Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-13T17:08:58.831798Z
Paper Citation Record · LEDGER
As of 6 August 2026, this Paper Citation Record lists 100 of 299 outbound references and 2 inbound Pith citation observations for arXiv:2605.02900.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-13T17:08:58.831798Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-01T04:17:44.004753Z
A source-named dated measurement, never combined with another source.
Source: cited_works
100 of 299 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation fa5abc3f-f819-425a-837c-efa07ecd60fd · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems
Reference 1
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Observation 05f2d849-ffac-48cf-a729-d3a736b7423a · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Vision-onlyrobotnavigationinaneuralradianceworld.IEEERoboticsandAutomationLetters(RA-L), 2022
Reference 2
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Observation 93c8ba71-a4e9-42a3-8076-0befa6a2eef2 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Cascading failures in agentic ai.https://adversa.ai/blog/cascading-failures-in-age ntic-ai-complete-owasp-asi08-security-guide-2026/, 2025
Reference 3
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Observation 3f8eca13-bbd1-4564-933f-90488c0f832d · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Distributionally adaptive meta reinforcement learning
Reference 4
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Observation 1c94bd1b-4634-4418-bf8e-edb72025ad39 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Ataxonomyoffactorsinfluencingperceived safety in human–robot interaction.Robotics, 2023
Reference 5
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Observation 21843cdc-7a07-478b-8826-34be5cb58858 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Remembering More, Risking More: Longitudinal Safety Risks in Memory-Equipped LLM Agents
Reference 6
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Observation f78c970a-890d-4a97-bbec-53752b9c2afa · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses The adolescence of technology.https://www.darioamodei.com/essay/the-adolescen ce-of-technology, 2025
Reference 7
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Observation e4809d9a-a70c-48f4-a879-16cc5508948c · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses FlowHijack: Adynamics-aware backdoor attack on flow-matching vision-language-action models
Reference 8
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Observation 80fb9495-f751-442c-a6b7-32de9381f945 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Chips-messagerobustauthentication(chimera)forgpscivilian signals
Reference 9
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Observation 8901a8fb-04d6-44ea-81a0-9f71963940bb · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments
Reference 10
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Observation a2037cc6-0740-43ab-a44e-0c4bded27524 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
Reference 11
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Observation 9148838f-a866-492a-bf3e-12cf655aedf2 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Ashcraft, Ted Staley, Josh Carney, Cameron Hickert, Derek Juba, Kiran Karra, and Nathan Drenkow
Reference 12
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Observation f82a30d0-d60f-4e36-8301-fbcddb254566 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Mash-vlm: Mitigating action-scene hallucination in video-llms through disentangled spatial-temporal representations
Reference 13
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Observation d77ae71f-89ec-4484-bf06-e8d758ce2f77 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Multi-robot coordination with adversarial perception
Reference 14
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Observation d6bba2e0-ad5c-4997-82e6-0e63a0c140cc · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Rat: Adversarial attacks on deep reinforcement agents for targeted behaviors
Reference 15
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Observation c2b02963-d8a8-422e-aa28-af113f23d376 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Universal closed-box adversarial attack for trajectory representation via controlling high-dimensional iterative constraints.IEEE Internet of Things Journal (IoT-J), 2025
Reference 16
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Observation 79667f3b-b33d-459a-99fd-3cb88310a46c · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses CleanCLIP: Mitigating data poisoning attacks in multimodal contrastive learning
Reference 17
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Observation 0e4979f0-36e6-4534-bb6f-8ec244d2e9bc · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses The safety challenge of world models for embodied ai agents: A review.arXiv preprint arXiv:2510.05865, 2025
Reference 18
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Observation 9a137dec-315b-47dc-a3d2-9405a4e418a0 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses On minimizing adversarial counterfactual error
Reference 19
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Observation 0b719c0a-ec99-4a39-9df7-94e34658f3dd · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Regret-based defense in adversarial reinforcement learning
Reference 20
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Observation 41f00fa2-d1d9-4298-8c44-af22c0bb7a4b · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models
Reference 21
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Observation 4d7c0755-abe0-4d3e-b9e2-ba813fc8206f · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses International ai safety report 2025: Second key update — technical safeguards and risk management.arXiv preprint arXiv:2511.19863, 2025
Reference 22
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Observation 7a1bb945-88cd-4d5d-ae95-252d8c956a48 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Hello Me, Meet the Real Me: Audio Deepfake Attacks on Voice Assistants
Reference 23
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Observation 25c74575-ac32-46ab-90f7-a8c291ac8bb7 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
Reference 24
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Observation d22b8089-740a-43d5-b076-056010af6a6a · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Securing the lane: Defences against patch attacks on autonomous vehicle’s lane detection
Reference 25
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Observation a0cbaf86-d11c-4fc0-ab1c-0d34c8aa740f · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses The emergence of adversarial communication in multi-agent reinforcement learning
Reference 26
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Observation a96d0c0d-71e9-4dc4-b8b4-bf9dadc87a50 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Stochastic model predictive control with a safety guarantee for automated driving.IEEE Transactions on Intelligent Vehicles, 2021
Reference 27
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Observation 03faf67c-47cb-41fe-9847-4b9a99787c9e · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Schoellig
Reference 28
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Observation 0e389ac7-803b-4063-966d-e201d288678c · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Unresolved cited work
Reference 29
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Observation e7a43be8-8667-49ad-8f7e-319ffca61573 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Diffusion models-based purification for common corruptions on robust 3d object detection.Sensors, 2024
Reference 30
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Observation 5b2a4dff-4daa-4428-aefd-1312540a0687 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Summit: A simulator for urban driving in massive mixed traffic
Reference 31
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Observation 27ad799c-08b8-46a0-977e-1e538c7fa33f · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Adversarial Objects Against LiDAR-Based Autonomous Driving Systems
Reference 32
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Observation eaa5cd61-4668-413c-ac06-e11e0aed094e · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Invisible for both camera and lidar: Security of multi-sensor fusion based perception in autonomous driving under physical-world attacks
Reference 33
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Observation 4630aa1f-7dac-483e-b508-9381debb4294 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Advdo: Realistic adversarial attacks for trajectory prediction
Reference 34
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Observation 95566bbf-eeff-4bed-969f-ec0558d192da · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Hidden voice commands
Reference 35
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Observation 6e41de10-e7e8-4a18-a36f-4c01eceb73ae · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Jeyapratap, Kaidi Xu, and Lifeng Zhou
Reference 36
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Observation ef91c7c9-770c-4354-8dd5-18c75e606fa0 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Heal: An empirical study on hallucinations in embodied agents driven by large language models.arXiv preprint arXiv:2506.15065, 2025
Reference 37
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Observation 5dcb48e6-dc9e-45bd-ab7e-b503f0c03196 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses If you're waiting for a sign... that might not be it! Mitigating Trust Boundary Confusion from Visual Injections on Vision-Language Agentic Systems
Reference 38
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Observation 758c4f41-4481-4d4f-bf5d-19bdb646ea7c · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Adversarial attacks on monocular pose estimation
Reference 39
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Observation 6e56db13-55bc-40db-b631-808d012d7340 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Adversary is on the road: Attacks on visual{SLAM} using unnoticeable adversarial patch
Reference 40
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Observation 5a00ea9e-e7e8-4e49-b109-94446b3535b4 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Alemzadeh, and Xugui Zhou
Reference 41
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Observation 5cc80e3d-aa62-4549-9e81-522fd4541f5b · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Tex3D: Objects as attack surfaces via adversarial 3D textures for vision-language-action models.arXiv preprint arXiv:2604.01618, 2026
Reference 42
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Observation 82e816bd-de5a-46b5-a5ee-ae7e88aa151d · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Lidattack: Robust black-box attack on lidar-based object detection
Reference 43
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Observation dc75f095-0770-49b3-978a-d9ce6cb2c2f1 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Towardsphysically-realizable adversarial attacks in embodied vision navigation
Reference 44
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Observation 2d3d2350-247c-4e62-8892-8bb4ac873651 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Safemind: Benchmarking and mitigating safety risks in embodied llm agents.arXiv preprint arXiv:2509.25885, 2025
Reference 45
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Observation d646946e-ca6d-4dfa-970d-257212892d6e · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Shapeshifter: Robust physical adversarial attack on faster r-cnn object detector
Reference 46
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Observation 4e4cd320-a621-4a99-9a00-ad398484d9b7 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Metamorph: Injecting inaudible commands into over-the-air voice controlled systems
Reference 47
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Observation 3a9f97ba-9005-4ae4-bc4d-c039ffeda039 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Catnips: Collision avoidance through neural implicit probabilistic scenes.IEEE Transactions on Robotics (T-RO), 2024
Reference 48
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Observation 98162ca2-4780-4a3f-86b2-8d244f95cd2a · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses SAFER-Splat: A Control Barrier Function for Safe Navigation with Online Gaussian Splatting Maps
Reference 49
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Observation 2a1d62ae-5ebd-44dc-80be-a03f2ac189d7 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Splat-nav: Safe real-time robot navigation in gaussian splatting maps.IEEE Transactions on Robotics (T-RO), 2025
Reference 50
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Observation e124d44a-2285-4c62-842f-d162b2142ba0 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Metawave: Attackingmmwavesensingwithmeta-material-enhancedtags
Reference 51
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Observation fc1089ea-c5cf-4d80-bc6a-7bf15bb3bbba · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Fouhey, and Joyce Chai
Reference 52
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Observation 7011f80d-5af1-492a-b5d1-53b5c7819c12 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Marnet: Backdoor attacks against cooperative multi-agent reinforcement learning.IEEE Transactions on Dependable and Secure Computing (TDSC), 2023
Reference 53
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Observation fe140c86-8ddd-4145-98a5-ac778255f2bf · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Diffusion policy attacker: Crafting adversarial attacks for diffusion- based policies
Reference 54
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Observation cfe3fa2a-947d-4ceb-88da-a7bc22d5faf6 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems
Reference 55
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Observation c54f00a5-7956-4953-a218-7415397c3f99 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Devil’s whisper: A general approach for physical adversarial attacks against commercial black-box speech recognition devices
Reference 56
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Observation 9f7794a7-1040-4feb-8cd8-bc0715ad5afe · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Agentpoison: Red-teaming llm agents via poisoning memory or knowledge bases
Reference 57
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Observation e6b85c10-3ca5-4cd9-b067-75352c5cbc56 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses DecodingTrust-Agent Platform (DTap): A Controllable and Interactive Red-Teaming Platform for AI Agents
Reference 58
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Observation 58a040f3-1a22-4412-a6d7-30dba030aa64 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses HazardArena: Evaluating Semantic Safety in Vision-Language-Action Models
Reference 59
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Observation 71584592-313e-4b69-93b3-27713a6a9db2 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Manipulation facing threats: Evaluating physical vulnerabilities in end-to-end vision language action models.arXiv preprint arXiv:2409.13174, 2024
Reference 60
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Observation 69c5a7c5-e289-46cf-bdb4-2d1e53b60d97 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Universal adversarial attack against 3d object tracking
Reference 61
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Observation 678d1c7a-3f39-477f-8621-e12919c558e9 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Black-box explainability-guided adversarial attack for 3d object tracking.IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2025
Reference 62
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Observation bb4b3920-e727-44a4-9589-1afba20a3797 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Physical attack on monocular depth estimation with optimal adversarial patches
Reference 63
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Observation c31b3345-1958-47f4-831d-5d988eab8885 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses ADoPT: LiDAR Spoofing Attack Detection Based on Point-Level Temporal Consistency
Reference 64
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Observation 2e7c7d5f-2b71-4870-87ca-870344188008 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Sentinet: Detecting localized universal attacks against deep learning systems
Reference 65
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Observation 9e2730a3-905a-4c1a-96a7-24606cd59049 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Gupta, Mykel J
Reference 66
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Observation f9b855b1-68f8-4cf8-b21b-674fb688ffcf · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Handover control for human-robot and robot-robot collaboration.Frontiers in Robotics and AI, 2021
Reference 67
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Observation 167d61be-9a20-4063-892e-2ff5420716bf · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Pybullet, a python module for physics simulation for games, robotics and machine learning.http://pybullet.org, 2016–2021
Reference 68
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Observation 7234eee3-3b92-4538-a584-3786325a2593 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Unveiling the Stealthy Threat: Analyzing Slow Drift GPS Spoofing Attacks for Autonomous Vehicles in Urban Environments and Enabling the Resilience
Reference 69
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Observation 7e59aaba-bdc2-44b9-af3e-163e8305fae4 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Navsim: Data-drivennon-reactiveautonomousvehiclesimulation and benchmarking
Reference 70
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Observation 7fd5d916-5edf-441d-8741-595344f4a076 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents
Reference 71
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Observation 68911bbe-3cb3-467e-81ad-de390d30e705 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Ai agents under threat: A survey of key security challenges and future pathways.ACM Computing Surveys, 2025
Reference 72
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Observation da983f2f-3592-46c2-b5b2-603697299cbb · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Learning to collide: An adaptive safety-critical scenarios generating method
Reference 73
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Observation 78916a14-065d-4d69-888f-c153e4e0ed5a · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Doan, Yingjie Lao, Peng Yang, and Ping Li
Reference 74
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Observation fd082a4f-d8e2-44ee-abcc-c09f3639b7f1 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Viewfool: Evaluating the robustness of visual recognition to adversarial viewpoints
Reference 75
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Observation aca29f42-57c1-4ba9-a504-8c56e780c08f · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Carla: An open urban driving simulator
Reference 76
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Observation 5c767250-2703-47c8-a227-857ca5014c24 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Human–robot object handover: Recent progress and future direction.Robotics, 2024
Reference 77
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Observation 602b054e-2d02-4e57-bcce-77dde3ecd453 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses TRAP: Tail-aware Ranking Attack for World-Model Planning
Reference 78
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Observation 5a9d0ca3-c15c-4bde-b007-37ffcee448f7 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses A robust multi-sensor fusion model against adversarial patch attack.Wireless Networks, 2026
Reference 79
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Observation 3f9b5485-f63b-40c2-906d-85ceb794a2f8 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Drones in distress: A game-theoretic countermeasure for protecting uavs against gps spoofing.IEEE Internet of Things Journal (IoT-J), 2019
Reference 80
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Observation ebc8b687-5da7-4549-992c-7ca5e03742b7 · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Robust physical-world attacks on deep learning visual classification
Reference 81
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Observation 042a58f3-0e26-4e62-a8eb-53802afccaf4 · outbound
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Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Adversarial attack on trajectory prediction for autonomous vehicles with generative adversarial networks
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Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
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Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment.Nature Communications, 2021
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Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Scenic: a language for scenario specification and data generation.Machine Learning (MLJ), 2023
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Observation 2fb2f3b1-11d6-48ef-a598-b8c8d4014a5f · outbound
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Random spoofing attack against lidar-based scan matching slam
Reference 95
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Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Exploring practical acoustic transduction attacks on inertial sensors in mdof systems.IEEE Transactions on Mobile Computing, 2023
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