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Paper Citation Record · LEDGER

Adversarial Attacks on Robotic Vision Language Action Models

As of 21 August 2026, this Paper Citation Record lists 95 of 95 outbound references and 20 inbound Pith citation observations for arXiv:2506.03350.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.03350 v1

Coverage vector

measured 95 of 95 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:11:40.585654Z

measured 115 of 115 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T10:45:44.770792Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T15:18:33.813982Z

Reference resolution

95 of 95 outbound references displayed

  • verified exact1
  • verified fuzzy11
  • unresolved80
  • parse uncertain1
  • malformed identifier1
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e2638f25-9f56-45b6-b855-3f8482c90f3b · outbound

This paper cites Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks.

Adversarial Attacks on Robotic Vision Language Action Models Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks

Reference 1

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source=pdf_text observed=2026-08-07T11:11:40.260798Z digest=sha256:0f7146256934975b63c689aa1ec9400a1a9862cc78b30f0a4131b75cd35145a8

Observation 0ed4bea1-ba5d-466b-8b89-6155f1232930 · outbound

This paper cites General-purpose foundation models for increased autonomy in robot-assisted surgery.Nature Machine Intelligence, pages 1–9, 2024.

Adversarial Attacks on Robotic Vision Language Action Models General-purpose foundation models for increased autonomy in robot-assisted surgery.Nature Machine Intelligence, pages 1–9, 2024

Reference 2

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source=pdf_text observed=2026-08-07T11:11:40.264747Z digest=sha256:94dcda83ddd2980a9c181689cf2713aefd9411e6d0c8f6b2c49784a12b43f881

Observation 0d042e41-996c-40a5-8a96-c74ece747715 · outbound

This paper cites Real-Time Anomaly Detection and Reactive Planning with Large Language Models.

Adversarial Attacks on Robotic Vision Language Action Models Real-Time Anomaly Detection and Reactive Planning with Large Language Models

Reference 3

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Observation 216ff483-159e-4104-945f-fd527514230b · outbound

This paper cites Dolphins: Multimodal language model for driving.

Adversarial Attacks on Robotic Vision Language Action Models Dolphins: Multimodal language model for driving

Reference 4

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source=pdf_text observed=2026-08-07T11:11:40.271537Z digest=sha256:674f5064ba6ab1662500173732a3a09c20a4eda603d6eea81e2d5c3a7fe50099

Observation 923b1969-c838-4021-8026-4f628b6de4be · outbound

This paper cites GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models.

Adversarial Attacks on Robotic Vision Language Action Models GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models

Reference 5

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local_arxiv, observed 2026-08-07T11:11:41.550160Z

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source=pdf_text observed=2026-08-07T11:11:40.274805Z digest=sha256:7b16f11915edd289fed0afadf2e745523dc4de1ada08312deb71e3120fbc0fec

Observation 807cd2f2-78e2-4d0c-94a7-ae0358f52552 · outbound

This paper cites Large language models can help boost food production, but be mindful of their risks.Frontiers in Artificial Intelligence, 7: 1326153, 2024.

Adversarial Attacks on Robotic Vision Language Action Models Large language models can help boost food production, but be mindful of their risks.Frontiers in Artificial Intelligence, 7: 1326153, 2024

Reference 6

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Observation fdcea2ce-bd8d-44ae-a516-318f872a4e76 · outbound

This paper cites Master plan.

Adversarial Attacks on Robotic Vision Language Action Models Master plan

Reference 7

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Observation 9e218c89-4030-44c4-af9a-5901d2ad0205 · outbound

This paper cites Unitree go2.

Adversarial Attacks on Robotic Vision Language Action Models Unitree go2

Reference 8

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source=pdf_text observed=2026-08-07T11:11:40.288010Z digest=sha256:7f83c0b928e9ffcace7ed5c1549f95a271e6ae6959c518ef75a677858d6e02e4

Observation b849afe8-229e-477a-9709-267e2a35504b · outbound

This paper cites $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control.

Adversarial Attacks on Robotic Vision Language Action Models $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 9

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source=pdf_text observed=2026-08-07T11:11:40.294364Z digest=sha256:b3dd57ed727d1aa2e48e0bd2adeeed79e15e9e9a0d104801945667dc61a796b3

Observation 5f28870d-87a9-47b5-aef3-fddf90adbc18 · outbound

This paper cites Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs.

Adversarial Attacks on Robotic Vision Language Action Models Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs

Reference 11

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source=pdf_text observed=2026-08-07T11:11:40.301692Z digest=sha256:d94dbbefa32f8ef943f237d187663d7cb6d522e45e1a05ffa965fbef17a759c7

Observation aeef1849-75b8-4419-8cf8-576ad109c65c · outbound

This paper cites Autort: Embodied foundation models for large scale orchestration of robotic agents.arXiv preprint arXiv:2401.12963, 2024.

Adversarial Attacks on Robotic Vision Language Action Models Autort: Embodied foundation models for large scale orchestration of robotic agents.arXiv preprint arXiv:2401.12963, 2024

Reference 12

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Observation c75cd383-58c4-4964-8eca-3ea296dfcb39 · outbound

This paper cites AI Control: Improving Safety Despite Intentional Subversion.

Adversarial Attacks on Robotic Vision Language Action Models AI Control: Improving Safety Despite Intentional Subversion

Reference 13

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source=pdf_text observed=2026-08-07T11:11:40.307636Z digest=sha256:042085a984a5327f4f1f2f4752f77eab9e761ba5bde8e36922432cef737f944d

Observation 2c9b4ea0-f4f4-4e86-938a-730a22d769cc · outbound

This paper cites Alignment faking in large language models.

Adversarial Attacks on Robotic Vision Language Action Models Alignment faking in large language models

Reference 14

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source=pdf_text observed=2026-08-07T11:11:40.310964Z digest=sha256:0977114fbd80b61bfd295810f9495c03cb5e12fc4fa1e2cc6d6db5e613909b0e

Observation 15125cb8-ae57-4f43-8ec1-8a7bf3d9eb45 · outbound

This paper cites Adversaries Can Misuse Combinations of Safe Models.

Adversarial Attacks on Robotic Vision Language Action Models Adversaries Can Misuse Combinations of Safe Models

Reference 15

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source=pdf_text observed=2026-08-07T11:11:40.314308Z digest=sha256:0d8eee650ecc76689c62d37924c9fad732807d924343e7b8d1cf2f9e33bd9725

Observation 239da7d6-d643-4676-b3fa-84a0c4a054ff · outbound

This paper cites Agentdojo: A dynamic environment to evaluate prompt injection attacks and defenses for llm agents.

Adversarial Attacks on Robotic Vision Language Action Models Agentdojo: A dynamic environment to evaluate prompt injection attacks and defenses for llm agents

Reference 16

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source=pdf_text observed=2026-08-07T11:11:40.317535Z digest=sha256:d6c4cf860945de544da423515f2d0718c8856ed8905ded28786688c1233dfad6

Observation 627b5ad0-cdfa-4cd6-8663-ea090ef0c1c8 · outbound

This paper cites Prompt Injection attack against LLM-integrated Applications.

Adversarial Attacks on Robotic Vision Language Action Models Prompt Injection attack against LLM-integrated Applications

Reference 17

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source=pdf_text observed=2026-08-07T11:11:40.321516Z digest=sha256:4224f438e430013a52faaea29229023fae400ce2e6f0a6d466881ffc9db0bd57

Observation 5620d33d-b301-43fa-8769-f67b108b3bba · outbound

This paper cites Defeating Prompt Injections by Design.

Adversarial Attacks on Robotic Vision Language Action Models Defeating Prompt Injections by Design

Reference 18

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source=pdf_text observed=2026-08-07T11:11:40.324656Z digest=sha256:41ef4d8f436adc2eafa8213e6f26adf9342e8f6fb3381bc6aaa4afbbbf63a395

Observation 7032056c-c5ad-463d-80e0-340015449779 · outbound

This paper cites Jailbreaking Black Box Large Language Models in Twenty Queries.

Adversarial Attacks on Robotic Vision Language Action Models Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 19

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source=pdf_text observed=2026-08-07T11:11:40.327715Z digest=sha256:0360946a86bdaacfc839a1b73701282a41d4a652346da5c1ae491b26f5a0336b

Observation 15cc6568-1e5f-4cf0-9f70-c3905eea6629 · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

Adversarial Attacks on Robotic Vision Language Action Models Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 20

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source=pdf_text observed=2026-08-07T11:11:40.330830Z digest=sha256:ff5d8ead1a3114d83e38953b1d7f80e33bdf6f7497159d4c7333e0e95d3e5dc5

Observation a04a7ecb-8456-4aae-89f4-caa07a960682 · outbound

This paper cites JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models.

Adversarial Attacks on Robotic Vision Language Action Models JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models

Reference 21

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Observation 9ed4e4c6-625a-4d4c-9959-08a987e02d8b · outbound

This paper cites Is Power-Seeking AI an Existential Risk?.

Adversarial Attacks on Robotic Vision Language Action Models Is Power-Seeking AI an Existential Risk?

Reference 22

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source=pdf_text observed=2026-08-07T11:11:40.338107Z digest=sha256:2d6d33282a46aa2ce51ef248e411d0bcaf0818e14ced670c9280807b4538bb19

Observation 95933152-0f97-4195-a734-497d345c0a15 · outbound

This paper cites Risks from Learned Optimization in Advanced Machine Learning Systems.

Adversarial Attacks on Robotic Vision Language Action Models Risks from Learned Optimization in Advanced Machine Learning Systems

Reference 23

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source=pdf_text observed=2026-08-07T11:11:40.341689Z digest=sha256:667646f4168151635776162f436622ad42460e44d79a81b81a2580516ede750a

Observation b1007874-5152-4618-bab8-ab684268a9b2 · outbound

This paper cites Deceptive Alignment Monitoring.

Adversarial Attacks on Robotic Vision Language Action Models Deceptive Alignment Monitoring

Reference 24

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source=pdf_text observed=2026-08-07T11:11:40.345067Z digest=sha256:a29efb7ae9d23c0ef4b8f5a8190e551a4c00babd58a8067e72c64a045a37e39a

Observation fa19d45b-42b9-4813-a6bc-984737b7cbcb · outbound

This paper cites RepliBench: Evaluating the Autonomous Replication Capabilities of Language Model Agents.

Adversarial Attacks on Robotic Vision Language Action Models RepliBench: Evaluating the Autonomous Replication Capabilities of Language Model Agents

Reference 25

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source=pdf_text observed=2026-08-07T11:11:40.348373Z digest=sha256:da35eeb1b39da6bc24fdf0064a3995474c2f4757676f9a9841066f8c8fd8b012

Observation 826b1a83-6390-40fe-a2cf-1acf2962ef68 · outbound

This paper cites Frontier AI systems have surpassed the self-replicating red line.

Adversarial Attacks on Robotic Vision Language Action Models Frontier AI systems have surpassed the self-replicating red line

Reference 26

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source=pdf_text observed=2026-08-07T11:11:40.351607Z digest=sha256:487c847282ba707d59d78ac9b29462e8e391f21b26c78a93d10ebbf0724c88b6

Observation fbeca933-5ef0-4c09-a27d-b0aab4da468c · outbound

This paper cites OpenVLA: An Open-Source Vision-Language-Action Model.

Adversarial Attacks on Robotic Vision Language Action Models OpenVLA: An Open-Source Vision-Language-Action Model

Reference 27

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source=pdf_text observed=2026-08-07T11:11:40.354837Z digest=sha256:df4c8541b95b3642ab4131eddec6c7947f5e30cc1567f5e83f4bf3e0ee532272

Observation ff9f4791-6992-44c6-ac4d-f862331dac1a · outbound

This paper cites Jailbreaking LLM-Controlled Robots.

Adversarial Attacks on Robotic Vision Language Action Models Jailbreaking LLM-Controlled Robots

Reference 29

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source=pdf_text observed=2026-08-07T11:11:40.362352Z digest=sha256:515a3f2de9852d5f2f4e282aaeb8afe43d341dd40cb962b7c200ee382c71e86c

Observation 60a2b6a8-73f4-4b1e-81fe-9fa461df6dbb · outbound

This paper cites BadRobot: Jailbreaking Embodied LLM Agents in the Physical World.

Adversarial Attacks on Robotic Vision Language Action Models BadRobot: Jailbreaking Embodied LLM Agents in the Physical World

Reference 30

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source=pdf_text observed=2026-08-07T11:11:40.366090Z digest=sha256:619987b4ad2fd8939b1d4a22b477cb5ae158e027a69dfc2f65380e9ca6bdd3b3

Observation dc736ca6-a9ab-48f1-b599-11ffec3ed84c · outbound

This paper cites Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates.

Adversarial Attacks on Robotic Vision Language Action Models Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates

Reference 31

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source=pdf_text observed=2026-08-07T11:11:40.369464Z digest=sha256:6d32760e8697838ba8cacd564463eade42d8f4369ebd4fb7c3a99c7f5e18483e

Observation ff4cf46c-6222-420c-8f09-37e79d8e7e0a · outbound

This paper cites End-to-End Training of Deep Visuomotor Policies.

Adversarial Attacks on Robotic Vision Language Action Models End-to-End Training of Deep Visuomotor Policies

Reference 32

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source=pdf_text observed=2026-08-07T11:11:40.372910Z digest=sha256:727fc782568ab774003ef5d574cd18318ac3493594f9f949daef5f249e271f06

Observation a7fe9501-ed00-4812-91cc-c61a19391ff4 · outbound

This paper cites R3M: A Universal Visual Representation for Robot Manipulation.

Adversarial Attacks on Robotic Vision Language Action Models R3M: A Universal Visual Representation for Robot Manipulation

Reference 33

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source=pdf_text observed=2026-08-07T11:11:40.376578Z digest=sha256:de9d9a150ff1856b4749205c2374609577e8f929874a3f1234f1092c4856e148

Observation 64958992-f9da-4280-83fc-1fd88992192d · outbound

This paper cites Attention is all you need.Advances in Neural Information Processing Systems, 30, 2017.

Adversarial Attacks on Robotic Vision Language Action Models Attention is all you need.Advances in Neural Information Processing Systems, 30, 2017

Reference 34

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source=pdf_text observed=2026-08-07T11:11:40.379677Z digest=sha256:5e9c302fe7913131190919e2b288e8a2837bac92f48d25303f7b2ca12cd5acca

Observation ed30fb58-ae09-437d-88f1-54a29a92197e · outbound

This paper cites ChatGPT for Robotics: Design Principles and Model Abilities.

Adversarial Attacks on Robotic Vision Language Action Models ChatGPT for Robotics: Design Principles and Model Abilities

Reference 35

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source=pdf_text observed=2026-08-07T11:11:40.383134Z digest=sha256:8a59f448dde3f958d4bc76d5191fc5d75871dd68d9ca93d2793c1800c0da92a6

Observation f9a246e9-9350-46d2-ab56-99c0ae7dac44 · outbound

This paper cites Code as policies: Language model programs for embodied control.

Adversarial Attacks on Robotic Vision Language Action Models Code as policies: Language model programs for embodied control

Reference 36

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source=pdf_text observed=2026-08-07T11:11:40.386287Z digest=sha256:85b408f29516f4b44755d84b4bdc8c897ddbca3f19df4179f5866c06408900c1

Observation ed74ab90-1e54-4a0b-96a9-175150c9720d · outbound

This paper cites How to prompt your robot: A promptbook for manipulation skills with code as policies.

Adversarial Attacks on Robotic Vision Language Action Models How to prompt your robot: A promptbook for manipulation skills with code as policies

Reference 37

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source=pdf_text observed=2026-08-07T11:11:40.389639Z digest=sha256:a94a589233fcde0b280b035b7ed554dcfc73aa316c2f164ce782ae72558e3375

Observation 97a6686b-e2bc-4fae-a4a7-c505ef5d0344 · outbound

This paper cites Chatgpt for robotics: Design principles and model abilities.IEEE Access, 2024.

Adversarial Attacks on Robotic Vision Language Action Models Chatgpt for robotics: Design principles and model abilities.IEEE Access, 2024

Reference 38

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source=pdf_text observed=2026-08-07T11:11:40.392918Z digest=sha256:7a56a96fd8e5ec68eec676e2d859af9dc541043088e45e305b28ce7aeb9ace36

Observation ae916383-166f-43d2-b1cc-f7be2bc7b023 · outbound

This paper cites Driving Everywhere with Large Language Model Policy Adaptation.

Adversarial Attacks on Robotic Vision Language Action Models Driving Everywhere with Large Language Model Policy Adaptation

Reference 39

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source=pdf_text observed=2026-08-07T11:11:40.396219Z digest=sha256:2488f8c3e34be559c8edc912d134d4a13423581a5805200d4006e41823cac629

Observation fe66ef49-e91d-4a3b-9f6f-139d43a6edce · outbound

This paper cites A Survey on Multimodal Large Language Models for Autonomous Driving.

Adversarial Attacks on Robotic Vision Language Action Models A Survey on Multimodal Large Language Models for Autonomous Driving

Reference 40

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source=pdf_text observed=2026-08-07T11:11:40.399503Z digest=sha256:8cbc786da937ed79b22d45ba404f1be8d42a0fd33dfb1ae382f30ed0bfb52fad

Observation 5ea615c3-fa3b-4d4e-a69f-a9ace6959a2a · outbound

This paper cites Deploying and evaluating llms to program service mobile robots.IEEE Robotics and Automation Letters, 9(3):2853–2860, March 2024.

Adversarial Attacks on Robotic Vision Language Action Models Deploying and evaluating llms to program service mobile robots.IEEE Robotics and Automation Letters, 9(3):2853–2860, March 2024

Reference 41

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source=pdf_text observed=2026-08-07T11:11:40.403344Z digest=sha256:4f0b98887826c5a681185f902f854d9008cabc2398436344cfa46813f73accc4

Observation 111d9616-9573-4ba0-8012-40f4128e90dc · outbound

This paper cites SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Task Planning.

Adversarial Attacks on Robotic Vision Language Action Models SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Task Planning

Reference 42

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source=pdf_text observed=2026-08-07T11:11:40.406294Z digest=sha256:ff611af8af17bc970ac6f29feca2a9e1a22b991198417082539d6ba9444f2d58

Observation bbcfb905-0bb3-41db-b88e-b94ea0a40760 · outbound

This paper cites Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240): 1–113, 2023.

Adversarial Attacks on Robotic Vision Language Action Models Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240): 1–113, 2023

Reference 43

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source=pdf_text observed=2026-08-07T11:11:40.409342Z digest=sha256:54e491b4f525673c83f5695c3f3190b0725b7d8ff8deccd7ae7cf0bca1002c75

Observation 7d02eae8-43dc-4400-8256-542870d7463b · outbound

This paper cites Scaling Vision Transformers to 22 Billion Parameters.

Adversarial Attacks on Robotic Vision Language Action Models Scaling Vision Transformers to 22 Billion Parameters

Reference 44

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source=pdf_text observed=2026-08-07T11:11:40.412772Z digest=sha256:2429342a9d2a35f48f09bd7aa6364994076d8687c08b9fe9cdcf16c59dbb3b46

Observation c02606e4-4990-47a7-941b-403485f46814 · outbound

This paper cites PaLM-E: An Embodied Multimodal Language Model.

Adversarial Attacks on Robotic Vision Language Action Models PaLM-E: An Embodied Multimodal Language Model

Reference 45

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source=pdf_text observed=2026-08-07T11:11:40.415933Z digest=sha256:45516629b67a9a41c9586e88010fd3e08cc2bd1b24e9a599d7fd8a55b035fa78

Observation de04ff54-09b2-4422-a018-6f585975f8e8 · outbound

This paper cites SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning.

Adversarial Attacks on Robotic Vision Language Action Models SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning

Reference 46

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source=pdf_text observed=2026-08-07T11:11:40.419092Z digest=sha256:fd755b6b052ba98a2b1a0a1f95b6314551c5f8dc503306960ceb3d7a47776140

Observation 5d9b344b-c9f6-4d16-81e2-297627a67e6d · outbound

This paper cites Tenenbaum, Antonio Torralba, Florian Shkurti, and Liam Paull.

Adversarial Attacks on Robotic Vision Language Action Models Tenenbaum, Antonio Torralba, Florian Shkurti, and Liam Paull

Reference 47

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source=pdf_text observed=2026-08-07T11:11:40.422422Z digest=sha256:f1eae87b1bd5ca8aeb80abfce45d5388cc49a48f9bbf5801eb1870e37b64a3fc

Observation 454e0696-c079-4d0e-909f-a5b833d1a46b · outbound

This paper cites An embodied generalist agent in 3d world,.

Adversarial Attacks on Robotic Vision Language Action Models An embodied generalist agent in 3d world,

Reference 48

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source=pdf_text observed=2026-08-07T11:11:40.425459Z digest=sha256:30d087c8167b944da006aa1e554982b95015d2815f6fee529453ab4aa6bc351a

Observation e366fbbc-a98d-45d6-a9fd-282cfbe72334 · outbound

This paper cites Do As I Can, Not As I Say: Grounding Language in Robotic Affordances.

Adversarial Attacks on Robotic Vision Language Action Models Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Reference 49

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source=pdf_text observed=2026-08-07T11:11:40.431994Z digest=sha256:e2fc46cbdfe685ff82859a033203213050b5e9551378be4c321640e6164a7975

Observation 69f8b978-f345-4e2d-8ad3-13de2d76af9b · outbound

This paper cites Octo: An open-source generalist robot policy.

Adversarial Attacks on Robotic Vision Language Action Models Octo: An open-source generalist robot policy

Reference 50

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raw_fallback, observed 2026-08-07T11:11:43.107063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T11:11:40.435203Z digest=sha256:214a9944c4d9101ace96dc83a9623099e483594b770ae0fe61afc645bebb9f90

Observation 8f7cc1a5-e099-48d0-a700-894ab31e8269 · outbound

This paper cites RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

Adversarial Attacks on Robotic Vision Language Action Models RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 51

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source=pdf_text observed=2026-08-07T11:11:40.438695Z digest=sha256:8a7151359464cde564cab25d6c22d955a3784d5a71f30d9820b2cfefe60b80c9

Observation e8fbd981-45eb-48d6-9617-ce72bf99ee65 · outbound

This paper cites Open X-Embodiment: Robotic Learning Datasets and RT-X Models.

Adversarial Attacks on Robotic Vision Language Action Models Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Reference 52

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source=pdf_text observed=2026-08-07T11:11:40.441575Z digest=sha256:4acab94d4c557fe7583acb3d21468d7fd2de3fd8c5ddd29fde6aaabafae20e32

Observation 85fc64ee-2038-44b9-b857-3bf9cc06b2e3 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Adversarial Attacks on Robotic Vision Language Action Models Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 53

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source=pdf_text observed=2026-08-07T11:11:40.445540Z digest=sha256:29f598cd0368be707614cd19248b0b1726a0bc0c6e0fcf234da77a97f17d3f3c

Observation 8aace697-34ed-43c3-914a-50c88905b990 · outbound

This paper cites CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation.

Adversarial Attacks on Robotic Vision Language Action Models CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation

Reference 54

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source=pdf_text observed=2026-08-07T11:11:40.448850Z digest=sha256:4e0d6e08df1c7912fe807146bd907fde7117e74083d92d90f9f381bfb78e1698

Observation 1dd9723d-b75f-4f4b-8e87-6aa97ceca224 · outbound

This paper cites A General Language Assistant as a Laboratory for Alignment.

Adversarial Attacks on Robotic Vision Language Action Models A General Language Assistant as a Laboratory for Alignment

Reference 55

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source=pdf_text observed=2026-08-07T11:11:40.452007Z digest=sha256:9c813a9fd084a50c806996dfc3bfa0ce6829990534c91972d8940b1ce7abb311

Observation 3b1a64ec-e3a2-4458-b5c2-5f6b6dabd9e5 · outbound

This paper cites Regulating chatgpt and other large generative ai models.

Adversarial Attacks on Robotic Vision Language Action Models Regulating chatgpt and other large generative ai models

Reference 56

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verified fuzzy
raw_fallback, observed 2026-08-07T11:11:42.948942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T11:11:40.455122Z digest=sha256:ec7d5d1adcc022c30d5368507cd24555e8ec51b28960e8a6034edbbe7a0c911a

Observation 1f2ed3ec-f3c5-4faa-9f7c-bb8f1d2a5a3d · outbound

This paper cites Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744, 2022.

Adversarial Attacks on Robotic Vision Language Action Models Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744, 2022

Reference 57

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raw_fallback, observed 2026-08-07T11:11:42.782514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T11:11:40.458043Z digest=sha256:377dc3ade4ac0be4a9b7fab2936642957293c0e51d850161838506d896485cb3

Observation a4bffa5c-d692-42cd-992c-46a087197421 · outbound

This paper cites Jailbroken: How does llm safety training fail?Advances in Neural Information Processing Systems, 36, 2024.

Adversarial Attacks on Robotic Vision Language Action Models Jailbroken: How does llm safety training fail?Advances in Neural Information Processing Systems, 36, 2024

Reference 58

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raw_fallback, observed 2026-08-07T11:11:42.655018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T11:11:40.461177Z digest=sha256:7b59739a0e5d26e133d6de20ea5c9ef0bf390e58f34d43b3f92c80e5b1613b96

Observation fdc1b322-6605-44f4-92bc-3a0dbb4173f5 · outbound

This paper cites Are aligned neural networks adversarially aligned?Advances in Neural Information Processing Systems, 36, 2024.

Adversarial Attacks on Robotic Vision Language Action Models Are aligned neural networks adversarially aligned?Advances in Neural Information Processing Systems, 36, 2024

Reference 59

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raw_fallback, observed 2026-08-07T11:11:42.548810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T11:11:40.464457Z digest=sha256:57f97fbea59a5d7fa58da7274e0ab1dbb24ec9db5e13fb1d5c479adf5c2e8799

Observation 878ebc4a-d52b-422e-8869-dff158b29204 · outbound

This paper cites AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents.

Adversarial Attacks on Robotic Vision Language Action Models AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents

Reference 60

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source=pdf_text observed=2026-08-07T11:11:40.467496Z digest=sha256:619ec30fe580bcada647ac9e505939a6f693492492928cdda26876f7b81bc628

Observation 7a5db577-51ce-41a8-a94c-fd43ca12a759 · outbound

This paper cites Frontier Models are Capable of In-context Scheming.

Adversarial Attacks on Robotic Vision Language Action Models Frontier Models are Capable of In-context Scheming

Reference 61

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source=pdf_text observed=2026-08-07T11:11:40.470984Z digest=sha256:cc6833255cae3d5b1ac2d240040e3d0dce32fd9f6ab881474f0bfb0e8a362892

Observation 81792aed-f23f-4c4b-b15f-3b7ae48bef03 · outbound

This paper cites Stress-Testing Capability Elicitation With Password-Locked Models.

Adversarial Attacks on Robotic Vision Language Action Models Stress-Testing Capability Elicitation With Password-Locked Models

Reference 62

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source=pdf_text observed=2026-08-07T11:11:40.474093Z digest=sha256:3c3051c26b578135b17bc718f100d40f112a72ee60a62310d09ba5151847acc2

Observation e41ed12b-0b24-4dd6-9bb1-47952bab1fa8 · outbound

This paper cites A Safe Harbor for AI Evaluation and Red Teaming.

Adversarial Attacks on Robotic Vision Language Action Models A Safe Harbor for AI Evaluation and Red Teaming

Reference 63

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source=pdf_text observed=2026-08-07T11:11:40.477043Z digest=sha256:1b6d4f3a74fa079f916e360cc3b2f772c7fcb378199a2eda48f5f53a0931dab6

Observation a50f1dbe-d4a2-4ed6-bbeb-2ce01e49fb71 · outbound

This paper cites Open Problems in Technical AI Governance.

Adversarial Attacks on Robotic Vision Language Action Models Open Problems in Technical AI Governance

Reference 64

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source=pdf_text observed=2026-08-07T11:11:40.480206Z digest=sha256:063fa0b84ce7568fac79d058e90a79afaf4418dff6ee033086bf7299c214acb2

Observation 3e69ab2f-f2d8-4b74-a925-d5125038e49d · outbound

This paper cites AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models.

Adversarial Attacks on Robotic Vision Language Action Models AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models

Reference 65

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source=pdf_text observed=2026-08-07T11:11:40.483280Z digest=sha256:55a18bdd898c7d65957c39ff59902146a834514a3a13dd4f18e9fe61cb6cd371

Observation b370fc59-43ab-4246-9e74-508814b52dfa · outbound

This paper cites Visual adversarial examples jailbreak aligned large language models.

Adversarial Attacks on Robotic Vision Language Action Models Visual adversarial examples jailbreak aligned large language models

Reference 66

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raw_fallback, observed 2026-08-07T11:11:42.405908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T11:11:40.486909Z digest=sha256:6f5acd00abf701a32bc597c8a7dd4a0ea8ffc87c90d5ad2846bd4ae9ec8498b8

Observation ea5b7d3f-4fcd-41a9-bb98-f17d2047398d · outbound

This paper cites LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet.

Adversarial Attacks on Robotic Vision Language Action Models LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet

Reference 67

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source=pdf_text observed=2026-08-07T11:11:40.490378Z digest=sha256:35880689bc18d989bc629301fee94766121e6f09a9af1bacc02ab74af3ea46ef

Observation aed2d884-f9d4-4301-9d15-bace8a2c0e14 · outbound

This paper cites Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack.

Adversarial Attacks on Robotic Vision Language Action Models Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack

Reference 68

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source=pdf_text observed=2026-08-07T11:11:40.493528Z digest=sha256:8f476926df86f99fe8362e86c64754ea0808c795ac113cc8c84e97fa44b52254

Observation 7ecf9ed4-023e-45d3-aeec-6358a25a357e · outbound

This paper cites Improving alignment and robustness with circuit breakers.

Adversarial Attacks on Robotic Vision Language Action Models Improving alignment and robustness with circuit breakers

Reference 69

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raw_fallback, observed 2026-08-07T11:11:42.312697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T11:11:40.497097Z digest=sha256:ec1835cf6fd5962f2185ef391c97b8282c30f84f8b4a9616b19c0dd3b03eee8a

Observation f9bc86cb-b49f-4797-a4a8-b8e8efc55350 · outbound

This paper cites SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks.

Adversarial Attacks on Robotic Vision Language Action Models SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks

Reference 70

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source=pdf_text observed=2026-08-07T11:11:40.500019Z digest=sha256:59e89876a392ead76725c3c609c7c4eeb8303355e1673b51a3ca7ce69d189d2a

Observation c8c0b561-2586-42fe-ba9a-8ef06af55522 · outbound

This paper cites OpenAI o1 System Card.

Adversarial Attacks on Robotic Vision Language Action Models OpenAI o1 System Card

Reference 71

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source=pdf_text observed=2026-08-07T11:11:40.503166Z digest=sha256:9bcbcfaa1b2b0b573a5b499098701ef7bbac6c85ad1d4b8581251a9a88d5fc5b

Observation dfc03a1d-a0fc-48df-9b31-4379c8b5300e · outbound

This paper cites The Llama 3 Herd of Models.

Adversarial Attacks on Robotic Vision Language Action Models The Llama 3 Herd of Models

Reference 72

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source=pdf_text observed=2026-08-07T11:11:40.506433Z digest=sha256:d2d645046c14531561696b8cf0494029ab20fa48ee32cf8803ce53b00d81574f

Observation 98b16650-4cd2-4544-817b-1fc5a857bd5b · outbound

This paper cites Dissecting Adversarial Robustness of Multimodal LM Agents.

Adversarial Attacks on Robotic Vision Language Action Models Dissecting Adversarial Robustness of Multimodal LM Agents

Reference 73

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source=pdf_text observed=2026-08-07T11:11:40.509846Z digest=sha256:4dc42ad1e9a6c5346273cca34bbfc0b5396b7a1db073f83d74db9278ac0aa923

Observation f3f856b1-b0bf-43af-a1cc-44626b5a8be5 · outbound

This paper cites BadAgent: Inserting and Activating Backdoor Attacks in LLM Agents.

Adversarial Attacks on Robotic Vision Language Action Models BadAgent: Inserting and Activating Backdoor Attacks in LLM Agents

Reference 74

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source=pdf_text observed=2026-08-07T11:11:40.513409Z digest=sha256:105a9e772cc17c5652c3a9387c92c6d95a09192c591d99eb6e8091c50e7aedff

Observation 5365cf22-08b9-4730-9778-c3bf52a2e170 · outbound

This paper cites Adversarial Search Engine Optimization for Large Language Models.

Adversarial Attacks on Robotic Vision Language Action Models Adversarial Search Engine Optimization for Large Language Models

Reference 75

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source=pdf_text observed=2026-08-07T11:11:40.516945Z digest=sha256:a7e3877608e6397c300174fc2573c225835cdc2ab5e7dc5079f73cfad85bc93d

Observation a04af57b-0804-441e-9000-44ea52aa744a · outbound

This paper cites Embodied Red Teaming for Auditing Robotic Foundation Models.

Adversarial Attacks on Robotic Vision Language Action Models Embodied Red Teaming for Auditing Robotic Foundation Models

Reference 76

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source=pdf_text observed=2026-08-07T11:11:40.520328Z digest=sha256:2ee451c695b58baba1bd504330d514fcaf6966e352f35f94794d48b4cd04048a

Observation 1fba4143-4ab4-4792-8c3a-9c11a96de4a1 · outbound

This paper cites Intriguing properties of neural networks.

Adversarial Attacks on Robotic Vision Language Action Models Intriguing properties of neural networks

Reference 77

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source=pdf_text observed=2026-08-07T11:11:40.523904Z digest=sha256:6ae622102b2442e033b223bf8aeab0bee6b6e013ce54badffc8ce41487f09657

Observation 2d667f05-5244-47b9-886d-539fdc725335 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Adversarial Attacks on Robotic Vision Language Action Models Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 78

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:11:40.527358Z digest=sha256:11620180c3acb11d2baebed6ed9e8a106cdf3b41e456d6c77898ad484b10b5f1

Observation 3c0bbdf6-817c-4bd0-a666-601b851f6a74 · outbound

This paper cites Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation.

Adversarial Attacks on Robotic Vision Language Action Models Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation

Reference 79

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source=pdf_text observed=2026-08-07T11:11:40.530536Z digest=sha256:1237f1f24d117609b2007e40644fbc51a5ad07549ea0eeae4603645065c14364

Observation a40bcbbd-e932-475c-a08d-3e8861d51d2d · outbound

This paper cites HYDRA: Hybrid Robot Actions for Imitation Learning.

Adversarial Attacks on Robotic Vision Language Action Models HYDRA: Hybrid Robot Actions for Imitation Learning

Reference 80

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source=pdf_text observed=2026-08-07T11:11:40.534332Z digest=sha256:ecfdd9355caaebf3c8677476a03f16951f94d4f0082426fb9b979f1d7d971c8e

Observation bc765adb-df3b-4956-a7c0-7499f5f3f0f0 · outbound

This paper cites Evaluating Real-World Robot Manipulation Policies in Simulation.

Adversarial Attacks on Robotic Vision Language Action Models Evaluating Real-World Robot Manipulation Policies in Simulation

Reference 81

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source=pdf_text observed=2026-08-07T11:11:40.538116Z digest=sha256:80d4c6c3a96c2c0ed478770b34cb5f62f9faabcc4d06f3d02b83223188b7f044

Observation 149d46b6-f86a-47fa-82d7-2d7afc0d3ee0 · outbound

This paper cites TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies.

Adversarial Attacks on Robotic Vision Language Action Models TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies

Reference 82

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:11:40.541709Z digest=sha256:31dfe36e520b441fcb1e36b102aa66fee86723157d615734ee531d6b81a60bee

Observation fd157157-cf82-437d-b9f5-72fad7cbaeb3 · outbound

This paper cites GitHub - allenzren/open-pi-zero: Re-implementation of pi0 vision-language-action (VLA) model from Physical Intelligence — github.com.

Adversarial Attacks on Robotic Vision Language Action Models GitHub - allenzren/open-pi-zero: Re-implementation of pi0 vision-language-action (VLA) model from Physical Intelligence — github.com

Reference 83

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raw_fallback, observed 2026-08-07T11:11:42.134928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T11:11:40.544923Z digest=sha256:7910046150ec615954152c4b635cc32e0c8a364946674fdacdbfc46d9910c564

Observation 0634483d-8172-449a-bfcc-5c817b073cd5 · outbound

This paper cites Failures to find transferable image jailbreaks between vision-language models.

Adversarial Attacks on Robotic Vision Language Action Models Failures to find transferable image jailbreaks between vision-language models

Reference 84

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verified fuzzy
raw_fallback, observed 2026-08-07T11:11:41.952328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T11:11:40.547821Z digest=sha256:ff5351c10f6df78e1211007ba471baae6301a1a413b30d4fd370572ac75ea1c8

Observation 60f67dd0-5532-4871-9215-078dcc65b23a · outbound

This paper cites Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations.

Adversarial Attacks on Robotic Vision Language Action Models Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations

Reference 85

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:11:40.551096Z digest=sha256:201af96f6bf2ee071202c43939eae2dd20ddd16938d4d541067c2fe713b2ac02

Observation d1f6c3b4-8554-4a04-9e30-2071d3ed7753 · outbound

This paper cites Baseline Defenses for Adversarial Attacks Against Aligned Language Models.

Adversarial Attacks on Robotic Vision Language Action Models Baseline Defenses for Adversarial Attacks Against Aligned Language Models

Reference 86

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source=pdf_text observed=2026-08-07T11:11:40.554404Z digest=sha256:7b7ef380c2aa6b0930d8d0c2dec6f55df3c4370cbf74cfa0b8617d59f00d32ef

Observation e2885798-5bb7-4b89-90b1-3f90f216a57e · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Adversarial Attacks on Robotic Vision Language Action Models Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 87

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no resolver link, observed 2026-08-07T11:11:40.557735Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:11:40.557735Z digest=sha256:081354c6c1f249522eb7a9b71b0b9d23aae7916238abbb56942db00367f66f06

Observation 90d52dea-0a17-49c8-9875-b97aa91dd09c · outbound

This paper cites Deep reinforcement learning from human preferences.

Adversarial Attacks on Robotic Vision Language Action Models Deep reinforcement learning from human preferences

Reference 88

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:11:40.561507Z digest=sha256:510ccdd341739e8964463cee613e95003624b41d862d81775bb522382e54d750

Observation 45a0b585-35d0-4e79-b213-a2c76e17bc74 · outbound

This paper cites Scalable agent alignment via reward modeling: a research direction.

Adversarial Attacks on Robotic Vision Language Action Models Scalable agent alignment via reward modeling: a research direction

Reference 89

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no resolver link, observed 2026-08-07T11:11:40.565294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:11:40.565294Z digest=sha256:dc219959bcb7440725101d56f27eecb26bbe555f05a241dc7ff5c3d44986d450

Observation b5a7b127-a24d-4762-b079-14c90623e9ff · outbound

This paper cites HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal.

Adversarial Attacks on Robotic Vision Language Action Models HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal

Reference 90

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no resolver link, observed 2026-08-07T11:11:40.568661Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:11:40.568661Z digest=sha256:ac80e2cfc52eeb16e647b89c165ecb95b91c83561980b909287fbbbe8be2164f

Observation 4bface85-924a-42ee-a04b-36476e85f515 · outbound

This paper cites GRAPE: Generalizing Robot Policy via Preference Alignment.

Adversarial Attacks on Robotic Vision Language Action Models GRAPE: Generalizing Robot Policy via Preference Alignment

Reference 91

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no resolver link, observed 2026-08-07T11:11:40.572064Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:11:40.572064Z digest=sha256:b40f317aef737b5c2469026b03c014c32fa307d8c7968089f6cba0b4b3db3fb5

Observation 44e5a0f2-5daa-40d8-9f27-de07fa26d6d0 · outbound

This paper cites Abbas, Shakra Mehak, Georgios C.

Adversarial Attacks on Robotic Vision Language Action Models Abbas, Shakra Mehak, Georgios C

Reference 92

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raw_fallback, observed 2026-08-07T11:11:41.806984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T11:11:40.575417Z digest=sha256:d9770f5479435f4bfa8c0bfe12b3017412d5410b9d5b8ed2e9ddd3c0d8a5a55f

Observation c8df0c7d-307e-485e-ae5e-048422841b12 · outbound

This paper cites SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning.

Adversarial Attacks on Robotic Vision Language Action Models SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning

Reference 93

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no resolver link, observed 2026-08-07T11:11:40.578496Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:11:40.578496Z digest=sha256:71b23afc0452da26cb8ebace407e76530253e67d283d84275e5651074dfef15b

Observation 67b24b70-5179-4f26-b95c-7f1ef6f9c7f9 · outbound

This paper cites Safety guardrails for llm-enabled robots.arXiv preprint arXiv:2503.07885, 2025.

Adversarial Attacks on Robotic Vision Language Action Models Safety guardrails for llm-enabled robots.arXiv preprint arXiv:2503.07885, 2025

Reference 94

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source=pdf_text observed=2026-08-07T11:11:40.582432Z digest=sha256:8f9863ebeae0ca0eda9ed9ae2b39d4487f79c4b4b5c58132a2cac11852f48275

Observation bd3044df-29b1-4895-ab51-4291ae069659 · outbound

This paper cites pick coke can.

Adversarial Attacks on Robotic Vision Language Action Models pick coke can

Reference 95

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verified fuzzy
raw_fallback, observed 2026-08-07T11:11:41.661182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T11:11:40.585654Z digest=sha256:5cb92dc6acc3268e9610c3ac8fe8a7e04121e9dcffb6e85a514dec01203c0882

Observation 8a317e3e-f960-47ea-89e2-8c58204d714b · outbound

This paper cites an unresolved cited work.

Adversarial Attacks on Robotic Vision Language Action Models Unresolved cited work

Reference 2023

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parse uncertain
no resolver link, observed 2026-08-07T11:11:40.291167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:11:40.291167Z digest=sha256:39109a36eb274f76bbad59bc602083248970df90f96bbe8df567e36e02b3cc38

Observation 0ac6cac2-4c25-4831-94ea-903e4988266f · outbound

This paper cites An Embodied Generalist Agent in 3D World.

Adversarial Attacks on Robotic Vision Language Action Models An Embodied Generalist Agent in 3D World

Reference 2024

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no resolver link, observed 2026-08-07T11:11:40.428658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:11:40.428658Z digest=sha256:c4dd97db62d21fc05e297ec29cee4cdc9c61369b0bd87c7f7d022303920bcf5d

Pith citing papers

Observation 56c4544d-d9f6-4201-b6e6-570b1e1f3fab · inbound

Embodied AI: Emerging Risks and Opportunities for Policy Action cites this paper.

Embodied AI: Emerging Risks and Opportunities for Policy Action Adversarial Attacks on Robotic Vision Language Action Models

Reference 64

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no resolver link, observed 2026-08-05T14:37:25.598981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:37:25.598981Z digest=sha256:52bbd144df7d27161ca21ad2f53146321f16be42f0ac5f082af2f63547f2068e

Observation d785a531-3760-4bc3-ab92-fe42c7835f4f · inbound

High-Entropy Tokens as Multimodal Failure Points in Vision-Language Models cites this paper.

High-Entropy Tokens as Multimodal Failure Points in Vision-Language Models Adversarial Attacks on Robotic Vision Language Action Models

Reference 15

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verified exact
arxiv_id, observed 2026-05-16T19:31:13.125269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-16T19:29:43.382392Z digest=sha256:993e435653de5c704d44fbde21db755de67a09b51e28cdef8f25b1a00c994418

Observation f820d86b-5131-48c4-8b76-47580c354891 · inbound

High-Entropy Tokens as Multimodal Failure Points in Vision-Language Models cites this paper.

High-Entropy Tokens as Multimodal Failure Points in Vision-Language Models Adversarial Attacks on Robotic Vision Language Action Models

Reference 15

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no resolver link, observed 2026-08-03T14:05:23.448332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T14:05:23.448332Z digest=sha256:be1bdf4ec2b6a11d8df3811ac2edce196c346d8407affe89032a8fe28d4f5d3e

Observation 0aec7b11-f324-47b0-a56b-7a2f3b0d881c · inbound

TRAP: Hijacking VLA CoT-Reasoning via Adversarial Patches cites this paper.

TRAP: Hijacking VLA CoT-Reasoning via Adversarial Patches Adversarial Attacks on Robotic Vision Language Action Models

Reference 4

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no resolver link, observed 2026-07-13T19:50:50.950451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T19:50:50.950451Z digest=sha256:bfd4312a76fbb295bfea1d9ec970c7b4d9c6d6fbc5709f4a665adab259480c3b

Observation e7b3b776-b54d-43bf-a6dc-b989de00f8a8 · inbound

SABER: A Stealthy Agentic Black-Box Attack Framework for Vision-Language-Action Models cites this paper.

SABER: A Stealthy Agentic Black-Box Attack Framework for Vision-Language-Action Models Adversarial Attacks on Robotic Vision Language Action Models

Reference 7

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metadata mismatch
arxiv_id, observed 2026-05-15T01:08:25.791834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-15T01:06:42.421062Z digest=sha256:114759980186d8b4f8aa26a2e4b6687a8fa23aaf4be8d1a988e9bc3f91ca832f

Observation 11d4d8ed-79e8-4493-a573-143f81210c60 · inbound

From Prompt to Physical Action: Structured Backdoor Attacks on LLM-Mediated Robotic Control Systems cites this paper.

From Prompt to Physical Action: Structured Backdoor Attacks on LLM-Mediated Robotic Control Systems Adversarial Attacks on Robotic Vision Language Action Models

Reference 8

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metadata mismatch
arxiv_id, observed 2026-05-13T16:48:02.805576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-13T16:47:10.678447Z digest=sha256:4f7c10260a436902b2ce7bf287568b78de40e7ca468dafd2652e50f163d6e952

Observation 6137c7a4-a2d8-44b0-8bb8-6782d72cfe7a · inbound

FlowHijack: A Dynamics-Aware Backdoor Attack on Flow-Matching Vision-Language-Action Models cites this paper.

FlowHijack: A Dynamics-Aware Backdoor Attack on Flow-Matching Vision-Language-Action Models Adversarial Attacks on Robotic Vision Language Action Models

Reference 11

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metadata mismatch
arxiv_id, observed 2026-05-14T22:23:03.846995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-14T22:20:35.818770Z digest=sha256:2758cb3743db676e145b432f8a798cb97b5a8845c422e5a16132823b4ab55f32

Observation 271e1474-c83c-4f4b-a890-278af2b8fe62 · inbound

Vision-Language-Action Safety: Threats, Challenges, Evaluations, and Mechanisms cites this paper.

Vision-Language-Action Safety: Threats, Challenges, Evaluations, and Mechanisms Adversarial Attacks on Robotic Vision Language Action Models

Reference 32

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metadata mismatch
arxiv_id, observed 2026-05-11T21:21:10.586498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-08T05:56:13.913710Z digest=sha256:7b89d453a05109b67f73b907b4dc3ddb73966184900a70ab12c3b3c56a21188c

Observation b1d9eb21-727b-4ae6-bdd8-48e5742cbda8 · inbound

Semantic Denial of Service in LLM-controlled robots cites this paper.

Semantic Denial of Service in LLM-controlled robots Adversarial Attacks on Robotic Vision Language Action Models

Reference 23

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metadata mismatch
arxiv_id, observed 2026-05-11T20:46:14.626428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-08T07:59:42.478294Z digest=sha256:c55f407e1115bf60d9f87401ea37b6efd3e93ddf1c8d195dd0b41f53aa931d87

Observation 9f5e4732-6c6d-4dc5-84e8-371556000c9e · inbound

Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses cites this paper.

Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Adversarial Attacks on Robotic Vision Language Action Models

Reference 198

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metadata mismatch
arxiv_id, observed 2026-05-14T22:23:04.330888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-14T22:20:31.623849Z digest=sha256:32b79663b736331c61dc5e26eca3358f376dd950a34d4908b9ebde97196b5aa4

Observation b4b5feba-993b-45df-b020-5bb525bb5b25 · inbound

Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses cites this paper.

Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Adversarial Attacks on Robotic Vision Language Action Models

Reference 164

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no resolver link, observed 2026-07-13T17:08:58.831798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T17:08:58.831798Z digest=sha256:2b24d84ff7afe19110666c139934fbb3bf4fbe9847fc4c83450629215c4fe28c

Observation 4270abea-ea1e-4112-97b3-b2edf3763d5c · inbound

ReasonBreak: Probing Vulnerabilities in Reasoning-Enabled Vision-Language-Action Models for Autonomous Driving cites this paper.

ReasonBreak: Probing Vulnerabilities in Reasoning-Enabled Vision-Language-Action Models for Autonomous Driving Adversarial Attacks on Robotic Vision Language Action Models

Reference 21

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metadata mismatch
arxiv_id, observed 2026-06-29T11:13:20.764580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T11:10:46.269185Z digest=sha256:70af68a762d19eae34a30bba4fd146656b1efbbc458e5d2951698a4074e370e6

Observation a8f39fb6-c78a-4773-b990-ce8864d880b8 · inbound

Adversarial Attacks on Learned Policies for Surgical Robotic Tasks cites this paper.

Adversarial Attacks on Learned Policies for Surgical Robotic Tasks Adversarial Attacks on Robotic Vision Language Action Models

Reference 42

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verified exact
arxiv_id, observed 2026-07-03T10:17:57.712107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-27T10:07:43.624430Z digest=sha256:0d5b76e78c39fc26c52aa2dd9d89ef1a6b6d11323c0a74fca43992fe0ed3740c

Observation 14643cb9-ab5b-41b3-929e-b61651e4006e · inbound

Trajectory-Level Redirection Attacks on Vision-Language-Action Models cites this paper.

Trajectory-Level Redirection Attacks on Vision-Language-Action Models Adversarial Attacks on Robotic Vision Language Action Models

Reference 5

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verified exact
arxiv_id, observed 2026-07-03T15:18:33.815886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-27T06:33:53.013076Z digest=sha256:87b35ba10b740661a7cfd1b084e936b40928d65175bd8301d09d35f441d957f1

Observation 4cf02cd1-f5a2-4b89-9fcb-fd4a0c1283e4 · inbound

Security of World-Model-Based Embodied AI: A Lifecycle of Threats, Defenses, and Evaluation cites this paper.

Security of World-Model-Based Embodied AI: A Lifecycle of Threats, Defenses, and Evaluation Adversarial Attacks on Robotic Vision Language Action Models

Reference 110

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no resolver link, observed 2026-07-31T14:03:37.094818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T14:03:37.094818Z digest=sha256:9a6d1dadaffc494fa7ecb47d4224fdf7e16c94b6feb4188e6a327290f9b5842c

Observation c623575b-7270-4968-8cbe-1d7c034a4973 · inbound

VLAGuard: A Framework for Evaluating and Mitigating Physical Attention Hijacking in Vision-Language-Action Robots within Wireless Sensor Networks cites this paper.

VLAGuard: A Framework for Evaluating and Mitigating Physical Attention Hijacking in Vision-Language-Action Robots within Wireless Sensor Networks Adversarial Attacks on Robotic Vision Language Action Models

Reference 14

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unresolved
no resolver link, observed 2026-08-06T00:41:28.189227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:41:28.189227Z digest=sha256:799facb1925757d9df6ea19e5f1fb16276fde3ff7f1573401f44892abe719ff9

Observation d8894222-796d-4019-834d-cfedfc1712a5 · inbound

DRIFT: Derailing Denoising Trajectories of Flow-Matching VLAs with Adversarial Patch Attack cites this paper.

DRIFT: Derailing Denoising Trajectories of Flow-Matching VLAs with Adversarial Patch Attack Adversarial Attacks on Robotic Vision Language Action Models

Reference 18

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unresolved
no resolver link, observed 2026-08-05T23:33:28.586419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T23:33:28.586419Z digest=sha256:8f7c739d874e78d1b0ac1ca956994846a2e02e8655c7f6e5a7bf32840f3c24be

Observation 3eb7074b-c81e-4cc4-b498-e7acc5d3c737 · inbound

Hijacking Robots with a Piece of Paper: A Systematic Study of Physical Prompt Injection in VLM-Controlled Robots cites this paper.

Hijacking Robots with a Piece of Paper: A Systematic Study of Physical Prompt Injection in VLM-Controlled Robots Adversarial Attacks on Robotic Vision Language Action Models

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-08T00:38:15.328733Z

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source=arxiv_source observed=2026-08-08T00:38:15.328733Z digest=sha256:3de9c33be37686d7b27ba1397ab97898269167827d10752ef13744aad377183f

Observation 12adfa21-748f-4556-a278-0a95271a1b16 · inbound

Hidden in Plain Sight: Diffusion-Based Unrestricted Robotic Attacks on Vision-Language-Action Models cites this paper.

Hidden in Plain Sight: Diffusion-Based Unrestricted Robotic Attacks on Vision-Language-Action Models Adversarial Attacks on Robotic Vision Language Action Models

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-12T00:43:51.177082Z

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source=arxiv_source observed=2026-08-12T00:43:51.177082Z digest=sha256:0dca14555f986049df81452c0f0444f2989c34e209b40c37db5f7fa9b09de841

Observation 95db10d5-0e8e-4743-a01a-ff6d6ecdaee9 · inbound

UniTexture: Cross-Task Universal Adversarial Textures for Vision-Language-Action Models cites this paper.

UniTexture: Cross-Task Universal Adversarial Textures for Vision-Language-Action Models Adversarial Attacks on Robotic Vision Language Action Models

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-14T10:45:44.770792Z

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source=arxiv_source observed=2026-08-14T10:45:44.770792Z digest=sha256:3040f45950fa20c0c8ce179e5ed7690907df685f52955950491771ebb0a70628