Not What You Asked For: Typographic Attacks in Household Robot Manipulation
- URL: http://arxiv.org/abs/2605.18593v1
- Date: Mon, 18 May 2026 16:06:29 GMT
- Title: Not What You Asked For: Typographic Attacks in Household Robot Manipulation
- Abstract summary: This work evaluates typographic attacks in a Habitat-based simulation using the HomeRobot benchmark.<n>We introduce a decoupled perception architecture that exposes a frozen CLIP encoder to adversarial stickers.<n>We find that perceptual errors propagate through the persistent 3D semantic map to produce kinetic failures.
- Score: 2.8988874060074163
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Open-vocabulary embodied AI agents increasingly rely on vision-language models such as CLIP for object perception and task grounding. However, the shared embedding space that enables this flexibility introduces a structural vulnerability to typographic attacks, where printed text in a physical scene semantically overrides visual judgment. While prior work has quantified this threat in static 2D benchmarks and 3D navigation tasks, its impact on the full Sense-Plan-Act pipeline of household robot manipulation remains unexplored. This work evaluates typographic attacks in a Habitat-based simulation using the HomeRobot benchmark. We introduce a decoupled perception architecture that exposes a frozen CLIP encoder to adversarial stickers while maintaining geometric grounding via DETIC. In a controlled evaluation pool of 59 attributable episodes, the attack achieves an overall Attack Success Rate (ASR) of 67.8%, rising to 70.0% among fully successful episodes, under uncontrolled viewing angles and occlusion with no perceptual optimization. Critically, we find that perceptual errors propagate through the persistent 3D semantic map to produce kinetic failures, defined here as physically executed grasping and transport of the wrong object driven by an adversarially poisoned semantic state. In these cases, the robot physically grasps and delivers the wrong object to a target receptacle. These results establish typographic misclassification as a real, measurable, and physically consequential threat to the safety of modular manipulation pipelines that prior typographic attack research has left unexamined.
Related papers
- DeSeG: Decoupling Semantic Intent and Geometric Constraints for Physically Plausible Human-Scene Interaction [50.06148080975928]
We propose DeSeG, a hierarchical framework that explicitly decouples semantic intent from geometric constraints.<n>DeSeG achieves state-of-the-art performance, reducing mean scene penetration by 47% and improving semantic alignment by 29% over the SOTA baselines.
arXiv Detail & Related papers (2026-07-07T03:21:08Z) - PhyScene3D: Physically Consistent Interactive 3D Tabletop Scene Generation [75.9028457575434]
Generating 3D tabletop scenes is a fundamental yet underexplored problem for interactive and generalist robotic learning.<n>Here, we introduce PhyScene3D, a framework that reformulates generation as a Human-Mimetic Constructive Process.<n>Experiments demonstrate that PhyScene3D outperforms state-of-the-art approaches in both semantic accuracy and physical validity.
arXiv Detail & Related papers (2026-06-01T03:56:32Z) - From Prompt to Physical Actuation: Holistic Threat Modeling of LLM-Enabled Robotic Systems [0.5161531917413708]
We study how robotic cybersecurity, adversarial perception attacks, and LLM safety interact and propagate across trust boundaries.<n>This is the first DFD-based threat analysis integrating all three threat categories across the full perception-planning-actuation pipeline of an LLM-enabled robotic system.
arXiv Detail & Related papers (2026-04-29T23:44:07Z) - Beyond the Patch: Exploring Vulnerabilities of Visuomotor Policies via Viewpoint-Consistent 3D Adversarial Object [26.15314358613966]
This work proposes a viewpoint-consistent adversarial texture optimization method for 3D objects through differentiable rendering.<n>As optimization strategies, we employ Expectation over Transformation (EOT) with a Coarse-to-Fine (C2F) curriculum.<n>We further integrate saliency-guided perturbations to redirect policy attention and design a targeted loss that persistently drives robots toward adversarial objects.
arXiv Detail & Related papers (2026-03-05T07:57:47Z) - Adversarial Patch Attacks on Vision-Based Cargo Occupancy Estimation via Differentiable 3D Simulation [0.0]
We study the feasibility of such attacks on a convolutional cargo-occupancy classifier using fully simulated 3D environments.<n>Our experiments demonstrate that 3D-optimized patches achieve high attack success rates, especially in a denial-of-service scenario.<n>This is the first study to investigate adversarial patch attacks for cargo-occupancy estimation in physically realistic, fully simulated 3D scenes.
arXiv Detail & Related papers (2025-11-24T16:05:40Z) - Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics [68.36528819227641]
This paper systematically evaluates the robustness of Vision-Language-Action (VLA) models.<n>We introduce two untargeted attack objectives that leverage spatial foundations to destabilize robotic actions, and a targeted attack objective that manipulates the robotic trajectory.<n>We design an adversarial patch generation approach that places a small, colorful patch within the camera's view, effectively executing the attack in both digital and physical environments.
arXiv Detail & Related papers (2024-11-18T01:52:20Z) - Transient Adversarial 3D Projection Attacks on Object Detection in Autonomous Driving [15.516055760190884]
We introduce an adversarial 3D projection attack specifically targeting object detection in autonomous driving scenarios.
Our results demonstrate the effectiveness of the proposed attack in deceiving YOLOv3 and Mask R-CNN in physical settings.
arXiv Detail & Related papers (2024-09-25T22:27:11Z) - Defending From Physically-Realizable Adversarial Attacks Through
Internal Over-Activation Analysis [61.68061613161187]
Z-Mask is a robust and effective strategy to improve the robustness of convolutional networks against adversarial attacks.
The presented defense relies on specific Z-score analysis performed on the internal network features to detect and mask the pixels corresponding to adversarial objects in the input image.
Additional experiments showed that Z-Mask is also robust against possible defense-aware attacks.
arXiv Detail & Related papers (2022-03-14T17:41:46Z) - On the Real-World Adversarial Robustness of Real-Time Semantic
Segmentation Models for Autonomous Driving [59.33715889581687]
The existence of real-world adversarial examples (commonly in the form of patches) poses a serious threat for the use of deep learning models in safety-critical computer vision tasks.
This paper presents an evaluation of the robustness of semantic segmentation models when attacked with different types of adversarial patches.
A novel loss function is proposed to improve the capabilities of attackers in inducing a misclassification of pixels.
arXiv Detail & Related papers (2022-01-05T22:33:43Z) - Evaluating the Robustness of Semantic Segmentation for Autonomous
Driving against Real-World Adversarial Patch Attacks [62.87459235819762]
In a real-world scenario like autonomous driving, more attention should be devoted to real-world adversarial examples (RWAEs)
This paper presents an in-depth evaluation of the robustness of popular SS models by testing the effects of both digital and real-world adversarial patches.
arXiv Detail & Related papers (2021-08-13T11:49:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.