Framework for Discovering GPS Spoofing Attacks in Drone Swarms

2026-05-30

AI Summary

This study addresses a critical security vulnerability in drone swarm control algorithms, termed Swarm Propagation Vulnerabilities (SPVs), wherein GPS spoofing of a single drone can trigger cascading failures leading to trajectory deviations or collisions. The work presents the first systematic characterization of SPVs and introduces two automated fuzz testing tools: SwarmFuzzGraph and SwarmFuzzBinary. SwarmFuzzGraph integrates graph-theoretic modeling with gradient-guided optimization, while SwarmFuzzBinary employs observation-based seed scheduling combined with binary search strategies, each tailored to distinct swarm topologies. Experimental results demonstrate that SwarmFuzzGraph achieves an average SPV discovery success rate of 48.8% across representative algorithms, and SwarmFuzzBinary proves effective across all tested algorithms, substantially advancing the state of swarm safety verification.

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 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.

Problem

• GPS spoofing
• drone swarms
• swarm control algorithms
• security vulnerabilities
• Swarm Propagation Vulnerabilities

Innovation

• Swarm Propagation Vulnerabilities
• GPS spoofing
• fuzzing
• swarm robotics
• SwarmFuzzBinary