Paper Citation Record · LEDGER

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving

As of 11 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 9 inbound Pith citation observations for arXiv:2501.13563.

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

pith.paper-citation-record.v1
2501.13563 v1

Coverage vector

measured 78 of 78 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:55:07.989455Z

measured 87 of 87 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T14:05:25.727614Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T00:25:48.506578Z

Reference resolution

78 of 78 outbound references displayed

verified exact0
verified fuzzy44
unresolved34
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f231ce3f-24b2-41c6-9b7f-5cb45e52dff7 · outbound

This paper cites GPT-4 Technical Report.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.698921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 672c8f38-f131-402c-9210-fa2c8cf2fb36 · outbound

This paper cites Flamingo: a visual language model for few-shot learn- ing.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Flamingo: a visual language model for few-shot learn- ing

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.708222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d9cdc214-c1ea-457a-9030-58c44c1397f9 · outbound

This paper cites Vlmo: Unified vision- language pre-training with mixture-of-modality-experts.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Vlmo: Unified vision- language pre-training with mixture-of-modality-experts

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.698989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fc9d67ba-0f24-4aad-8ef9-e69f2eb7f43a · outbound

This paper cites Towards evaluating the robustness of neural networks.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Towards evaluating the robustness of neural networks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.690100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 72b56224-1863-4190-b77d-01e59ef57a54 · outbound

This paper cites Mp3: A unified model to map, perceive, predict and plan.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Mp3: A unified model to map, perceive, predict and plan

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.681147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e195ba8a-ff4e-4b00-afcc-7d2a978ddea5 · outbound

This paper cites Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.671538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e43b68db-c105-4b64-b89c-9c00899137ec · outbound

This paper cites Adversarial attack on attackers: Post-process to mitigate black-box score- based query attacks.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Adversarial attack on attackers: Post-process to mitigate black-box score- based query attacks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.662140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5dbdfcee-536d-4685-ab37-720198574b98 · outbound

This paper cites Detection as regression: Certified object detection with median smoothing.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Detection as regression: Certified object detection with median smoothing

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.652671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bfa5066f-5d8f-4b5a-909b-0a7c1f689c68 · outbound

This paper cites InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.730406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 228be00e-ee43-4fd6-a235-ece150060882 · outbound

This paper cites Deep learning-based autonomous driving systems: A survey of attacks and defenses.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Deep learning-based autonomous driving systems: A survey of attacks and defenses

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.643518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 48d7bc60-7885-45e9-8cf0-fa3b751178f1 · outbound

This paper cites Driverless vehicle security: Challenges and future research opportunities.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Driverless vehicle security: Challenges and future research opportunities

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.633185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2ae6c8c5-542d-4ba4-b58a-4766749b4bb9 · outbound

This paper cites Boosting adversar- ial attacks with momentum.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Boosting adversar- ial attacks with momentum

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.623264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6acdcefb-3d01-4adf-85ad-153fe00407b2 · outbound

This paper cites Carla: An open urban driving simulator.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Carla: An open urban driving simulator

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.613109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1959681b-4931-4050-ab41-8a28b82fd39b · outbound

This paper cites Boosting transferability in vision-language attacks via diversification along the intersection region of adversarial trajectory.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Boosting transferability in vision-language attacks via diversification along the intersection region of adversarial trajectory

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.602727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9520b2c6-199c-4116-8691-677df51ca9bf · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Explaining and Harnessing Adversarial Examples

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.752017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c4f6275-a817-43af-a58e-4cd2b5ae05dc · outbound

This paper cites Countering Adversarial Images using Input Transformations.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Countering Adversarial Images using Input Transformations

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.755932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88ec32e5-ff0c-4e78-b507-8aa1112e1e15 · outbound

This paper cites A comprehensive evaluation frame- work for deep model robustness.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving A comprehensive evaluation frame- work for deep model robustness

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.592772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ab8150bb-9da2-4bf5-bf67-4a69489e9db3 · outbound

This paper cites A comprehensive evaluation frame- work for deep model robustness.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving A comprehensive evaluation frame- work for deep model robustness

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.582887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 08e257ee-875c-4989-8146-5b5c06960123 · outbound

This paper cites St-p3: End-to-end vision- based autonomous driving via spatial-temporal feature learning.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving St-p3: End-to-end vision- based autonomous driving via spatial-temporal feature learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.571806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 446b0a52-c9b9-4247-bca5-88b4b378a517 · outbound

This paper cites Planning-oriented autonomous driving.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Planning-oriented autonomous driving

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.770734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af98711f-fbc7-4823-9314-80c5ce1df427 · outbound

This paper cites Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.774232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23db10a4-575c-4373-86f1-b00cc29a9c5f · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.778242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 388987b8-e2a5-4269-8c9c-d68560866d9c · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.781576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cad2352-2dc0-4105-b7aa-a84f1b54f245 · outbound

This paper cites Align before fuse: Vision and language representation learning with momentum distillation.NeurIPS, 34:9694– 9705, 2021.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Align before fuse: Vision and language representation learning with momentum distillation.NeurIPS, 34:9694– 9705, 2021

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.543638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e1da9c02-fd0a-472f-b290-40fe2ac0205a · outbound

This paper cites Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.788512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 122dc8ed-6cf0-40e4-9eb9-75af5f757d57 · outbound

This paper cites A large- scale multiple-objective method for black-box attack against object detection.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving A large- scale multiple-objective method for black-box attack against object detection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.533760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 63a04fd3-7cc6-4743-996f-66b25bb48083 · outbound

This paper cites Gener- ate more imperceptible adversarial examples for object detection.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Gener- ate more imperceptible adversarial examples for object detection

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.524903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0519a552-35f6-4f1b-b6f1-90bfddf6b432 · outbound

This paper cites Efficient adversarial attacks for visual object track- ing.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Efficient adversarial attacks for visual object track- ing

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.515659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8f371331-e5bf-4d02-a72b-545efbb8cc52 · outbound

This paper cites Parallel Rectangle Flip Attack: A Query-based Black-box Attack against Object Detection.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Parallel Rectangle Flip Attack: A Query-based Black-box Attack against Object Detection

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.803181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4974dc28-a855-4be2-9a49-22a1b3a6d2ca · outbound

This paper cites BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.807056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e395fd1-084c-4c7b-9cfc-017a4819fa63 · outbound

This paper cites {X-Adv}: Physical adversarial object attacks against x-ray prohibited item detection.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving {X-Adv}: Physical adversarial object attacks against x-ray prohibited item detection

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.505870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b2169595-f52b-4dc3-be6a-36b9f27c52d3 · outbound

This paper cites Spatiotemporal attacks for embodied agents.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Spatiotemporal attacks for embodied agents

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.814916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2d2b3ae-15eb-4e79-9485-18c9dd733d9c · outbound

This paper cites Perceptual- sensitive gan for generating adversarial patches.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Perceptual- sensitive gan for generating adversarial patches

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.491582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bc815605-bbf8-408e-87b5-a865c30bd4fa · outbound

This paper cites Training robust deep neural networks via adversarial noise propagation.IEEE TIP, 2021.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Training robust deep neural networks via adversarial noise propagation.IEEE TIP, 2021

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.481455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation eb3acdcf-37a3-4e1b-a02e-8cc22ca99c8e · outbound

This paper cites Towards defending multiple lp-norm bounded adversarial pertur- bations via gated batch normalization.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Towards defending multiple lp-norm bounded adversarial pertur- bations via gated batch normalization

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.471778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9190cd26-72fe-42f0-8158-99d3f573f256 · outbound

This paper cites Exploring the relationship between architecture and adversarially robust generalization.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Exploring the relationship between architecture and adversarially robust generalization

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.829646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b0ed1d0-5b86-4b67-bba7-0952d28ca974 · outbound

This paper cites Bias-based universal adversarial patch attack for automatic check-out.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Bias-based universal adversarial patch attack for automatic check-out

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.455813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 76c6966b-3928-408a-bac8-3125e4bb7e5c · outbound

This paper cites Visual instruction tuning.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Visual instruction tuning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.837107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29a51c1b-fc86-4d7a-a8cf-0e7586455398 · outbound

This paper cites Improving Adversarial Transferability by Stable Diffusion.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Improving Adversarial Transferability by Stable Diffusion

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.840809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5971ab3-fad6-4956-b6f5-f87037d7f4f1 · outbound

This paper cites Harnessing perceptual adversarial patches for crowd counting.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Harnessing perceptual adversarial patches for crowd counting

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.844858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48e80d84-073f-4d04-abf7-0e60319ee681 · outbound

This paper cites Set-level guidance attack: Boosting adversarial transferability of vision- language pre-training models.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Set-level guidance attack: Boosting adversarial transferability of vision- language pre-training models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.436529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9999d067-60d3-4ceb-ad95-aa859421de92 · outbound

This paper cites Dolphins: Multimodal Language Model for Driving.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Dolphins: Multimodal Language Model for Driving

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.852163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0dc3f6d-704e-4819-9923-56938e6020e0 · outbound

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

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.856480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d275c25-fe59-46e7-b68b-7d8149d265b3 · outbound

This paper cites GPT-Driver: Learning to Drive with GPT.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving GPT-Driver: Learning to Drive with GPT

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.860362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d8d3da1-e395-4cd3-819c-b7bcc2514ac8 · outbound

This paper cites LingoQA: Visual Question Answering for Autonomous Driving.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving LingoQA: Visual Question Answering for Autonomous Driving

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.864225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91dff88f-ac45-423c-9e6c-e3333d464a47 · outbound

This paper cites A self-supervised approach for adversarial robustness.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving A self-supervised approach for adversarial robustness

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.427693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3990462b-aea8-4613-b884-6c3eef060d55 · outbound

This paper cites Reason2Drive: Towards Interpretable and Chain-based Reasoning for Autonomous Driving.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Reason2Drive: Towards Interpretable and Chain-based Reasoning for Autonomous Driving

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.871862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60a7bac4-2806-4e1d-9243-00d702e2c4ee · outbound

This paper cites Jetbot, 2021.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Jetbot, 2021

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.418641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fef55696-9155-4d69-bf5e-7d93e28d9427 · outbound

This paper cites Chatgpt, 2023.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Chatgpt, 2023

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.410168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 952a5195-3e98-4a77-bdfa-16cee2c240cf · outbound

This paper cites Adaptive adversarial videos on 15 roadside billboards: Dynamically modifying trajectories of autonomous vehicles.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Adaptive adversarial videos on 15 roadside billboards: Dynamically modifying trajectories of autonomous vehicles

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.400901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f0e3548f-c313-4ce4-9e91-5112c04c9492 · outbound

This paper cites Learning transferable visual models from natural lan- guage supervision.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Learning transferable visual models from natural lan- guage supervision

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.391882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2ec1842f-6203-4c39-8bcd-514633aa81fc · outbound

This paper cites Limo, 2021.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Limo, 2021

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.382114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0eb8e327-4774-4b47-b266-7b607c8c38b3 · outbound

This paper cites Dirty road can attack: Security of deep learning based automated lane center- ing under {Physical-World} attack.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Dirty road can attack: Security of deep learning based automated lane center- ing under {Physical-World} attack

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.372582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 78fa2458-9e59-424e-80f5-464abfebb9cc · outbound

This paper cites Lm- drive: Closed-loop end-to-end driving with large lan- guage models.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Lm- drive: Closed-loop end-to-end driving with large lan- guage models

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.363183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3e083219-b9c8-45a8-a73b-6e47469bc84a · outbound

This paper cites DriveLM: Driving with Graph Visual Question Answering.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.901795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74ba45f5-8ffa-4a58-9a17-fa47ce378122 · outbound

This paper cites Intriguing properties of neural networks.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Intriguing properties of neural networks

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.905766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 711ff9f1-3de0-4e76-a139-8d5acf63fe35 · outbound

This paper cites Robustart: Benchmarking ro- bustness on architecture design and training techniques.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Robustart: Benchmarking ro- bustness on architecture design and training techniques

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.354013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4e8ae261-6345-4618-a3ab-5e6a5e66eaba · outbound

This paper cites Carla autonomous driving leaderboard.,.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Carla autonomous driving leaderboard.,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.344552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f7db3976-61a2-437e-8020-387d1513065a · outbound

This paper cites DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.922093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0623bbef-69ce-460d-8afa-7c9744cd505e · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving LLaMA: Open and Efficient Foundation Language Models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.926296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17ed0d44-4289-4c60-b55c-ec3f03b83d6b · outbound

This paper cites Transferable multimodal attack on vision-language pre- training models.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Transferable multimodal attack on vision-language pre- training models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.325788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 33e4cad2-684f-4527-bdff-8a5722965940 · outbound

This paper cites Dual attention suppres- sion attack: Generate adversarial camouflage in physical world.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Dual attention suppres- sion attack: Generate adversarial camouflage in physical world

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.316054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8520eb34-ca97-42d2-ac8b-24281697b944 · outbound

This paper cites Diversifying the High-level Features for better Adversarial Transferability.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Diversifying the High-level Features for better Adversarial Transferability

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.937330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e16cd594-65a8-4991-a135-8c90081a970e · outbound

This paper cites Transferable Adversarial Attacks for Image and Video Object Detection.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Transferable Adversarial Attacks for Image and Video Object Detection

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.941193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca47a575-6139-4011-a86c-cc76772f1d48 · outbound

This paper cites Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.944996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5042f3a5-0cc7-4215-af9e-5cfe52509bc6 · outbound

This paper cites Drivegpt4: Interpretable end-to-end autonomous driving via large language model.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Drivegpt4: Interpretable end-to-end autonomous driving via large language model

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.306737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 896b5aa8-bf0e-47cd-963b-b1de79bd22c0 · outbound

This paper cites Vlattack: Multimodal adversarial attacks on vision-language tasks via pre-trained models.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Vlattack: Multimodal adversarial attacks on vision-language tasks via pre-trained models

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.296105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b0ef4525-5d20-4cfc-8388-7223b45b464b · outbound

This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.956544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 749c5739-a25c-4d9a-b2b7-a401af56ef5d · outbound

This paper cites Interpreting and improving adversarial robustness of deep neural net- works with neuron sensitivity.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Interpreting and improving adversarial robustness of deep neural net- works with neuron sensitivity

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.285722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 551fc15f-bbc7-43ac-8633-fd17060ac177 · outbound

This paper cites AnyAttack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving AnyAttack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.963512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5de9e521-3937-498d-8f95-d9ee82e1766a · outbound

This paper cites Towards adver- sarial attack on vision-language pre-training models.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Towards adver- sarial attack on vision-language pre-training models

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.275714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e50a104b-5689-4d74-bfd7-5c032227d206 · outbound

This paper cites Module-wise Adaptive Adversarial Training for End-to-end Autonomous Driving.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Module-wise Adaptive Adversarial Training for End-to-end Autonomous Driving

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.971153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc8366ce-9095-4ad6-9dd6-a14f9854c545 · outbound

This paper cites Lanevil: Benchmarking the robustness of lane detection to environmental illusions.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Lanevil: Benchmarking the robustness of lane detection to environmental illusions

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.265691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bcf9bf43-b77a-48ee-8668-12d23549705e · outbound

This paper cites Visual Adversarial Attack on Vision-Language Models for Autonomous Driving.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Visual Adversarial Attack on Vision-Language Models for Autonomous Driving

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.978515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3585a7d5-75b7-4620-b1e6-68630b0bf73b · outbound

This paper cites On evaluating adversarial robustness of large vision- language models.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving On evaluating adversarial robustness of large vision- language models

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.255104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3e351456-b2b2-4cab-9a6d-74c54edca875 · outbound

This paper cites Advclip: Downstream- agnostic adversarial examples in multimodal contrastive learning.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Advclip: Downstream- agnostic adversarial examples in multimodal contrastive learning

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:55:08.244091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bbc7a042-2ede-49d2-841a-397887c0eeb2 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-10T15:55:07.989455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe308bde-4a34-44da-a885-d43d0db358b9 · outbound

This paper cites an unresolved cited work.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving Unresolved cited work

Reference 2021

Resolution: unresolved; raw_fallback, observed 2026-08-10T15:55:08.335315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation 404870a7-3020-493e-b62d-8bec5c7dca18 · 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 Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-16T19:31:13.102619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 65d0b44c-40d5-43e9-82c7-b6c59e5bd3b0 · 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 Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T14:05:25.727614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5796956-728e-4a50-830e-265f9ab218e1 · inbound

Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis cites this paper.

Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-15T21:56:41.027770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a5969ea7-1c79-4fa6-aa0f-bcc7e152a70f · inbound

A Patch-based Cross-view Regularized Framework for Backdoor Defense in Multimodal Large Language Models cites this paper.

A Patch-based Cross-view Regularized Framework for Backdoor Defense in Multimodal Large Language Models Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T22:05:49.157166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation df811b8f-f5c3-4060-8513-b4593151b893 · inbound

Adversarial Flow Matching for Imperceptible Attacks on End-to-End Autonomous Driving cites this paper.

Adversarial Flow Matching for Imperceptible Attacks on End-to-End Autonomous Driving Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:11:08.009639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 13534008-7363-4846-bbca-3af763f995bf · inbound

GuardAD: Safeguarding Autonomous Driving MLLMs via Markovian Safety Logic cites this paper.

GuardAD: Safeguarding Autonomous Driving MLLMs via Markovian Safety Logic Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T05:21:27.099436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 78440874-6672-480f-b107-ecef121e912b · inbound

WMAttack: Automated Attack Search for Adversarial Evaluation of World-Model Agents cites this paper.

WMAttack: Automated Attack Search for Adversarial Evaluation of World-Model Agents Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:15:22.174307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c7dbbf13-0617-49cd-b9c5-5f109929c191 · 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 Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-06-29T11:13:20.760382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 71b217a9-fbd1-430d-b301-c0485eecb121 · inbound

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective cites this paper.

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving

Reference 131

Resolution: verified exact; local_arxiv, observed 2026-07-09T00:25:48.507890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.