Constrained Black-Box Attacks Against Cooperative Multi-Agent Reinforcement Learning
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arXiv
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| Format: | Preprint |
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2025
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| author | Andam, Amine Bentahar, Jamal Hedabou, Mustapha |
| author_facet | Andam, Amine Bentahar, Jamal Hedabou, Mustapha |
| contents | Collaborative multi-agent reinforcement learning has rapidly evolved, offering state-of-the-art algorithms for real-world applications, including sensitive domains. However, a key challenge to its widespread adoption is the lack of a thorough investigation into its vulnerabilities to adversarial attacks. Existing work predominantly focuses on training-time attacks or unrealistic scenarios, such as access to policy weights or the ability to train surrogate policies. In this paper, we investigate new vulnerabilities under more challenging and constrained conditions, assuming an adversary can only collect and perturb the observations of deployed agents. We also consider scenarios where the adversary has no access at all (no observations, actions, or weights). Our main approach is to generate perturbations that intentionally misalign how victim agents see their environment. Our approach is empirically validated on three benchmarks and 22 environments, demonstrating its effectiveness across diverse algorithms and environments. Furthermore, we show that our algorithm is sample-efficient, requiring only 1,000 samples compared to the millions needed by previous methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09275 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Constrained Black-Box Attacks Against Cooperative Multi-Agent Reinforcement Learning Andam, Amine Bentahar, Jamal Hedabou, Mustapha Machine Learning Multiagent Systems Collaborative multi-agent reinforcement learning has rapidly evolved, offering state-of-the-art algorithms for real-world applications, including sensitive domains. However, a key challenge to its widespread adoption is the lack of a thorough investigation into its vulnerabilities to adversarial attacks. Existing work predominantly focuses on training-time attacks or unrealistic scenarios, such as access to policy weights or the ability to train surrogate policies. In this paper, we investigate new vulnerabilities under more challenging and constrained conditions, assuming an adversary can only collect and perturb the observations of deployed agents. We also consider scenarios where the adversary has no access at all (no observations, actions, or weights). Our main approach is to generate perturbations that intentionally misalign how victim agents see their environment. Our approach is empirically validated on three benchmarks and 22 environments, demonstrating its effectiveness across diverse algorithms and environments. Furthermore, we show that our algorithm is sample-efficient, requiring only 1,000 samples compared to the millions needed by previous methods. |
| title | Constrained Black-Box Attacks Against Cooperative Multi-Agent Reinforcement Learning |
| topic | Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2508.09275 |