agents can achieve through coordinated action. However, these logics treat the system state as a monolithic whole, limiting support for modular verification. By contrast, resource logics such as Separation Logic, originally developed for sequential program verification, support compositional reasoning by partitioning state into disjoint regions, but they lack the means to express strategic interaction among agents. We present Separating Coalition Logic (SCL), a unified framework that combines strategic reasoning with resource-sensitive modularity. Built over minimal interactive systems with local update rules and disjoint agent control, SCL supports both strategic guarantees and system decomposition along clean boundaries. A key feature is the Strategic Frame Rule, which lifts local properties into global guarantees even under adversarial conditions, enabling compositional proofs without reanalysis. We illustrate the framework via a case study in decentralized governance for Decentralized Autonomous Organizations, showing how SCL supports modular reasoning about both strategic interference and resource isolation.
