Explainable AI (XAI) Vulnerabilities in Autonomous Drone Swarms: Identifying Adversarial Inputs That Disrupt AI Decision-Making

Executive Summary: Autonomous drone swarms rely heavily on Explainable AI (XAI) to ensure transparency, accountability, and trust in critical missions such as search-and-rescue, surveillance, and logistics. However, XAI systems are vulnerable to adversarial inputs that exploit explainability mechanisms to manipulate decision-making processes. This article examines the emerging threat landscape of XAI vulnerabilities in drone swarms, identifies high-risk adversarial input techniques, and provides actionable recommendations for securing these systems. Our analysis is based on the latest research as of March 2026 and highlights the urgent need for robust, adversarially resilient XAI frameworks in autonomous aerial systems.

Key Findings

• XAI Systems Act as Double-Edged Swords: While XAI enhances interpretability, it also exposes decision logic to adversarial manipulation, enabling attackers to reverse-engineer and exploit explainability outputs.
• Adversarial Inputs Target Explainability Interfaces: Attackers inject carefully crafted inputs that alter model explanations (e.g., saliency maps, feature importance scores) without significantly changing the model’s output, leading to misplaced trust in faulty decisions.
• Drone Swarms Are High-Risk Targets: The distributed and cooperative nature of drone swarms amplifies the impact of explainability attacks, potentially causing coordinated failures across multiple units.
• Existing Defenses Are Inadequate: Current XAI security measures—such as input sanitization and anomaly detection—fail to address the semantic nature of explainability attacks, which manipulate the meaning of explanations rather than the data itself.
• Regulatory and Ethical Gaps Persist: There is no standardized framework for securing XAI in autonomous systems, leaving drone swarms vulnerable to exploitation under real-world operational conditions.

Understanding XAI in Autonomous Drone Swarms

Explainable AI (XAI) is a cornerstone of trustworthy autonomous systems, especially in high-stakes environments where human operators must understand and override AI decisions. In drone swarms, XAI serves multiple functions:

• Providing real-time justification for navigation, target identification, and collision avoidance.
• Enabling human-in-the-loop oversight during mission-critical phases.
• Facilitating post-mission audits to comply with safety and regulatory standards.

Common XAI techniques used in drone systems include:

• Saliency Maps: Highlighting image regions that influenced the AI’s classification of objects (e.g., identifying a vehicle in a reconnaissance image).
• SHAP (SHapley Additive exPlanations): Quantifying feature contributions to the decision, such as the role of altitude, speed, or sensor data in avoiding a collision.
• Local Interpretable Model-agnostic Explanations (LIME): Generating simplified, interpretable models around specific decisions to explain outcomes.

These mechanisms are designed to be transparent—but they also provide a roadmap for adversaries seeking to manipulate the system.

The Emergence of XAI-Specific Adversarial Attacks

Traditional adversarial attacks (e.g., FGSM, PGD) focus on altering model outputs by perturbing input data. However, a new class of attacks—explainability-targeted adversarial inputs—directly manipulates the explainability interface without significantly changing the model’s decision. These attacks exploit the fact that explanations are derived from the same internal representations as the model’s output.

In drone swarms, such attacks can be executed via:

• Sensor Spoofing: Injecting misleading sensor readings (e.g., false thermal signatures) that alter saliency maps to highlight harmless objects (e.g., a tree) while downplaying critical threats (e.g., an intruder).
• Semantic Data Injection: Modifying metadata or contextual inputs (e.g., GPS timestamps, mission parameters) to skew SHAP values, making a drone believe a collision risk is “low” despite clear sensor evidence.
• Adversarial Overlays: Projecting or displaying deceptive visual cues (e.g., fake lane markings or obstacles) that are misinterpreted by onboard vision systems, leading to incorrect saliency maps and misguided avoidance maneuvers.
• Coordinated Explanation Poisoning: In swarms, an attacker compromises one drone to send falsified SHAP values or LIME explanations to neighboring units, triggering cascading misinterpretations of shared environmental data.

A 2025 study by MITRE demonstrated that adversaries could reduce the perceived threat level of a human target in a drone’s object detection system by 78% through targeted perturbations to saliency maps—without changing the underlying classification output. This highlights the potency of XAI-focused attacks in real-world scenarios.

Why Drone Swarms Are Particularly Vulnerable

Autonomous drone swarms introduce unique attack surfaces due to their:

• Distributed Coordination: Swarms rely on shared explanations and decision justifications. If one node’s XAI output is compromised, it can mislead the entire group.
• Real-Time Constraints: Rapid decision cycles limit the time available for human oversight or cross-validation of XAI outputs, increasing reliance on automated explanations.
• Heterogeneous Sensors: Variations in sensor types (LiDAR, RGB, thermal) complicate consistency checks, making it easier to inject explanations that appear valid across different modalities.
• Edge Computing Limitations: Many drones process XAI locally to reduce latency. This increases exposure to on-device manipulation of explanation outputs.

For example, during a 2026 humanitarian mission in Sub-Saharan Africa, a threat actor exploited a vulnerability in the swarm’s LIME-based terrain analysis module. By injecting false elevation data, the attacker caused multiple drones to misclassify safe landing zones as hazardous, delaying critical supply drops. The incident went undetected until post-mission analysis revealed inconsistencies in the XAI logs—long after the damage was done.

Current Security Measures: Gaps and Limitations

Existing defenses against adversarial attacks in drone swarms are insufficient for XAI-specific threats:

• Input Validation: Traditional sanitization cannot detect semantic manipulations that don’t alter raw data but skew derived explanations.
• Model Hardening: Techniques like adversarial training improve output robustness but do not prevent explanation manipulation.
• Anomaly Detection: AI-based monitors flag outliers in behavior but struggle with subtle, context-dependent shifts in explanation quality.
• Blockchain for Audit Trails: While useful for logging decisions, blockchain alone cannot verify the integrity of explanations generated in real time.

Moreover, current XAI frameworks (e.g., DARPA’s XAI program outputs) lack built-in security primitives. Most assume a benign environment, leaving explainability pipelines open to exploitation.

Recommendations for Securing XAI in Drone Swarms

To mitigate XAI vulnerabilities in autonomous drone swarms, the following measures should be implemented:

1. Adversarially Robust XAI (ARXAI) Frameworks

Develop XAI methods that are inherently resistant to manipulation by incorporating:

• Uncertainty-Aware Explanations: Output confidence intervals alongside explanations (e.g., “This object is a civilian vehicle with 85% ± 12% confidence”).
• Consistency Checks: Cross-validate explanations across multiple XAI techniques (e.g., SHAP vs. LIME) to detect inconsistencies caused by adversarial inputs.
• Explanation Integrity Verification: Use cryptographic hashing or digital signatures to ensure explanations have not been altered post-generation.

2. Swarm-Level Explanation Consensus

Implement distributed agreement protocols where drones collectively validate explanations before acting on them:

• Reputation Scoring: Nodes rate the reliability of other drones’ explanations based on historical accuracy and consistency.
• Majority Voting: Require consensus among a majority of swarm members before accepting an explanation as valid.
• Byzantine Fault Tolerance: Apply consensus algorithms (e.g., PBFT) to tolerate up to a third of compromised nodes without cascading failures.