Second Order State Hallucinations for Adversarial Attack Mitigation in Formation Control of Multi-Agent Systems

TL;DR

This paper introduces Second-Order State Hallucination (SOSH), a decentralized method for detecting and mitigating adversarial attacks in multi-agent formation control, ensuring stability and robustness through predictive state correction.

Contribution

The paper presents SOSH, a novel lightweight, decentralized framework that uses second-order Taylor expansions for rapid attack detection and mitigation in multi-agent systems.

Findings

SOSH outperforms W-MSR and Huber filters in convergence speed and accuracy.

Formation errors remain exponentially bounded under persistent attacks.

SOSH demonstrates practical scalability and robustness in simulations.

Abstract

The increasing deployment of multi-agent systems (MAS) in critical infrastructures such as autonomous transportation, disaster relief, and smart cities demands robust formation control mechanisms resilient to adversarial attacks. Traditional consensus-based controllers, while effective under nominal conditions, are highly vulnerable to data manipulation, sensor spoofing, and communication failures. To address this challenge, we propose Second-Order State Hallucination (SOSH), a novel framework that detects compromised agents through distributed residual monitoring and maintains formation stability by replacing attacked states with predictive second-order approximations. Unlike existing mitigation strategies that require significant restructuring or induce long transients, SOSH offers a lightweight, decentralized correction mechanism based on second-order Taylor expansions, enabling rapid and scalable resilience. We establish rigorous Lyapunov-based stability guarantees, proving that formation errors remain exponentially bounded even under persistent attacks, provided the hallucination parameters satisfy explicit conditions. Comprehensive Monte Carlo experiments on a 5-agent complete graph formation demonstrate that SOSH outperforms established robust control schemes, including W-MSR and Huber-based consensus filters, achieving faster convergence rates, lower steady-state error, and superior transient recovery. Our results confirm that SOSH combines theoretical robustness with practical deployability, offering a promising direction for securing MAS formations against sophisticated adversarial threats.