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Paper Citation Record · LEDGER

Adversarial Attacks on Robotic Vision Language Action Models

As of 9 August 2026, this Paper Citation Record lists 95 of 95 outbound references and 18 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 113 of 113 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 18 of 18 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T00:38:15.328733Z

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:d6b6e1e85340037f675a6e8f30c23cb59a7372a14ba7ec67e4773bf7c7c7f541

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=sha256:0aec5aebf12e55e7de1a22fdaeb520ce9050cec39c510b2be5c30bfa9e7b073f

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=sha256:8198469e1bb8b73a2397c61b1944a5a1d6d5f1a11bf6dba507062f8734077ec5

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:a71d061ac535f2c5c2d6312f77e2c52de2740dd79b4074e03f40ecd4c8d3547a

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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source=pdf_text observed=2026-08-07T11:11:40.283521Z digest=sha256:6cc3b40329bf88924c6aaf6d02cb2427b59e118641606e5d09482de0b40120c6

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=sha256:9c88407645e15f554aad441ebde1fef3e86acb89cdaf4cefec1ada6d62c1c4f6

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:ec0820ec8bff20cca1429d62cb9b5aad768672aab47b41615349bad1cfd9c9a4

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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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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source=pdf_text observed=2026-08-07T11:11:40.304922Z digest=sha256:fd4ac3e4cd5b0e688be9908c885aab0de7d2d8f3d29b0764612111c3e249b18c

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=sha256:6524940663f62420df55e3710ad9c80712909c8d84160441f127df543b5a3e3b

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=sha256:1fe277e0d8e796f1e6a447dbc86dce67aefd2c38c57841ece97774de142f2863

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=sha256:026293440cc72f35d55489740d94d55245b0c2f448e8b11af739f338a86bae3b

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=sha256:83d091e309ab4ec776f0bf012d21b46308d70b5781c4ece2f334fc08785abe1c

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=sha256:153fdff00f265dd6df9763fc8c5be402b4fc39e476dc35df7df14d6f6ffa6692

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=sha256:2f344244ac2f95f36ba974fe86c687bf699899a3576d699ccf9249de4cfb73d1

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:f931af31f6d819c74e11d975860889be9c6aaae4b673e2ae985b62cc4d47d22c

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=sha256:97a26e34c5bf5e5137fac7a33d01a2ae0deb75e90aec165602d074f5cf147844

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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source=pdf_text observed=2026-08-07T11:11:40.333890Z digest=sha256:8bf84045505aa10a9cecb0756d589afa89dfe0c3a725997e27630ffe8908d3aa

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=sha256:000676727db23d16f961013826cc1576ef79f23eb19af4c072bf15ddfd3ed8bf

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:cf181d7685be81629ec6140aad60cdd3f607be769e7d7fb5a004d13ae352af34

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=sha256:85c494e4ac59bc57eea7f2dff4eca8d83ca2000bd9aa0d12b90d5f5a6cd2a344

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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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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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:ceeaabd35c46f11072c3c18e3a896aa9e17e6ce731dde3881b090586dc6223e3

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:e2b171b79a9d4e821ffc4b25f5a56e107cadd3a0f8de83a00d2abda0da8d78c9

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=sha256:90df557b6dc2c364a0282ed56d87918f4e54c58a65faf635ed1c705b15a66223

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:d6bf4550bfc82061870bfbaeb4011d98e2e572fc19c43046a158bcd6f9eb09f8

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=sha256:4b565d62c883b412f69cc27165f8f0b0d6210fab39534a3a1aa56e23f9dea168

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=sha256:2a1e5043a085b3d7b060ede42e87da7ea3740413960a030d350e7c43602a9899

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:df5d9df98ac0b8039bf2a1cbd80ce97f6bf4fb56f7ef46c6889af40b1b11846d

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=sha256:733888fa1116148848ce10f25f6194f8e80ef7920bf05a03bdc9146f9a2a6b4a

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=sha256:386e2300b5d03eaae5a5e178fde5f7289848a7ffd8765488beae4af49aa75c1b

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=sha256:5f6446a7b6edf57345f85176e44f90eac7a4f0e5920e2dab02ccb6b8ac67d89b

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=sha256:36e59565393ad50a5198a4644938508d4e79901165ec721944964301c05d2847

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=sha256:2687c69ffa8b6c6610c62e305911eedf193c0610ce3466fd6741511321ae8f69

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=sha256:96dbd060ac10e32fb2a680f1fb94073b4e6dbf1a3a48f8b8d0a588fca98555e7

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:11:40.403344Z digest=sha256:578ca7e1968b3c24d7121420dc6b0c6f820ebbec0fe207ba8218bdf50e095bbe

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:ff88b2da8a8567964badb059421c4721f556bd07d8ac8652bb26287667b116ab

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:a5bf3a9425c824388d03802acaaec220230c29c67b1c299b47d89d56a7be419f

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=sha256:7b0d88bcafce1ebda674fdc900921518d5a3728906cfcfe2174e6d5c0f52d73f

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:bb9ab736fe3d8dff9201467640bdfa20dbf37713dc10570fdc2b8cd877ba3dad

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:c600bf84bb114e1239c593f5b4257543334efc1314b998a17617b62b3be133df

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=sha256:33da8d989192b8b3dd85f02cbc2ddc9de146401b58e9fcc75424c999918f35bf

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=sha256:23e6bb7c8d71589673faf252258bc3fb1fd78ab9ad70f61ea37695c05cf79480

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:deba29216d40aaf972f76f949e03a9f596a504c4bc01ae4d203eef4968bd21fd

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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verified fuzzy
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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:11:40.435203Z digest=sha256:87a6a3d4aa29eeb999e0dc42149584f0724cc16ed391a67fe52ed9f6f0b044e4

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:e581b575d26b28639778da56579e1f14e86748299bbd92c20672e9ca53193c28

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=sha256:07cb77cb64b693957a5fdabc03be2740cb9e962191c2e5799b01d4c23a4c5afa

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:e9d11f3efa6afc3f930fe9d4789e2bd227c517bb6da4c2304ffa54bb338e9a17

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:fdbfa759eb3de9cc696e794ad2d94f61c3d6eabb71fe1bfafe359ec8ab38d1aa

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=sha256:9c19aef262ed492bef1b5dd71402cbb3165be2bc1eef434e2894a2cc9aba83e1

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:11:40.455122Z digest=sha256:090ef2da40212b0e512f61570236f960dbbcdb795fb3bfe79e5e6605f7511544

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:11:40.458043Z digest=sha256:a0f8acd2da89d3737955ee780db55fc2aadf84882e221235ef6066e2c9613e8c

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:11:40.461177Z digest=sha256:dec2fedcc7633f88d3d6c23d062be58ece4f5ad1512dc62fc0164d400a4e5435

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:11:40.464457Z digest=sha256:76adda9f7e940089e5078d3baed1d98652c572e5244dbda25156815a2cc5bb59

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:a910972f6368d29994813e6ec713da0a29ce52f65e57f06136381e402d1f713e

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=sha256:4b00f969172e6dc16e11ac640e9a79211cb0ebf2810501b56c9f75750ed98250

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=sha256:2e7f421688eaadda95c449e0843278669a2dacaf6587498e1047454705d23f97

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:e561afe284ecb89fe1d4a12e3576ec550a5704c17967a57b502045ff740fce8f

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:de4e1afa3cf6953d063743bb8916b9852a70a77c1d90ab33eaf40399f251dcec

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=sha256:608c54669e084581ca827f75a4b962f8e2133167b838a688f8251199f1d45f17

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:11:40.486909Z digest=sha256:69398613aa1755eb7d9fd0e26a24d2ebfd2f855d17dd6e81eb829af4ec018f4a

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=sha256:7d663a880552fb190678d86dd3bd70cb7e7b6dece3d5712c9f1c8d14da15a033

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=sha256:6d1b180bcaa9063245f879a93dd200fac7d74d0ab64c494783d0c4472d38e168

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:11:40.497097Z digest=sha256:0bedbd933028398d8b5a1e3c55f20e3a36ae60460ada57a634cca1987cc276bf

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=sha256:6cfc6890dea4a74d5bdb80b2784510b45f23e992ce29ef7789f2c54232e81c0f

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=sha256:3b2a7dd8bf1de301901332de71b7af6a3ba9759ded6a952de91a59f7f63881b4

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=sha256:59e2bd879851ae1850b8bc2cb591e3e254787b94ccccb9c3d287d1e64080358c

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:ec16a57b249df444c1a746bb11e628e45aeb3586a6eea51a99624a72996feb46

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=sha256:58c39f9bf8cac8739d0134a2395ac725124d57ac5e92b0ef27caa6631198270e

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=sha256:77b3bce7b59d1143ecf0ec7df1d797dd83b6c3759f28479fb4125624135536b3

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:d259755c979f7ef86b3863a591c2d33aad0122eb979657315754b0f091140d2b

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:efee2496e033cf28e7a05b2d6e8c1c41e86bf6fbb2a809ad4b8e9daa5ff955a3

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=sha256:02ba136f71dba2687d0ebdc86190380e58cc3861e94a0d001a5933f9f5fffad1

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=sha256:79e1f25488e4bec3c525ed427c330a2d6730bfa8ac69a2206c8c45920efb115a

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:d105c8d97062efb9de703c1cc946802b09c712cb9d2e50e972e0cfd23803b03d

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-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:11:40.538116Z digest=sha256:60a9022d38bd14f229f7d652cc0716859b108c08c13467cd596bf0613d8d1633

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:11:40.541709Z digest=sha256:b70d196ef71308151ab0139e4dc3cd2750f2d097e28a372ca443dc8d318f61ea

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:11:40.544923Z digest=sha256:71aff47f56b2304b24548914e2a4403191ae1773f317227b13a86c954d1f99d2

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:11:40.547821Z digest=sha256:e22993f3ea0cf9b09401df0a55ab396e2778d047cadcefecc0677a6915eda3b1

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=sha256:1e0cf599b85ec727c46d126521509914414c62730541aec6c4afb5bb504ec85b

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:e76c1313408354e35b99e4a363176d114189c18201ca5ba89d73cca96beadfb9

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:11:40.557735Z digest=sha256:08e65a69249d3b01383c0d98fc71bbebef1e5ada0071de1d9bb02abcbed12426

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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no resolver link, observed 2026-08-07T11:11:40.561507Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:11:40.561507Z digest=sha256:bc2b03b7730e0a87f7fe06f1378f3c57dc7f61258cddde4abae9d0bbd3728b41

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=sha256:9375af45c4c080578babca05b2239b9afd51d7ba0070ad07a20554b174c2e200

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=sha256:6f0805b11f4e97795cbd0efd506332335647d0289301281910d3022994c8a334

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=sha256:7f3374a9eb919154ae707437374906c7307ddac101a1918355d7adb90be4b124

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:11:40.575417Z digest=sha256:55bd0cd9388faa27c38ada8e6dc4f63037426d36e76011bca57f9ed9ec177f2a

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:11:40.578496Z digest=sha256:5287640feda5ef5ea577f3c78fe00bd3ab3758c5cdfafef19162529139de9311

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:d8335da009c7a30029fce0c1f077b72efe6e2ff5d8b68fc8e11e51ef74096d3c

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:11:40.585654Z digest=sha256:aafd9e094ea7bee548028d078888cde42cc751ab8dc7d40591bbed71b60295b5

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=sha256:53bfd00e2815a9f24e51dafe5c5640ca53a768d8bfe72eb6aca4d1231143d4e2

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=sha256:3e356c5b1019e3defbc1f821891a9befe52525e80b3f29618c34a09ae97e400e

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=sha256:26d2a6116856b5a6cd881b6ad0efb7fc58ed8c1189dbfe0ce947c59d78a47ef2

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-16T19:29:43.382392Z digest=sha256:42a6147c9e0b45735a7e2cd4fd01a6e66322740d4e432a9122cbb1abbfae89a9

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:e9a8d1ef844a663922520f0043fb431e065ae93f75d7ea47d8b40c2d32d099fe

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=sha256:9b819b594b97b74af2f8c40f075e03ee7bd5dcac87814f32cc1ec51cfa8cc681

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

Resolution
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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T01:06:42.421062Z digest=sha256:3e7c6e299f3ae33cc296d6fc5d291f1f9b5926c9efddeb0015c04d40788681ad

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-13T16:47:10.678447Z digest=sha256:6282bee996a5560017470790598f501006dfe8c9ac2893d852b44555235cd12f

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-14T22:20:35.818770Z digest=sha256:9372645341380140a49a7b1a8837831dbb84e769eb0ee20aa3545514b49f3fd6

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T05:56:13.913710Z digest=sha256:75346afca4859fd38998e545f19ed75117320b85168ceda168428992678583a0

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

Resolution
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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T07:59:42.478294Z digest=sha256:9a670d359103a38b4c1991e0ab52b5c0d5cf07985e4c45e9edc03eb09010a095

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-14T22:20:31.623849Z digest=sha256:6d4d999ad0ae78ec21f3337d09cdc2645ad62842e0b61bfe3f940d553514306f

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=sha256:1f583b35f0b833727515bc56e32b59448e252fd15ffbbee53ead1d5f6c1c9192

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

Resolution
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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T11:10:46.269185Z digest=sha256:c003f9ab99bde6f7c4472e42d38ccc87751534a8b945504ab168a47e8f7ae04b

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-27T10:07:43.624430Z digest=sha256:c9c51297a19da372eb99d3a797839d1dadba746b18a0fd6ee6a701d494b24b00

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-27T06:33:53.013076Z digest=sha256:f86807be6d80583a37fda7b58c33c65f99d887e4f7876cc799f61ce5498bcc4e

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:cc1f11cd3ec52a29b1cca74012ff036b5ed5694e7041c18d537f2c513ddb3aab

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=sha256:589356c4a2a1372d5b906083cb968e528bac6fc67c5cfc4baae326bc82151af4

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:ddb993c27254f05f703c6fccd5cff3aba78ee0f122896a38cc7b9ce38dc78678

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=sha256:0ccebf70ef6bc18ded2bbe19a4f501fa8c7ab52923e0ec6c7fb66790800225a6