Active Defense Against False Data Injection Attacks in Robotic Manipulators

Gabriele Gualandi; Carl Mikael Larsson; Alessandro V. Papadopoulos

cs.ROMay 18, 2026

Active Defense Against False Data Injection Attacks in Robotic Manipulators

Authors: Gabriele Gualandi, Carl Mikael Larsson, Alessandro V. Papadopoulos

Organizations: Mälardalen University, Väster˚as, Sweden

Abstract

Robotic systems are vulnerable to False Data Injection Attacks (FDIAs), where adversaries corrupt sensor signals to gain malicious control. Feedback linearization exposes robotic systems to integrator vulnerability, making them susceptible to stealthy attacks that can cause significant deviations in end-effector behavior without raising alarms. This paper addresses the resilience of manipulators against finite-horizon FDIAs by formalizing two defense methods, namely anomaly-aware virtual damping and manipulability reduction, with probabilistic guarantees on nominal task execution. Simulations on a 7-DOF redundant manipulator show that the proposed defenses substantially reduce the impact of FDIA compared to using solely a threshold-based ADS like the Chi-squared, while preserving nominal task performance in the absence of attack.

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Jul 4, 2026 · cs.ROJ/K move · Enter open · S save

Occluding the Solution Space: Planner-Agnostic Adversarial Attacks on Tolerance-Aware Manipulation

Keke Tang, Tianyu Hao, Weilong Peng, Hao Jiang+4

Guangzhou University, Guangzhou 510006, China · University of Science and Technology of China, Hefei 230026, China · Northwestern Polytechnical University, Xi’an 710072, China

Adversarial attacks on motion planning are crucial for evaluating and quantifying the intrinsic robustness of robotic manipulation. However, existing approaches are typically limited by restrictive exact-pose objectives and their reliance on planner-in-the-loop queries. To address these limitations, we propose a planner-agnostic attack framework for tolerance-aware manipulation. Our approach shifts the evaluation paradigm to task-level feasibility over goal regions, efficiently inserting adversarial obstacles without requiring oracle access to the victim system. Offline, we characterize the robot's intrinsic workspace capabilities via a kinematic occupancy heatmap, which encodes the density of feasible trajectories and robustness priors without invoking a specific planner. Online, we formulate the attack as a budgeted maximum-coverage optimization, strategically deploying obstacles subject to explicit geometric constraints to occlude the solution space. Extensive experiments across simulation and real-world scenarios demonstrate that our method reliably induces planning failures, significantly outperforming planner-in-the-loop baselines in both computational efficiency and attack efficacy.

Obstacle Avoidance Adversarial Examples

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Abstract

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