CAMA: Exploring Collusive Adversarial Attacks in c-MARL
arXiv:2603.20390v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning (c-MARL) has been widely deployed in real-world applications, such as social robots, embodied intelligence, UAV swarms, etc. Nevertheless, many adversarial attacks still exist to threaten various c-MARL systems. At present, the studies mainly focus on single-adversary perturbation attacks and white-box adversarial attacks that manipulate agents' internal observations or actions. To address these limitations, we in this paper attempt to study collusive adversarial attacks through strategically organizing a set of malicious agents into three collusive attack modes: Collective Malicious Agents, Disguised Malicious Agents, and Spied Malicious Agents. Three novelties are involved: i) three collusive adversarial attacks are creatively proposed for the first time, and a unified framework CAMA for policy-level collusive attacks is designed; ii) the attack effectiveness is theoretically analyzed from
arXiv:2603.20390v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning (c-MARL) has been widely deployed in real-world applications, such as social robots, embodied intelligence, UAV swarms, etc. Nevertheless, many adversarial attacks still exist to threaten various c-MARL systems. At present, the studies mainly focus on single-adversary perturbation attacks and white-box adversarial attacks that manipulate agents' internal observations or actions. To address these limitations, we in this paper attempt to study collusive adversarial attacks through strategically organizing a set of malicious agents into three collusive attack modes: Collective Malicious Agents, Disguised Malicious Agents, and Spied Malicious Agents. Three novelties are involved: i) three collusive adversarial attacks are creatively proposed for the first time, and a unified framework CAMA for policy-level collusive attacks is designed; ii) the attack effectiveness is theoretically analyzed from the perspectives of disruptiveness, stealthiness, and attack cost; and iii) the three collusive adversarial attacks are technically realized through agent's observation information fusion, attack-trigger control. Finally, multi-facet experiments on four SMAC II maps are performed, and experimental results showcase the three collusive attacks have an additive adversarial synergy, strengthening attack outcome while maintaining high stealthiness and stability over long horizons. Our work fills the gap for collusive adversarial learning in c-MARL.
Executive Summary
This article proposes a new framework, CAMA, to study collusive adversarial attacks in cooperative multi-agent reinforcement learning (c-MARL). The authors introduce three novel collusive attack modes: Collective Malicious Agents, Disguised Malicious Agents, and Spied Malicious Agents. A unified framework CAMA is designed to facilitate policy-level collusive attacks, and theoretical analysis is provided from the perspectives of disruptiveness, stealthiness, and attack cost. Experimental results on four SMAC II maps demonstrate the effectiveness and stability of the proposed attacks. The study fills a gap in collusive adversarial learning in c-MARL and has significant implications for the development of secure c-MARL systems.
Key Points
- ▸ Introduction of three novel collusive attack modes in c-MARL
- ▸ Design of a unified framework CAMA for policy-level collusive attacks
- ▸ Theoretical analysis of attack effectiveness from multiple perspectives
Merits
Significance of the Study
The study addresses a critical limitation in existing adversarial attack research and fills a gap in collusive adversarial learning in c-MARL.
Methodological Innovation
The authors introduce a novel framework and three new attack modes, which provides a significant methodological contribution to the field.
Practical Impact
The study has significant implications for the development of secure c-MARL systems, which are increasingly used in real-world applications.
Demerits
Limited Experimental Scope
The study is limited to four SMAC II maps, which may not be representative of the broader range of c-MARL systems.
Lack of Real-World Data
The study relies on simulated data, which may not accurately reflect real-world scenarios.
Complexity of the Framework
The CAMA framework may be difficult to implement and interpret, particularly for non-experts.
Expert Commentary
This study makes a significant contribution to the field of c-MARL by introducing new attack modes and a framework for collusive adversarial attacks. The authors provide a thorough theoretical analysis of the attack effectiveness and demonstrate the practical impact of the proposed attacks through experiments on four SMAC II maps. However, the study is limited by its reliance on simulated data and the complexity of the CAMA framework. Nevertheless, the study has significant implications for the development of secure c-MARL systems and highlights the need for policymakers to consider the potential risks of these systems.
Recommendations
- ✓ Future research should focus on developing more robust and secure c-MARL systems that can withstand collusive adversarial attacks.
- ✓ Policymakers should consider the potential risks of c-MARL systems and develop frameworks for ensuring their security and reliability.
Sources
Original: arXiv - cs.LG
