一句话：提出鲁棒性引导的群体模糊测试框架，用行为约束量化群体状态并定位关键节点以生成故障触发场景。
多机器人群体在战场侦察、农业监测与灾难搜救等复杂任务中至关重要，但群体算法的逻辑漏洞可能带来严重安全后果。现有软件测试方法在群体环境中面临两大挑战：群体可组合参数空间巨大，难以生成触发故障的场景；群体高度灵活动态，难以建模和评估全局协同行为，导致逻辑漏洞难以检测。本文提出 RSFuzz，一种鲁棒性引导的群体模糊测试框架，利用行为约束的鲁棒性定量评估群体状态，并引导生成故障触发场景；同时识别并针对关键群体节点施加扰动，有效缩减输入空间。基于该框架，作者构建了单攻击者模糊测试 SA-Fuzzing 与多攻击者模糊测试 MA-Fuzzing 两种方案。在三种主流群体算法上的仿真评估表明，RSFuzz 相比现有最优方法平均有效性提升 17.75%，效率提升 38.4%，并在真实环境中验证了部分检测到的漏洞。
Multi-robot swarms play an essential role in complex missions including battlefield reconnaissance, agricultural pest monitoring, as well as disaster search and rescue. Unfortunately, given the complexity of swarm algorithms, logical vulnerabilities are inevitable and often lead to severe safety and security consequences. Although various methods have been presented for detecting logical vulnerabilities through software testing, when they are used in swarm environments, these techniques face significant challenges: 1) Due to the swarm’s vast composable parameter space, it is extremely difficult to generate failure-triggering scenarios, which is crucial to effectively expose logical vulnerabilities; 2) Because of the swarm’s high flexibility and dynamism, it is challenging to model and evaluate the global swarm state, particularly in terms of cooperative behaviors, which makes it difficult to detect logical vulnerabilities.In this work, we propose RSFuzz, a robustness-guided swarm fuzzing framework designed to detect logical vulnerabilities in multi-robot systems. It leverages the robustness of behavioral constraints to quantitatively evaluate the swarm state and guide the generation of failure-triggering scenarios. In addition, RSFuzz identifies and targets key swarm nodes for perturbations, effectively reducing the input space. Upon the RSFuzz framework, we construct two swarm fuzzing schemes, Single Attacker Fuzzing (SA-Fuzzing) and Multiple Attacker Fuzzing (MA-Fuzzing), which employ single and multiple attackers, respectively, during fuzzing to disturb swarm mission execution. We evaluated RSFuzz’s performance with three popular swarm algorithms in simulated environments. The results show that RSFuzz outperforms the state-of-the-art with an average improvement of 17.75% in effectiveness and a 38.4% increase in efficiency. We also validated some detected vulnerabilities in real-world environments. Our code and data are publicly available.
DOI 原文 ·
@inproceedings{paperbot9222,
title = {RSFuzz: A Robustness-Guided Swarm Fuzzing Framework Based on Behavioral Constraints},
author = {Ruoyu Zhou and Zhiwei Zhang and Haocheng Han and Xiaodong Zhang and Zehan Chen and Jun Sun and Yulong Shen and Dehai Xu},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00048}
}