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Paper Citation Record · LEDGER

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models

As of 18 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2510.17759.

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

pith.paper-citation-record.v1
2510.17759 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T09:02:41.029778Z

measured 37 of 37 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

37 of 37 outbound references displayed

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  • unresolved37
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External citation measurements

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Outbound references

Observation 091f8ee0-ab77-4c76-aad0-fd5b72694fcc · outbound

This paper cites Qwen2.5-VL Technical Report.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Qwen2.5-VL Technical Report

Reference 1

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source=arxiv_source observed=2026-08-04T09:02:36.585841Z digest=sha256:5297ec8a7ad4632fc301675a25689e1fe70a01f533a39ca461c0eb7047ac45a5

Observation c6ff87cc-55f6-4fd3-83f7-8132cfddcc6e · outbound

This paper cites Trust-vlm: Thorough red-teaming for uncovering safety threats in vision-language models.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Trust-vlm: Thorough red-teaming for uncovering safety threats in vision-language models

Reference 2

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source=arxiv_source observed=2026-08-04T09:02:36.693293Z digest=sha256:8d50b3018ffb1119e8f57fd120a34e5c19f703a524de14b03c61e832a2c23864

Observation 822e213c-bd6e-4428-a4fe-d57ff9776962 · outbound

This paper cites JPS: Jailbreak Multimodal Large Language Models with Collaborative Visual Perturbation and Textual Steering.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models JPS: Jailbreak Multimodal Large Language Models with Collaborative Visual Perturbation and Textual Steering

Reference 3

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source=arxiv_source observed=2026-08-04T09:02:36.800236Z digest=sha256:c71a140d83e458365122d7e7fc6adeaf28f6d47dd3d4b11430a46c017941322e

Observation 13326619-084b-42db-916e-3bb3d21b4adf · outbound

This paper cites Gonzalez, Ion Stoica, and Eric P.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Gonzalez, Ion Stoica, and Eric P

Reference 4

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source=arxiv_source observed=2026-08-04T09:02:36.953149Z digest=sha256:dfe7d018a769a5db2fa9ec908e19370113ff19c1b6c32855b2b916598f84e06b

Observation 8eaa35df-b236-42ce-a2d1-b764b1728477 · outbound

This paper cites Eta: Evaluating then aligning safety of vision language models at inference time.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Eta: Evaluating then aligning safety of vision language models at inference time

Reference 5

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source=arxiv_source observed=2026-08-04T09:02:37.046720Z digest=sha256:18cf6e35fc232658a0a9d30f04b64dca23e386c5629b5cf7091b4873c2998f8b

Observation 6962335d-e8ab-44d5-97ea-00b444f5db40 · outbound

This paper cites Scaling rectified flow transformers for high-resolution image synthesis.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Scaling rectified flow transformers for high-resolution image synthesis

Reference 6

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source=arxiv_source observed=2026-08-04T09:02:37.148757Z digest=sha256:02013fba6378345ec3744f698aab6a339a52ee59538c7a873869bc6e9228d49f

Observation 7d688941-46ad-4aaf-9f2d-088d0a311427 · outbound

This paper cites Figstep: Jailbreaking large vision-language models via typographic visual prompts.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Figstep: Jailbreaking large vision-language models via typographic visual prompts

Reference 7

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source=arxiv_source observed=2026-08-04T09:02:37.370781Z digest=sha256:f01c6118001e2d7e048c599bd6c8ddc87500929641c2a279bc12e207251202a4

Observation 9adf6efa-39f9-4f04-a303-024b5adda4f7 · outbound

This paper cites The VLLM Safety Paradox: Dual Ease in Jailbreak Attack and Defense.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models The VLLM Safety Paradox: Dual Ease in Jailbreak Attack and Defense

Reference 8

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source=arxiv_source observed=2026-08-04T09:02:37.531373Z digest=sha256:b77cc543dfaa8fd98d839d0aee0979f674c483146d8910aeda72517042322f17

Observation 414a6897-861b-4f39-b9c4-a1ef332f6833 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Lora: Low-rank adaptation of large language models

Reference 9

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source=arxiv_source observed=2026-08-04T09:02:37.657331Z digest=sha256:87b315bfb5ac470ca0ef4209d8101acba03ab1ed1f64dd3dad6b49c0bcd0752d

Observation a4b938b8-0588-4903-b0f2-26c079ca1d0a · outbound

This paper cites Images are achilles' heel of alignment: Exploiting visual vulnerabilities for jailbreaking multimodal large language models.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Images are achilles' heel of alignment: Exploiting visual vulnerabilities for jailbreaking multimodal large language models

Reference 10

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source=arxiv_source observed=2026-08-04T09:02:37.787749Z digest=sha256:588bc0b2a05dd3500ab25f59eecf03acf6c5cbb5010985d56e639ec5bd611aa4

Observation c709635b-4625-4f7d-b9fa-534eb2b0a28c · outbound

This paper cites Images are achilles’ heel of alignment: Exploiting visual vulnerabilities for jailbreaking multimodal large language models.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Images are achilles’ heel of alignment: Exploiting visual vulnerabilities for jailbreaking multimodal large language models

Reference 11

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source=arxiv_source observed=2026-08-04T09:02:37.935623Z digest=sha256:75d4c2ea66e27a8f407701808ac126e56fdadef9954b4cd9fad8f5300f03ac40

Observation bf9bab15-904c-455e-bd2e-9bdf7b4e6b1f · outbound

This paper cites Vl-trojan: Multimodal instruction backdoor attacks against autoregressive visual language models.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Vl-trojan: Multimodal instruction backdoor attacks against autoregressive visual language models

Reference 12

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source=arxiv_source observed=2026-08-04T09:02:38.064964Z digest=sha256:69ccdac1b99090da905fbef82625ddf205575a36082f6defe6bec6e5d31c9794

Observation 72f10972-37fe-4be9-af0f-d6733e68f3ec · outbound

This paper cites Visual instruction tuning.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Visual instruction tuning

Reference 13

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source=arxiv_source observed=2026-08-04T09:02:38.117232Z digest=sha256:002c4129228062a31b719875aa8371dc848ddeb62232cce3cd6e12b5467500c0

Observation 257b9b0b-1e0d-493c-b832-35d95ca85419 · outbound

This paper cites Arondight: Red teaming large vision language models with auto-generated multi-modal jailbreak prompts.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Arondight: Red teaming large vision language models with auto-generated multi-modal jailbreak prompts

Reference 14

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source=arxiv_source observed=2026-08-04T09:02:38.191281Z digest=sha256:2f3e443a5444b81366b15ae0167fa030470404e991c6304a631aadb20a687913

Observation 81994794-73d5-4737-902b-fadf39160117 · outbound

This paper cites VERA: Variational Inference Framework for Jailbreaking Large Language Models.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models VERA: Variational Inference Framework for Jailbreaking Large Language Models

Reference 15

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source=arxiv_source observed=2026-08-04T09:02:38.279815Z digest=sha256:c50248e67b11f0cb1798fbce3d74da4f413d02f50b671c49fcdc03740c7910eb

Observation c588d445-d65f-4da7-bfee-0f88f4afb94e · outbound

This paper cites Backdooring vision-language models with out-of-distribution data.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Backdooring vision-language models with out-of-distribution data

Reference 16

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source=arxiv_source observed=2026-08-04T09:02:38.492727Z digest=sha256:6a53d70de2793e18b7553965141ae8d37b9a77dfc82a9e62cc4c13e6175aa2b2

Observation 59cd7582-ec3a-4e5a-9246-11362bc47eb0 · outbound

This paper cites Visual-RolePlay: Universal Jailbreak Attack on MultiModal Large Language Models via Role-playing Image Character.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Visual-RolePlay: Universal Jailbreak Attack on MultiModal Large Language Models via Role-playing Image Character

Reference 17

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source=arxiv_source observed=2026-08-04T09:02:38.637172Z digest=sha256:090f6044fccb75e351a7fb6c6afbf1f85f10d069f4313fcea1595072fd9a105a

Observation 47eb88d5-8b7d-4544-b473-3a07b91582a7 · outbound

This paper cites Forsyth, and Dan Hendrycks.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Forsyth, and Dan Hendrycks

Reference 18

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source=arxiv_source observed=2026-08-04T09:02:38.813023Z digest=sha256:c7197fbb098bc312a16eadfa29584618d4f9d16d97359013e221823b75bb982c

Observation 497efb19-bc19-44ac-8cc9-4ceb95cea2f9 · outbound

This paper cites Jailbreaking Attack against Multimodal Large Language Model.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Jailbreaking Attack against Multimodal Large Language Model

Reference 19

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source=arxiv_source observed=2026-08-04T09:02:38.962519Z digest=sha256:0c0beebf5147fe939c0b6bb63962c38eba8449151d8e0c6a1379126e91f72979

Observation 4649dad8-2c8f-4f16-9969-4600e5505cf2 · outbound

This paper cites Hello gpt-4o, 2024 a.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Hello gpt-4o, 2024 a

Reference 20

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source=arxiv_source observed=2026-08-04T09:02:39.128534Z digest=sha256:c3dc578f630996409a2c4ac70395cb1fcf92ba6e10f83d336567711c6cd45859

Observation da2bd73e-ae9c-44d6-a656-f1326093b6b4 · outbound

This paper cites Gpt-4o mini: Advancing cost-efficient intelligence, 2024 b.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Gpt-4o mini: Advancing cost-efficient intelligence, 2024 b

Reference 21

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source=arxiv_source observed=2026-08-04T09:02:39.254091Z digest=sha256:75c87db37089bc37d608756d84f94d7cdd509a4de16fd60634594ab1a708a95e

Observation dadd5531-ff1d-44e6-aedc-3ccc2cf2827b · outbound

This paper cites Learning To See But Forgetting To Follow: Visual Instruction Tuning Makes LLMs More Prone To Jailbreak Attacks.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Learning To See But Forgetting To Follow: Visual Instruction Tuning Makes LLMs More Prone To Jailbreak Attacks

Reference 22

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source=arxiv_source observed=2026-08-04T09:02:39.371506Z digest=sha256:7b4cea7c9d22ffe2e6cd61aa5922e086083c688bab86591a0291f2bfd934e7d2

Observation 44df4ca0-2727-458e-80f4-516314822216 · outbound

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

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Visual adversarial examples jailbreak aligned large language models

Reference 23

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source=arxiv_source observed=2026-08-04T09:02:39.498249Z digest=sha256:2618bc21a50d0cb977c1e2985588d509519b1f32c12c39f7d46d1427f40f609b

Observation ae799f24-33b2-466b-b074-5fb104fdb242 · outbound

This paper cites Vision-LLMs Can Fool Themselves with Self-Generated Typographic Attacks.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Vision-LLMs Can Fool Themselves with Self-Generated Typographic Attacks

Reference 24

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source=arxiv_source observed=2026-08-04T09:02:39.623038Z digest=sha256:556ab84b64f679967b469610632aa27edc7f64a2a495c24126feb7dc0fcba2a9

Observation 2549ffbb-e9e0-43bd-af0f-37b5366acbe2 · outbound

This paper cites Jailbreak in pieces: Compositional adversarial attacks on multi-modal language models.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Jailbreak in pieces: Compositional adversarial attacks on multi-modal language models

Reference 25

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source=arxiv_source observed=2026-08-04T09:02:39.794461Z digest=sha256:03d0628fb460755d3b5e363c14329f97e85fb2627c77636471a19d098e3dd60c

Observation 8ec290b4-3d51-490c-9ec3-ad4fe12b499a · outbound

This paper cites A strongreject for empty jailbreaks.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models A strongreject for empty jailbreaks

Reference 26

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source=arxiv_source observed=2026-08-04T09:02:39.855246Z digest=sha256:0f4e659893f9e14f9c01ced25bd79ad17945e0f856298d79f79fccb950cd252e

Observation 5aa58fde-4c0b-4fa3-9308-6ec03bda1952 · outbound

This paper cites Imgtrojan: Jailbreaking vision-language models with ONE image.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Imgtrojan: Jailbreaking vision-language models with ONE image

Reference 27

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source=arxiv_source observed=2026-08-04T09:02:39.941181Z digest=sha256:98731952ca04c64ef3740aa87891504783be367c1135784cc509b1fe6424622b

Observation 7c02fb54-f84d-4526-8ab4-26af9073ccc9 · outbound

This paper cites Ideator: Jailbreaking and benchmarking large vision-language models using themselves.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Ideator: Jailbreaking and benchmarking large vision-language models using themselves

Reference 28

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source=arxiv_source observed=2026-08-04T09:02:40.051324Z digest=sha256:67411f0e81aaf07c360f61338a2a81ba0daea126addc8feae6616b82d5fa3c83

Observation 7e210163-3f8a-407e-8757-f27ce44d7a38 · outbound

This paper cites Jailbreak large vision-language models through multi-modal linkage.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Jailbreak large vision-language models through multi-modal linkage

Reference 29

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source=arxiv_source observed=2026-08-04T09:02:40.152235Z digest=sha256:20e9ba9f7f563c2a388cf7ab532e260adfc4370b0faf48bc7fdeb940500fc381

Observation 52927ac3-116f-4a00-bce0-c5ab5b265af2 · outbound

This paper cites Simple statistical gradient-following algorithms for connectionist reinforcement learning.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Simple statistical gradient-following algorithms for connectionist reinforcement learning

Reference 30

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source=arxiv_source observed=2026-08-04T09:02:40.234142Z digest=sha256:8366c6c8286da7f1eaf7bd14e6b0311e58b1e5b7f4f9c909abc1d6138c7567e9

Observation 91f7918b-66f7-4817-ab8c-7c893431ab87 · outbound

This paper cites Distraction is all you need for multimodal large language model jailbreaking.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Distraction is all you need for multimodal large language model jailbreaking

Reference 31

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source=arxiv_source observed=2026-08-04T09:02:40.324350Z digest=sha256:81af6a08f1690c91e7f51d0c683e206bbdaa80c1a4ec7493cb755bc2f4b43aa7

Observation daee661d-3b7b-47f8-a12d-ecb072bc89bb · outbound

This paper cites Anyattack: Towards large-scale self-supervised adversarial attacks on vision-language models.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Anyattack: Towards large-scale self-supervised adversarial attacks on vision-language models

Reference 32

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source=arxiv_source observed=2026-08-04T09:02:40.441757Z digest=sha256:db4ed33b2fa07e128f97775391b1b2d7ec579fb7ea238c36389a97ef9c7bad3a

Observation 74d883df-13ad-462b-9443-1746eb1de9d7 · outbound

This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 33

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source=arxiv_source observed=2026-08-04T09:02:40.543643Z digest=sha256:b08de772d4e5885aef7c3de2e1d1dfc05b8b54031adcee87c0506f6d9c1055ab

Observation 573a73fc-0bb4-4d46-a44b-f90e820b36ac · outbound

This paper cites write newline.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models write newline

Reference 34

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source=arxiv_source observed=2026-08-04T09:02:40.666405Z digest=sha256:eb3700ddca2776dc224713b7d4596fd3d4e7395e14a2a379c06afea2aa834486

Observation c2d0ce8c-4fe0-4f23-8812-cf1c2369cf0a · outbound

This paper cites @esa (Ref.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models @esa (Ref

Reference 35

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source=arxiv_source observed=2026-08-04T09:02:40.833370Z digest=sha256:5d26ff27b75ab9fdbae44606a6901ac1031f7908592f126ca893117e1b7a25fc

Observation 399611c8-40de-44ec-86f1-68bd2449fc8c · outbound

This paper cites an unresolved cited work.

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Unresolved cited work

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source=arxiv_source observed=2026-08-04T09:02:40.913886Z digest=sha256:206948fc89574b605d6c0b8b9aa4b5ef0e73801314fcc70daf070e1c393d8f7e

Observation 488101bd-a5ae-4d55-91c9-568c30c5f659 · outbound

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VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models Unresolved cited work

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Pith citing papers

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