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arxiv: 2606.00904 · v1 · pith:I4QGM7EGnew · submitted 2026-05-30 · 💻 cs.CR

Framework for Discovering GPS Spoofing Attacks in Drone Swarms

Yingao Elaine Yao , Pritam Dash , Karthik Pattabiraman This is my paper

Pith reviewed 2026-06-28 18:21 UTC · model grok-4.3

classification 💻 cs.CR
keywords drone swarmsGPS spoofingSwarm Propagation Vulnerabilitiesfuzzing toolscontrol algorithmssecurity vulnerabilitiescollision risks
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The pith

GPS spoofing on one drone can propagate through control algorithms to cause collisions among others in a swarm.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that swarm control algorithms contain Swarm Propagation Vulnerabilities (SPVs) allowing an attacker to GPS-spoof a target drone and indirectly force victim drones off course into collisions. The authors introduce two fuzzing tools, SwarmFuzzGraph using graph theory and gradient-guided optimization, and SwarmFuzzBinary using observation-based seed scheduling and binary search, to discover these SPVs. Evaluation shows SwarmFuzzGraph reaches 48.8 percent success on one algorithm but fails on varied topologies, while SwarmFuzzBinary matches the rate and succeeds across all tested algorithms. Readers would care because drone swarms perform safety-critical tasks where such indirect attack paths could lead to physical failures.

Core claim

The authors claim that an attacker can target a swarm member through GPS spoofing attacks and indirectly cause other swarm members to veer from their course, resulting in collisions. They term these Swarm Propagation Vulnerabilities (SPVs) and show that two new fuzzing tools, SwarmFuzzGraph and SwarmFuzzBinary, can efficiently locate them in swarm control algorithms, with the second tool working across different swarm topologies.

What carries the argument

Swarm Propagation Vulnerabilities (SPVs): the exploitable weaknesses in swarm control algorithms that let GPS spoofing effects on a target drone propagate to alter the paths of other drones.

Load-bearing premise

The tested swarm control algorithms contain exploitable SPVs that the proposed fuzzing tools can reliably surface in a manner representative of real-world attacks.

What would settle it

A physical test in which GPS spoofing is applied to one drone in a live swarm and no other drones deviate or collide, or the tools failing to detect a known propagation path in a controlled simulation.

Figures

Figures reproduced from arXiv: 2606.00904 by Karthik Pattabiraman, Pritam Dash, Yingao Elaine Yao.

Figure 1
Figure 1. Figure 1: Workflow of distributed drone swarm systems. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Motivating example for SPVs in drone swarms. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: presents the overview of SwarmFuzzGraph. SwarmFuzzGraph only takes as inputs (1) the swarm control algorithm, (2) the mission parameters (including the swarm size and the location of the obstacle), and (3) the GPS spoofing deviation. It performs the following three steps. (1) It runs an initial test without any attack. If this test mission is successful (i.e., no collisions), it records mission information… view at source ↗
Figure 4
Figure 4. Figure 4: Convex property of the objective function in [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: shows the GPS spoofing parameters (i.e., the starting time and the duration) to trigger the SPVs under different swarm configurations. These are found by SwarmFuzz during the gradient￾based optimization process. We find that the average GPS spoofing starting time across different configurations is 6.91𝑠, and the average GPS spoofing duration is 10.33𝑠. 5d-5m 5d-10m 10d-5m 10d-10m 15d-5m 15d-10m Swarm confi… view at source ↗
Figure 6
Figure 6. Figure 6: Overview of SwarmFuzzBinary 6.1 Seed scheduling The goal of seed scheduling is to prioritize the most influential drone pairs during fuzzing. Instead of quantifying influence as in Section 4.2, we qualitatively assess the malicious influence of a drone pair. This influence is inferred by comparing two executions: one with the maximum attack applied and one without any attack. The behavioral differences obs… view at source ↗
Figure 7
Figure 7. Figure 7: VDO function in the convex and the monotonic forms. Algorithm 1 describes the SeedScheduling function. The seed scheduling takes as input a set of GPS spoofing parameter options. The parameters include the GPS spoofing distance, direction, start time, and duration. To exercise the victim drone’s behavior under different maximum amounts of GPS spoofing, the parameter options include performing GPS spoofing … view at source ↗
Figure 8
Figure 8. Figure 8: GPS spoofing duration found by SwarmFuzzBinary across swarm configurations in algorithms A1 and A2. In the figure, "5d-5m" means 5-drone swarms under 5m-spoofing. 5d-5m 5d-10m 10d-5m 10d-10m 15d-5m 15d-10m Swarm settings 0 20 40 60 80 100 120 140 160 Runtime (No. of search iterations) A1 A2 (a) SwarmFuzzBinary’s overhead in A1 and A2. 5d-5m 5d-10m 10d-5m 10d-10m 15d-5m 15d-10m Swarm settings 0 10 20 30 40 … view at source ↗
Figure 9
Figure 9. Figure 9: Average number of search iterations taken to find SPVs across swarm configurations. the runtime of SwarmFuzzBinary is 4.2x higher than that of SwarmFuzzGraph. This high runtime is mainly due to the difference in the seed scheduling in each framework. SwarmFuzzGraph only needs to run one simulation to construct the SVG, and then performs the seed scheduling in a single shot. However, SwarmFuzzBinary needs t… view at source ↗
Figure 10
Figure 10. Figure 10: Boxplot for the number of found SPVs per mission in algorithm A1 and A2. In the figure, "5d-5m" means 5-drone swarms under 5m-spoofing, and so on [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Comparison of fuzzers across A1 and A2 in terms of success rate and runtime [PITH_FULL_IMAGE:figures/full_fig_p019_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Collision scenarios in drone swarm control algorithms. (a) and (b) occur in the Olfati-Saber algorithm [PITH_FULL_IMAGE:figures/full_fig_p021_12.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper introduces Swarm Propagation Vulnerabilities (SPVs), in which GPS spoofing on a target drone in a swarm propagates through the control algorithm to cause victim drones to deviate and collide. It proposes two fuzzing tools—SwarmFuzzGraph (graph theory plus gradient-guided optimization) and SwarmFuzzBinary (observation-based seed scheduling plus binary search)—to discover such vulnerabilities. Evaluation on a popular swarm control algorithm reports an average 48.8% success rate for SwarmFuzzGraph (which fails on other topologies) while SwarmFuzzBinary achieves comparable rates across all tested algorithms.

Significance. If the central claim holds, the work would be significant for identifying previously unstudied security risks in swarm control algorithms used in safety-critical applications. The introduction of SPVs as a distinct vulnerability class and the provision of two concrete fuzzing approaches constitute a useful starting point for systematic security analysis of swarm dynamics. The empirical comparison across topologies is a positive aspect of the evaluation design.

major comments (1)
  1. [Abstract] Abstract: The reported success rates (48.8% for SwarmFuzzGraph) are defined solely as the rate of “finding SPVs,” yet the manuscript supplies no description of the downstream verification step that confirms the spoofed positions on the target drone actually produce measurable collisions among victim drones under the swarm dynamics model. This verification link—including the position-update equations, trajectory simulation fidelity, and collision-detection threshold—is load-bearing for the claim that SPVs result in collisions.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for major revision. The single major comment raises a valid point about the need for clearer description of the verification process linking SPV discovery to collisions. We address this directly below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The reported success rates (48.8% for SwarmFuzzGraph) are defined solely as the rate of “finding SPVs,” yet the manuscript supplies no description of the downstream verification step that confirms the spoofed positions on the target drone actually produce measurable collisions among victim drones under the swarm dynamics model. This verification link—including the position-update equations, trajectory simulation fidelity, and collision-detection threshold—is load-bearing for the claim that SPVs result in collisions.

    Authors: We agree that the current manuscript does not adequately describe the downstream verification step. The abstract and main text focus on the fuzzing process for identifying candidate SPVs but omit explicit details on how spoofed positions are fed into the swarm dynamics model, the position-update equations used, the fidelity of the trajectory simulation, and the precise collision-detection threshold. In the revised manuscript we will (1) expand the abstract to briefly note that discovered SPVs are verified via simulation of the swarm control algorithm, and (2) add a new subsection (likely in Section 4 or 5) that specifies the dynamics model, update equations, simulation parameters, and collision criterion. This revision will make the empirical claim that SPVs produce collisions fully traceable. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical evaluation with no derivations or self-referential fits.

full rationale

Paper introduces fuzzing tools (SwarmFuzzGraph, SwarmFuzzBinary) and reports empirical success rates (e.g., 48.8%) for finding SPVs in swarm control algorithms. No equations, parameter fitting, predictions derived from inputs, or self-citations are described in the provided text. Evaluation metrics are defined directly from the fuzzing process without reduction to prior results by construction. Central claim rests on simulation outcomes rather than any closed derivation loop.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities described in the abstract.

pith-pipeline@v0.9.1-grok · 5778 in / 918 out tokens · 15549 ms · 2026-06-28T18:21:14.186373+00:00 · methodology

discussion (0)

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