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Paper Citation Record · LEDGER

Adversarial Attacks on Robotic Vision Language Action Models

As of 7 August 2026, this Paper Citation Record lists 95 of 95 outbound references and 17 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 112 of 112 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:41:28.189227Z

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=sha256:771f5d604974cc0f63b2380d3d247996f9abc4bc01b959b1b90f3610bbc8045d

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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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:a8936f5e8d54461e5f7e539568ba19db4ff42ca71539bd3e06a48d5a70e2ff2a

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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source=pdf_text observed=2026-08-07T11:11:40.274805Z digest=sha256:e25e3831da37bd65e83a767358c3756ebf54591585f330f525456c076ebd1c7b

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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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=sha256:2b9ea73bb66fb832cc41a1b0d413f85978b476894eeb2af9995c558bea4a3e0f

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=sha256:63942539593aad65b28a5363c46f8fa135057922317e35ad29d0b1b4b671cc35

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:e26b52cd26facd676cb1c093f11c2adc7439d803dc1d6b6ad355ed212818bd84

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=sha256:6a7808ce29c0074ecebc9b4ffe9cb451ac799d3e353c6c5b16ef329d139a629d

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=sha256:5281a3d0dff3f64bfb928688f8a2c6d79b5e571e0140e6bb80a7789d8b1abeed

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=sha256:616c7fc3e9836ed59e70b40c2acf8088cf9d631775571daea23334f0c4722b1f

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:a2e78f8230e058dcebb037c05e4b9bf6381dac52919fdad521ecef4f05c83333

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:be2c4136443bb4d48f556de26f53ec7dcfc99485da333a0176154eba39939dac

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=sha256:5a18e4a630557ff845c5c7389cd2bda2f8710c02e8f9206c843d555cddd2aacc

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:a6cde429d4be30980fcaf5964a1182af4e982ef84499110761c4591964b43359

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=sha256:23d02d56145dfd70a6178efbb78fd83430eb4453b00bd58b2f32d82e7db1ba4b

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=sha256:10b0d2ba3b3d148eda99df891ec31cc216b4b5dfe9f59371e562de4885a5fbfa

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=sha256:51f3894a081fb1a1a68e89c14d06cc34c800752c79b692b38e7d2bcdf5c4b3e4

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=sha256:75dacd4398266c26406a1472f7522af25af2cbd5014f07aec5a8e13eaef9596b

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=sha256:88e8220f64197ce6b14448430d75a11449c38b3ceae1e10710755d7fe0f5e57e

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:f3ae8505c483d09187cafcc72a90677de5d79a9c0495a2d82da4ad3acaead9ad

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=sha256:1b82519923883cee4cd53ad9b115ba849a92d4c668e69570ce4ef06d87b2a633

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=sha256:22f8fb010787f57537218dec4ea12159e055f20515aea07314fdefc29513b279

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=sha256:45f0e17dcb61e7f31be4aa97561c4272c375b69a644a66de61aaef2d32192b6d

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=sha256:383d0a446cf99a9f12c7a573742d684ee2f040ba6ae864136d27387e50285f70

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:a014deceb74b17cb9a802612f5de774364d5de68dc038984d7640b44d778aea0

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=sha256:81608bf02b31bb4a20866d7ac0e45aa960e12557dc915fcabed9c182a61165eb

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:ecf7b593199851e67502eb2e590ef27a92f2dcf49ed5f73bf3124d8eabbd7505

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=sha256:91e1ac253ca69ca9a83922844c152086fbd6d4127bb07484b339be35e2a89369

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:f554bbd5498894f23dca9e98f442e961d53188050c8574e4e1c45bb599f2d40f

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=sha256:9aeef6b271ec58fc47dd213bfcd17d64636a12eb7c788413f8a2fe20156b29c8

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=sha256:88ebe5f1de1340bfb2de39dd42719e472b047e810f1eb66ce245f70db633ee15

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=sha256:1e4868944c10c14ba668defa06c8800c8dbcfa4752e49aa889a37d3802939c14

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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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:11:40.403344Z digest=sha256:68c20e96ab872b00b97296eb1d20f1a8fed55e1ce39d0e1c23b2c8ec7bb8d992

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=sha256:6be6cc4ae712f6df18fbe46e8fba77eebf40a5392490a13fe121ae3ef025b84e

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=sha256:1d8e65f549a02260b78cc2d1531eca3bcf1c615990d5994733d41bb02217157c

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=sha256:25e4471dfc60c36318bbdd489c19c39a9e10d91e32eb72153f548ab12b93cbf5

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=sha256:044253f7ec3fe47248181c45fce31cf4368842907b23fb7897494b42c1764add

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=sha256:91380a7e4132f4d4b6e0399537f01db60e99e097b6c27c6b9209b83b8ec13a25

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=sha256:2af6a171ce05a99f83821524e411824a0ab7eb95cda2f36710caa38a393eea3a

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:ca0ab272ada247fdcb1240bffa6bd0ad36031d5e52053f17249394e6f0d7c0f7

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=sha256:26e167496b2b3526e6e832aca3ffd33a2260cc0c57d2c9ddb6241134a033651d

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:11:40.435203Z digest=sha256:7c31e3c933d992776f1315dfec6f794a44ef281f331cb08ab2b402ecc6cb5848

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=sha256:496b7a375cb7d85e78079a925b93bd253a89fd781beaaf530dff41d13347bac7

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:dd4d998ab8a5a32d2feae4beffc77ce8df6f1a6bd6ce04dfbb0b554ee5d6b4ae

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=sha256:22cb99b414d29cd3d2634d05a41937dab82110c43a88af99746fd6e61a7757db

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:ab4074dfb4c0004c28ce69e8edc1a8f09c6fb4889fad02a9b92166d96993e8b8

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=sha256:62f11aec267208b7b4b10c1984b85e329b3b858327de713b7898571a4ce0bb57

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:11:40.455122Z digest=sha256:7b16c6fa516c526df72766406098d70c213bcb622bd64fa6c483995d155c2b9f

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:11:40.458043Z digest=sha256:12c5e71c1813df3ffc17d7da3b6980054a6b803e7d80a5f8dfea5672ef7e949a

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:11:40.461177Z digest=sha256:38d4cfd5de3580a2f727c75a94310dbcb9a2c32513aa6afe6dd8198eb26b3228

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:11:40.464457Z digest=sha256:0e1c9f8e0ffcdc8da900914e44aeb57126d26e28ea1ec92a7c5e59c318cdeda9

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=sha256:0ff00ff5e6acaef9b4f7c73d50816d1f3df61d9688ea24acb2145802c17561f4

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=sha256:07439f7d421b61b7d7d6e6bd1c6d5836bfc677ed9f50254c70e9bddaa1ef36f1

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=sha256:07804525a473e32135880ff3479302d655eb83b9c1288462d3b1d7820c90e8b9

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=sha256:6ea466704654269eab3268fd296c71a01708c186f18f9e28208250ab0f91378c

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:a663245b7210b39047c29fe1d2bee984e8ff2ace08a998c008138d23fee85381

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=sha256:0d847d5a3057c5bb8a4dc9ea6961b583e4ec834dd921a770fde214b8e6baf09a

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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verified fuzzy
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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:11:40.486909Z digest=sha256:ec2613628f224b2a5da13b78039ec678f8612f0b2a2740b4c08717832fb545ca

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=sha256:894fcbe1abe26ba67e438e1569acbb5b48475ae2493242e5f6a97dc37de511c0

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=sha256:38e46c43b4987e53248100750ff12c8eb7a6021a0b71be4082cc1f93eff59a6e

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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verified fuzzy
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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:11:40.497097Z digest=sha256:5aff1b1ba7aa2f2da130298c1579f922bd6b09e5b7429e5539d79a97fb80ffbf

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:d71af57ddb9b0e59f2a63b854f174db55b52ee302fda2aaf7314ee7c96e7b2c5

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=sha256:5500a50bd0cdfdb60cd95aca1c2d15eca0b0d6eef504ac807310d03070b0f848

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=sha256:192910464e1d5983ab3224794dd34d29d0aa6ce041310f1d46626ba16386c9e0

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:eacd3e238063b331fbc673e0123fcdd789aeebacd4809c7cfa710893a4db4cf4

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:fec8d90a5261ec9876df4a149ca322b23d0197e47a6935d51f2d28c462b419d8

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:a8e4d85b93fa9967b9ec3a00512406a14de5ea6929ed5210294d218e55960c65

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:ca23609e6543e1dfe8a8a85f91a96f0a462380e5ee26b68e3b8ba720f8fc24e4

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=sha256:2f6a57eea0ce06d342f1c1254f6c53505a197ee1f536e331264a4a09d197a8bd

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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source=pdf_text observed=2026-08-07T11:11:40.527358Z digest=sha256:b0c7dee177f186e881eaaba9adb101c6b340344dbc079ad303644d836a4ce92b

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=sha256:762aa0b7ec9fae716682dc994cd91a47215af3f53a34d514703272409fcf47d4

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:f4c0de589cd6aaca8b74470e30ba5a798898785e9fa1e13826e025182d74efe2

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:bc979393142cee527a45b239617953c6805dbac382ce0149d716039161481c10

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=sha256:731c06fa0572305feb7e7ae8c352b88e50ab0c6c4b0eeeb5801be3b395eb548d

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:11:40.544923Z digest=sha256:1eac2586398e8c4bd09709ede265e98c2a46e7b45b9cef91b81c33ed17e7154c

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:11:40.547821Z digest=sha256:ac7bb2b425240df430b11131e6246a1089005b5e3b23035d14749783fd827ea3

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=pdf_text observed=2026-08-07T11:11:40.551096Z digest=sha256:a92d6f05f612ffbb0898cfff508c7dbd8ba98d70a82f2586b0168ca559d9f464

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-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:11:40.554404Z digest=sha256:1e85683110efa35bad6872c043cab45bd80e4ea863bb6c0df07952ca46ae8cab

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=sha256:8b6899670ea53ffc8379d711e728b5c3a3a7ef4c40fba2cdb558f50c8c167cb9

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=sha256:005f96e68614d08d7fb47666b927ff2037295d23287f40b54a661983600eb450

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:b84c5171641c86b4c49e06f044e209df21e56fe931516fa8da48b429a994ce99

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:a0be953425502cea1b35b4ac5498d3896799f859653ce99e4002790af36f3590

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:b6f448b4a692e8d30a6f4eb74a6524af6b5787ecb09190ee51df4c3562b0571b

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:11:40.575417Z digest=sha256:b179a5eb27aff20b7c924996c41c2e9245d572c838b35d71c00cc060ae73a949

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=sha256:71410a7ca1a4b31d450d623268dd2f371ee962d379c1947cdc8d1743844d5366

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:e11fdce84de01c99bd410f5af4b0f53dedda5d8974fcc965bf0dc0102124b459

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:11:40.585654Z digest=sha256:2adb0c94b52c519c1a2f60bdd17aa0c83dd5d37e4b18c1725e2088adeace340f

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

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source=pdf_text observed=2026-08-07T11:11:40.291167Z digest=sha256:13638e0ca41a4816ad193cd33115190ab636f413c228b621375fe5436daf8466

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:11:40.428658Z digest=sha256:13de2aae5d13a475be048e460157f2eab1a12d7f4512844b0bb7a7bff3789eb9

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=sha256:297d7f04f1a83b81115c89a0be1e0d1be4828280ac940450f7c4f874be1e2811

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-16T19:29:43.382392Z digest=sha256:13e66d6da94fe1ae02ca4e133c00b81558e498feb56f192c92bb758778024c24

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:f2a8733495d9c9d16ffbe45aed56c62feb4e954aaa4cb74107c02da44724231f

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=sha256:536451e50cb9ae8cce7dc1b37fe4863ab5f73143a14f9739fccb4ed19425e91b

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-15T01:06:42.421062Z digest=sha256:da7a7cf513f0d8fdf25871481a1af17c0b1beea517825f22f882206c79f39da5

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-13T16:47:10.678447Z digest=sha256:13a1da1236c6cdec32752d563a7d1c70385b6f3fa85be3e2636c1f3750551a0c

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-14T22:20:35.818770Z digest=sha256:cecbdf2db027f020aa8e3d57fad3b21181d65c32c067ac1b848a14c4ba561a8a

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-08T05:56:13.913710Z digest=sha256:47f09810e5eb5752b9f15deeec85f6b3596bf4f4ff045fdc75f1e33032abffcc

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-08T07:59:42.478294Z digest=sha256:b1595c99056cf9a87a259bd72027568c40ad9e2496c962d3a0152e078776d30e

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-14T22:20:31.623849Z digest=sha256:c6ba66f2a973432619b9eb82454446c03e790d1176109af300e5583cd73ce6ab

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:c314a336b69957cf1d6fce24aa345ad71b9c12c1a05db3e204891bceda13a767

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-29T11:10:46.269185Z digest=sha256:43e7bc67cc0feeeecd3ebd06f778e46b221352ccb468bb0c86a518113c4d24af

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-27T10:07:43.624430Z digest=sha256:55fbc33c97eb026c804ed8422d4ed045031fe3a737ba12bc06fce1184887e621

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

Resolution
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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-27T06:33:53.013076Z digest=sha256:e5d6a0dda87859bc5f8af51e56191857cdd109d6efcb0f88edb062fec2068f2b

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:efc05e1ba3c5f21a3f8939a46aeb01eff0378f1cdc3cafb4f5f3038e72d6d258

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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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:b9dbe31166eadf32325e0aa739527353ac249d50fb4e09558c183a2db0985323

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=sha256:36945b0e57520f0bd3d3f474344df080a599482b0b98c024d6c85d6aed1d60a7