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Framework for Discovering GPS Spoofing Attacks in Drone Swarms

arXiv Security Archived Jun 02, 2026 ✓ Full text saved

arXiv:2606.00904v1 Announce Type: new Abstract: Swarm robotics, particularly drone swarms, are used in various safety-critical tasks. While a lot of attention has been given to improving swarm control algorithms for improved intelligence, the security implications of various design choices in swarm control algorithms have not been studied. We highlight how an attacker can exploit the vulnerabilities in swarm control algorithms to disrupt drone swarms. Specifically, we show that the attacker can

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    Computer Science > Cryptography and Security [Submitted on 30 May 2026] Framework for Discovering GPS Spoofing Attacks in Drone Swarms Yingao Elaine Yao, Pritam Dash, Karthik Pattabiraman Swarm robotics, particularly drone swarms, are used in various safety-critical tasks. While a lot of attention has been given to improving swarm control algorithms for improved intelligence, the security implications of various design choices in swarm control algorithms have not been studied. We highlight how an attacker can exploit the vulnerabilities in swarm control algorithms to disrupt drone swarms. Specifically, we show that the attacker can target a swarm member (target drone) through GPS spoofing attacks, and indirectly cause other swarm members (victim drones) to veer from their course, resulting in collisions. We call these Swarm Propagation Vulnerabilities (SPVs). In this paper, we introduce two fuzzing tools, SwarmFuzzGraph and SwarmFuzzBinary, to efficiently find SPVs in swarm control algorithms. SwarmFuzzGraph uses a combination of graph theory and gradient-guided optimization to find SPVs. Our evaluation on a popular swarm control algorithm shows that SwarmFuzzGraph achieves an average success rate of 48.8% in finding SPVs. However, SwarmFuzzGraph fails to find any SPVs in drone swarms with different topologies. We then propose SwarmFuzzBinary, which uses observation-based seed scheduling and binary search to find SPVs. The evaluation shows that SwarmFuzzBinary's success rate is comparable to SwarmFuzzGraph and work in all tested algorithms. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.00904 [cs.CR]   (or arXiv:2606.00904v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.00904 Focus to learn more Submission history From: Yingao Elaine Yao [view email] [v1] Sat, 30 May 2026 21:59:33 UTC (1,053 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Jun 02, 2026
    Archived
    Jun 02, 2026
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