August 19, 2025
The GIST Simulating wolf pack attacks to strengthen AI collaboration and resilience 
Lisa Lock
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Robert Egan
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In the rapidly advancing fields of drone swarms and cooperative robotics, AI agents embedded in individual drones and robots must collaborate seamlessly—such as drones flying in formation to encircle an enemy or multiple robots working together in smart factories. However, these multi-agent systems are vulnerable to disruptions caused by adverse conditions or malicious attacks, which can compromise their cooperation and operational integrity.
Addressing this challenge, a research team led by Professor Seungyul Han from the Artificial Intelligence Graduate School of UNIST has developed a new adversarial attack framework inspired by wolf pack hunting strategies, alongside a corresponding defense training method.
These innovations aim to evaluate and strengthen the robustness of multi-agent reinforcement learning (MARL) systems against coordinated disruptions. They presented their research on July 15 at the International Conference on Machine Learning (ICML 2025), held in Vancouver. The paper is available on the arXiv preprint server.
Reinforcement learning enables AI agents to learn optimal behaviors through trial and error across diverse scenarios. In multi-agent settings, collaboration among agents typically ensures system resilience; if one agent encounters issues, others compensate to maintain overall performance.
However, existing attack strategies that target individual agents often fall short in exposing vulnerabilities within these cooperative structures, especially under realistic conditions such as sensor failures, weather disturbances, or cyber-attacks.
The proposed wolf pack attack simulates a strategic assault where an initial agent is deliberately compromised, triggering a cascading failure among assisting agents—mirroring a wolf pack isolating and overpowering prey. This attack leverages advanced predictive models to determine the optimal moment to initiate disruption and to sequentially compromise agents sensitive to cooperative cues.
Complementing this, the researchers developed the WALL (Wolfpack-Adversarial Learning) framework, which incorporates these adversarial scenarios into the training process. By exposing AI systems to simulated wolf pack attacks, WALL enhances their ability to withstand real-world disruptions, ensuring more stable and reliable cooperative behavior.
Experimental results demonstrate that AI agents trained with WALL exhibit remarkable resilience, maintaining coordination and task performance even under challenging conditions such as communication delays and sensor inaccuracies. This advancement not only provides a powerful tool for evaluating the robustness of multi-agent systems but also paves the way for deploying more resilient autonomous drones, robotic swarms, and industrial automation solutions.
Professor Han stated, "Our approach offers a new perspective on assessing and fortifying the cooperative capabilities of AI agents. By simulating sophisticated adversarial scenarios, we can better prepare systems for unpredictable real-world challenges, contributing to safer and more reliable autonomous technologies."
More information: Sunwoo Lee et al, Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning, arXiv (2025). DOI: 10.48550/arxiv.2502.02844
Journal information: arXiv Provided by Ulsan National Institute of Science and Technology Citation: Simulating wolf pack attacks to strengthen AI collaboration and resilience (2025, August 19) retrieved 19 August 2025 from https://techxplore.com/news/2025-08-simulating-wolf-ai-collaboration-resilience.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.
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