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Paper Citation Record · LEDGER

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment

As of 8 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 13 inbound Pith citation observations for arXiv:2505.21494.

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

pith.paper-citation-record.v1
2505.21494 v1

Coverage vector

measured 67 of 67 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:35:56.832563Z

measured 80 of 80 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:58:51.899166Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T12:56:14.862810Z

Reference resolution

67 of 67 outbound references displayed

  • verified exact1
  • verified fuzzy10
  • unresolved54
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d6d6c590-42d6-41b0-822e-10ef275b5588 · outbound

This paper cites GPT-4 Technical Report.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment GPT-4 Technical Report

Reference 1

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source=pdf_text observed=2026-08-07T13:35:49.458315Z digest=sha256:b9fc1f054d70d9b3aae87db9267128c48b7162551b62fd20f01cd676ac9f7a7a

Observation 53aba7bc-ad04-4371-a30b-c53603628214 · outbound

This paper cites Flamingo: a visual language model for few-shot learning.Advances in Neural Information Processing Systems, 35: 23716–23736, 2022.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Flamingo: a visual language model for few-shot learning.Advances in Neural Information Processing Systems, 35: 23716–23736, 2022

Reference 2

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source=pdf_text observed=2026-08-07T13:35:49.567080Z digest=sha256:b36b5b6eeb4d3bf2dc297e102c9a6e90436092fd695f440b57c9139f3bf1022e

Observation debd7b32-c2c1-4a71-b9b7-6aa7a6b85f88 · outbound

This paper cites PaLM 2 Technical Report.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment PaLM 2 Technical Report

Reference 3

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source=pdf_text observed=2026-08-07T13:35:49.671246Z digest=sha256:3a642b05a81c11a69002b0f698cb95bbd8523bcd76bf690cf87052b190afdc7a

Observation 4039150c-a3ae-444f-a8b0-24972fc7bc92 · outbound

This paper cites Image Hijacks: Adversarial Images can Control Generative Models at Runtime.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Image Hijacks: Adversarial Images can Control Generative Models at Runtime

Reference 4

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source=pdf_text observed=2026-08-07T13:35:49.779331Z digest=sha256:901ba01f9cea27b69f0fd4c35af8d603d5a2139cad6bf7887c764afca7cfded5

Observation f873176d-2936-4a92-920e-f26e041679cc · outbound

This paper cites Improv- ing the transferability of targeted adversarial examples through object-based diverse input.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Improv- ing the transferability of targeted adversarial examples through object-based diverse input

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:35:49.904749Z digest=sha256:1be9bac6f6f89be0c1f3b32235109412364b9aade3ad19cfe5e2d62aa0aa0338

Observation d50c7d2c-baf9-477d-b570-0939cca85bf2 · outbound

This paper cites Towards evaluating the robustness of neural networks.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Towards evaluating the robustness of neural networks

Reference 6

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source=pdf_text observed=2026-08-07T13:35:50.024648Z digest=sha256:22c04928e2b4811908c395e73757eeac8b3e36cafecf9c06db36d4d6aecfd866

Observation 77e1130e-de80-4e79-896e-53283f08cf13 · outbound

This paper cites Are aligned neural networks adversarially aligned?.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Are aligned neural networks adversarially aligned?

Reference 7

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source=pdf_text observed=2026-08-07T13:35:50.164811Z digest=sha256:14720d3c424cb521f6cd1dbf4f66d2929f621799c4a346992c91e9fe41b93b07

Observation cf1d090e-49ae-4a2f-8ac1-d4e618751a4b · outbound

This paper cites Rethinking Model Ensemble in Transfer-based Adversarial Attacks.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Rethinking Model Ensemble in Transfer-based Adversarial Attacks

Reference 8

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source=pdf_text observed=2026-08-07T13:35:50.302743Z digest=sha256:ee703ec9688f0d72bd833cfc019ae64df83e4971f1a76b1bb946d04aeec60108

Observation 1f2b4431-a890-4e58-93da-2020e2a032a6 · outbound

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

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240): 1–113, 2023

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:35:50.455824Z digest=sha256:0694848f284bd653c3ba0eb99cdbc06ca361ae2860ceb4826f518da4949628f7

Observation f09c250d-f3f8-40b9-944f-7b788bbe851c · outbound

This paper cites A survey on multimodal large language models for autonomous driving.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment A survey on multimodal large language models for autonomous driving

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:35:50.612019Z digest=sha256:027cb2a87cf54c4fec4c4f67911697befec2d1bfe8cf947f72228765647a2362

Observation 533f328e-9d98-4975-9baf-de628077d6f1 · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.Advances in neural information processing systems, 26, 2013.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Sinkhorn distances: Lightspeed computation of optimal transport.Advances in neural information processing systems, 26, 2013

Reference 11

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source=pdf_text observed=2026-08-07T13:35:50.689362Z digest=sha256:0d8ee239715804351fcd9cfa7ea3a7ffe785b3eee925585ea0abb20b1daac086

Observation 73af7a46-2e2e-4348-8f04-7932ad257400 · outbound

This paper cites advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch

Reference 12

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source=pdf_text observed=2026-08-07T13:35:50.841588Z digest=sha256:5c7bbb3a9d3f65d3318f770eb048fefb9c9b74aad00756cb2a81ba189b4e24aa

Observation fab8b3b2-4a32-4dcf-b09c-6f8515aa05db · outbound

This paper cites How Robust is Google's Bard to Adversarial Image Attacks?.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment How Robust is Google's Bard to Adversarial Image Attacks?

Reference 13

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source=pdf_text observed=2026-08-07T13:35:50.982242Z digest=sha256:d59acc249ae46898d9775cdecbd99e97b9e593f4c47b0a7266dd4aff832dfa75

Observation 373cb766-e746-4356-b191-4ad9c4dd6ed6 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

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source=pdf_text observed=2026-08-07T13:35:51.083696Z digest=sha256:93739c48e846596af8a4cd16052b6a5894daad5a6daf2f7d49c88dcd21da3c47

Observation 133529ae-b798-4bbb-ba8c-501f3debd4f7 · outbound

This paper cites Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned

Reference 15

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source=pdf_text observed=2026-08-07T13:35:51.204470Z digest=sha256:c18af7b36092b09f15583416764213267a10fdf3ffcf8edbb08410c1ebc934a5

Observation a5c23d5b-6673-41d0-a413-8aa5f2620c32 · outbound

This paper cites LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

Reference 16

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source=pdf_text observed=2026-08-07T13:35:51.324640Z digest=sha256:9d53c9871b1f97a62a5f18dec3a7a93464a3cf8b8e230b5a625fb5b9355ad4bb

Observation 87b55f07-3992-464a-b753-76803431ec0f · outbound

This paper cites Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial Trajectory.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial Trajectory

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:35:51.426223Z digest=sha256:5bd6af0308748eeeb2068db70580059b570a2e4262f5946a21fc5af120dbe62c

Observation 921febc2-46c8-4fdd-9ae1-3829b94e1aec · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Explaining and Harnessing Adversarial Examples

Reference 19

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source=pdf_text observed=2026-08-07T13:35:51.600167Z digest=sha256:be43680571a9b22ceaac6ca922fc377d26ef61494acf7c6b2b4827d64f83854f

Observation a67fab29-5b17-4fa7-8ecb-2d2a3aa6c4e2 · outbound

This paper cites Agent smith: A single image can jailbreak one million multimodal llm agents exponentially fast.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Agent smith: A single image can jailbreak one million multimodal llm agents exponentially fast

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:35:51.708519Z digest=sha256:2c751b185d36b99d9e93052ef770593694836ef577d9650b7750f40bff29c8db

Observation eaea06a7-6d6a-4179-bcd4-d5652491a65b · outbound

This paper cites Countering Adversarial Images using Input Transformations.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Countering Adversarial Images using Input Transformations

Reference 21

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source=pdf_text observed=2026-08-07T13:35:51.819305Z digest=sha256:c15a035815acc12f8b196591ffb6ea16d75b77f685f1fc7b43de575e61ba9ac9

Observation e4e90e0d-4279-4e19-9fd2-1ff88e579ba3 · outbound

This paper cites Efficient generation of targeted and transferable adversarial examples for vision-language models via diffusion models.IEEE Transactions on Information Forensics and Security, 2024.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Efficient generation of targeted and transferable adversarial examples for vision-language models via diffusion models.IEEE Transactions on Information Forensics and Security, 2024

Reference 22

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source=pdf_text observed=2026-08-07T13:35:51.945845Z digest=sha256:688e15809a8f2c5900ee3c7acdf16c22a834284eed44a6bb19beb74dea410f42

Observation 298d6280-0ced-4e25-beb5-41c086266e93 · outbound

This paper cites OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization

Reference 23

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source=pdf_text observed=2026-08-07T13:35:52.106307Z digest=sha256:55b54f151406867a3e6a410719aca99ecdbebfd5b435194933abbdd5d02c03f0

Observation ecdbbe7a-cca9-4aa1-9760-674bf2d4c7be · outbound

This paper cites SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation

Reference 24

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source=pdf_text observed=2026-08-07T13:35:52.215725Z digest=sha256:16f9f53738aca8db4266b13f8274cbcce6cf0c67cc6b57e537957ae1828e367f

Observation a65dbc69-8253-457c-8d16-faa8cab41031 · outbound

This paper cites Language Is Not All You Need: Aligning Perception with Language Models.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Language Is Not All You Need: Aligning Perception with Language Models

Reference 25

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source=pdf_text observed=2026-08-07T13:35:52.279416Z digest=sha256:3650bbccf00350ed94cec2b180f23e51c2c7581dd82bbb97b14607d62b1e1728

Observation a5854e17-ed80-4b06-9ea0-d8fe21ed1b1f · outbound

This paper cites Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models

Reference 26

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source=pdf_text observed=2026-08-07T13:35:52.404988Z digest=sha256:aa3789534dbb0ea9a4a202de513356e3c0afaa54199e48de01e01534a0c8ecb6

Observation 58a8fe99-1638-4ad1-ad42-378876adc7ce · outbound

This paper cites Comdefend: An efficient image compression model to defend adversarial examples.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Comdefend: An efficient image compression model to defend adversarial examples

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:35:52.527419Z digest=sha256:a996c069f00c3ee05e61cae4f79c79c8eaf4ebaae03bfa3c962093d00a0e8dbd

Observation 61b1cab7-2e3e-4b9b-b51b-1b94c1248c2b · outbound

This paper cites Natural language understanding and inference with mllm in visual question answering: A survey.ACM Computing Surveys, 57(8):1–36, 2025.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Natural language understanding and inference with mllm in visual question answering: A survey.ACM Computing Surveys, 57(8):1–36, 2025

Reference 28

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source=pdf_text observed=2026-08-07T13:35:52.599556Z digest=sha256:fae791efa1f9d6e7ea5a41e78ab1efc5c102ccbfe4548bc6fb4ed1083e7b8463

Observation 6a34e8f0-e763-46c1-86b4-a2962d01d0e2 · outbound

This paper cites BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

Reference 29

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source=pdf_text observed=2026-08-07T13:35:52.680342Z digest=sha256:32414eead413953b6a5397acfd97c7e5942808e1d007f6a837bb8c6973af9182

Observation 23b3972f-bd30-42bf-b519-bc4af4101549 · outbound

This paper cites VideoChat: Chat-Centric Video Understanding.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment VideoChat: Chat-Centric Video Understanding

Reference 30

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source=pdf_text observed=2026-08-07T13:35:52.839627Z digest=sha256:d216a0187c2df07e8892dfd6750544b3cf1b2becd883dbe8417ea287c28b3b58

Observation b1e487e3-63f5-4fad-a7f3-e0ebddded0ca · outbound

This paper cites Improving context understanding in multimodal large language models via multimodal composition learning.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Improving context understanding in multimodal large language models via multimodal composition learning

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:35:52.951027Z digest=sha256:46e6e6dd941b54c6bdc50938b4aa5cf0ed236692401c8093bc933ef79ec6e669

Observation 67fd2408-7657-47dc-80fd-02e433a518b1 · outbound

This paper cites A frustratingly simple yet highly effective attack baseline: Over 90% success rate against the strong black-box models of gpt-4.5/4o/o1.arXiv preprint arXiv:2503.10635, 2025.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment A frustratingly simple yet highly effective attack baseline: Over 90% success rate against the strong black-box models of gpt-4.5/4o/o1.arXiv preprint arXiv:2503.10635, 2025

Reference 32

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source=pdf_text observed=2026-08-07T13:35:53.099184Z digest=sha256:cc856586bacddc0e90134bb3da6ccc18fb005f246b51ab67929b89918eb421c0

Observation 3787a6af-926e-4f33-a16f-c8057a359b74 · outbound

This paper cites Enhancing Advanced Visual Reasoning Ability of Large Language Models.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Enhancing Advanced Visual Reasoning Ability of Large Language Models

Reference 33

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source=pdf_text observed=2026-08-07T13:35:53.235556Z digest=sha256:35532b90632ed667dd1de130ba3dff24f057db28074b072132d8cec3125d89bc

Observation d06aba4f-4b63-4865-833a-368c781dd20f · outbound

This paper cites Microsoft coco: Common objects in context.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Microsoft coco: Common objects in context

Reference 34

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source=pdf_text observed=2026-08-07T13:35:53.358879Z digest=sha256:155a41c7000e353e3728310897a75f1351afd218e63dc1fa752367afe0ddadd0

Observation d4c1560e-d017-4d83-9100-354e2efb8419 · outbound

This paper cites Visual Instruction Tuning.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Visual Instruction Tuning

Reference 35

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source=pdf_text observed=2026-08-07T13:35:53.444061Z digest=sha256:a53324fabd33cd1a6eb00c4a2e2bbb55cadc6db7ebbdb38da6f7245f0ac33c40

Observation df524861-71a2-4380-9ea3-11c54cc2b1ef · outbound

This paper cites Improved baselines with visual instruction tuning.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Improved baselines with visual instruction tuning

Reference 36

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source=pdf_text observed=2026-08-07T13:35:53.528417Z digest=sha256:813ebfbf943fff83d7843237c3cbbf4eaa30cab91d7843636d9b5687e96a1baf

Observation cccdc95a-89aa-4648-9e47-aaf4bb2cc0e4 · outbound

This paper cites Safety of Multimodal Large Language Models on Images and Texts.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Safety of Multimodal Large Language Models on Images and Texts

Reference 37

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source=pdf_text observed=2026-08-07T13:35:53.646908Z digest=sha256:739a539f7456cad3c2dab4b7c59359c83c34e2dd28dcb061fcc76ec82021c4d4

Observation 7730ddf2-4184-494a-a4b0-0763b5c50bca · outbound

This paper cites Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study

Reference 38

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source=pdf_text observed=2026-08-07T13:35:53.728471Z digest=sha256:304a448a2f8b44a2fac7142d6f719fbd00b9479a8db3c3c5fc3fdbaac66677fd

Observation 30817079-a523-4f02-92f4-a08dc790a7db · outbound

This paper cites Frequency domain model augmentation for adversarial attack.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Frequency domain model augmentation for adversarial attack

Reference 39

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source=pdf_text observed=2026-08-07T13:35:53.862054Z digest=sha256:fe072de980a2dc8502dba7b24269ebf602cadb741c985a9db2cdec38cb4f513c

Observation 7a66557e-295c-44d8-a4b5-c70850689098 · outbound

This paper cites Questioning, answering, and captioning for zero-shot detailed image caption.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Questioning, answering, and captioning for zero-shot detailed image caption

Reference 40

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:35:53.960674Z digest=sha256:c8d0474aa735eeb86826d498454d5ec10b213001802d035ca8a9bc3b839f6f9a

Observation 051b4a18-41c9-48cb-afad-412c5ced3412 · outbound

This paper cites Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models

Reference 41

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source=pdf_text observed=2026-08-07T13:35:54.066433Z digest=sha256:e9d9ce80c57cf4e3b30188ef7d03059a58b890e9ef8e6b9c5b65357b54d9843d

Observation d82267be-1595-454c-b0c4-e8fdd0f97725 · outbound

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

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 42

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source=pdf_text observed=2026-08-07T13:35:54.172351Z digest=sha256:e9c38b4fa9b3a1b0a29dd0f3470d3a47f054b0b6200b97d78b7f6679344ffef2

Observation 046113fe-17c2-41ff-af41-47069a38b3b9 · 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 against Closed-Source MLLMs via Feature Optimal Alignment Training language models to follow instructions with human feedback.Advances in Neural Information Processing Systems, 35:27730–27744, 2022

Reference 43

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source=pdf_text observed=2026-08-07T13:35:54.277385Z digest=sha256:35d4861f25bc7e8ba154c6bf03c0aaf8de096e3e45970ba755ca50e67ed06357

Observation b82bf650-0879-43e3-8872-4fd8ccc39a5f · outbound

This paper cites Red Teaming Language Models with Language Models.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Red Teaming Language Models with Language Models

Reference 44

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source=pdf_text observed=2026-08-07T13:35:54.349572Z digest=sha256:352f77911a44a62473f66c780de1b4c14e2d03e773babbea9dfa86bad7b581e9

Observation b6913d6d-2efe-4ffd-8948-bf8520245544 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Learning transferable visual models from natural language supervision

Reference 45

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T13:35:54.443770Z digest=sha256:0e2f8d7f29005284b598d4dbd83115ca59acea87cfb300619ef2d370879f6a6b

Observation 184826b5-a3fe-4e46-b094-04f2eb32be00 · outbound

This paper cites Image Captioning Evaluation in the Age of Multimodal LLMs: Challenges and Future Perspectives.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Image Captioning Evaluation in the Age of Multimodal LLMs: Challenges and Future Perspectives

Reference 46

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source=pdf_text observed=2026-08-07T13:35:54.549170Z digest=sha256:48300231a0c7654963ab966d9bc851e4d680323da99d827dc242e7f6c9b52540

Observation 6f82d06b-9593-4a8f-b71d-ef1caed492b3 · outbound

This paper cites BLOOM: A 176B-Parameter Open-Access Multilingual Language Model.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Reference 47

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source=pdf_text observed=2026-08-07T13:35:54.659888Z digest=sha256:aa5f5a1cab7d65e8ac65ff7ddc1f0ff9a3bdb98ed0c90947bff68bc0626751e6

Observation 07540c05-12cb-4f1c-b301-a6e7051a9fe8 · outbound

This paper cites On the adversarial robustness of multi-modal founda- tion models.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment On the adversarial robustness of multi-modal founda- tion models

Reference 48

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source=pdf_text observed=2026-08-07T13:35:54.845752Z digest=sha256:f177b079b6e24d8b380cc76fa6ef7f16679fc8bfdb55e0a298674ef801c7b9a6

Observation fa667496-da25-46f1-8041-c67904e0c053 · outbound

This paper cites PandaGPT: One Model To Instruction-Follow Them All.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment PandaGPT: One Model To Instruction-Follow Them All

Reference 49

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:35:54.954745Z digest=sha256:14d3ea73fc8bb000dcf9cec661fcadbca26be070f58bbe543b390af7f0228113

Observation c570df1d-1fa9-4844-bc50-6962a1af0081 · outbound

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

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment LLaMA: Open and Efficient Foundation Language Models

Reference 50

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:35:55.040125Z digest=sha256:0e9f45d8e60ebeefdf431eb73f3ae11b34d8a5d157d60bf023240b10974e9cd0

Observation 237911bb-7dd7-4e92-9109-59936103a8e5 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 51

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source=pdf_text observed=2026-08-07T13:35:55.132411Z digest=sha256:cca325cea0a125944b63d08d21874086348cadd2110889ab9d5e376c63227377

Observation d9988ab8-1bc9-4f7c-a53c-e55133945edb · outbound

This paper cites Multimodal few-shot learning with frozen language models.Advances in Neural Information Processing Systems, 34:200–212, 2021.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Multimodal few-shot learning with frozen language models.Advances in Neural Information Processing Systems, 34:200–212, 2021

Reference 52

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source=pdf_text observed=2026-08-07T13:35:55.254426Z digest=sha256:e085df6586e812ea69694161f0382e6c958d207d2c91eac96f2a1a4f6f8016e0

Observation cf22a302-1e01-4e57-b838-dfdc96986a09 · outbound

This paper cites How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs

Reference 53

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T13:35:55.414755Z digest=sha256:e793557fa7ab9fed2f0a9c802aebb9cafcf91e7bddf0b8d97f3f39cdca444c53

Observation b25bbc94-ba7f-4cd9-9b70-0e19cf893dba · outbound

This paper cites Springer, 2009.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Springer, 2009

Reference 54

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:35:55.501406Z digest=sha256:344e7aedd48b7b35ac9b8ade5d2b768865dfe45f3ed9ee24a238dbcfebd32c7d

Observation 8311a177-207b-4aaa-be9e-ff012ab7ce2b · outbound

This paper cites InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models

Reference 55

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T13:35:55.625791Z digest=sha256:4a367d8fc737948d1b86eaa0053e68f0785a1ee0d2bc9700a73a41539c84dd66

Observation cf193db2-d84d-4f48-897e-ebee42b7be9a · outbound

This paper cites Black-box sparse adversarial attack via multi-objective optimisation.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Black-box sparse adversarial attack via multi-objective optimisation

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:35:58.611990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:35:55.753909Z digest=sha256:01cd25953d1f63ea4878d237ca614fa200590e8444b77800eb72bf1eb756982b

Observation 9a79c70a-161c-46af-9692-6c381a109884 · outbound

This paper cites Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models

Reference 57

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source=pdf_text observed=2026-08-07T13:35:55.844596Z digest=sha256:091cc2af77213daa56017b795af9dc81b89e951cda1af3806236291726d66ac4

Observation 8972e13b-5038-458c-90c2-34d09d3499e8 · outbound

This paper cites An empirical study of gpt-3 for few-shot knowledge-based vqa.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment An empirical study of gpt-3 for few-shot knowledge-based vqa

Reference 58

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verified fuzzy
raw_fallback, observed 2026-08-07T13:35:58.453771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:35:55.913819Z digest=sha256:fe38e33a94a50a0317dcf5d3e3eb77a502b3a747f7a55df0b7cf3ad6ee4cfb06

Observation 9c5b7731-6173-402f-bac8-5f34f612c299 · outbound

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

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment AnyAttack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models

Reference 59

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T13:35:55.980677Z digest=sha256:a275c20a29839192fd9e0370fdf7c285e3270580074998a05a261dcd904c9332

Observation b088cfe2-7099-4481-9238-10f15653e530 · outbound

This paper cites On Evaluating Adversarial Robustness of Large Vision-Language Models.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment On Evaluating Adversarial Robustness of Large Vision-Language Models

Reference 60

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source=pdf_text observed=2026-08-07T13:35:56.073322Z digest=sha256:cda6c7959fc7ab352d9a46e96a37f873fdf1762858dca27e20a1b6cf3364cb44

Observation c82e27a2-fcc2-4b7a-9455-2e7e870501b1 · outbound

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

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 61

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source=pdf_text observed=2026-08-07T13:35:56.169643Z digest=sha256:88b7a6a38f0b7987a6784d167e13afc35fbf9b1e6a969d489d9b332c0a216ffd

Observation 49f3875c-3f69-4970-91e3-01b9fe572376 · outbound

This paper cites Boosting transferability of targeted adversarial examples with non-robust feature alignment.Expert Systems with Applications, 227:120248, 2023.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Boosting transferability of targeted adversarial examples with non-robust feature alignment.Expert Systems with Applications, 227:120248, 2023

Reference 62

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:35:56.253126Z digest=sha256:f3a667cba756221ed1e75ddd3a6ef2c4df435bc2f6a4453bc7a45f5d413123f3

Observation 02365128-d5a3-4427-853b-65671b5f5a03 · outbound

This paper cites PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts

Reference 63

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malformed identifier
no resolver link, observed 2026-08-07T13:35:56.355578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:35:56.355578Z digest=sha256:bb50adfc97be9aac0d17a3c2a9c64816d4573b440013c175a2068b5fcd2306d5

Observation 4e3ec945-1a16-4750-9926-157e393c97ba · outbound

This paper cites an unresolved cited work.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Unresolved cited work

Reference 64

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T13:35:56.434228Z digest=sha256:1f76570f46a5000b423e85e699ec8f599c33c013e0df629d96c6201233c4d85d

Observation 66e458ea-c708-46e0-ac33-0e1342c1c617 · outbound

This paper cites an unresolved cited work.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Unresolved cited work

Reference 65

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T13:35:56.529699Z digest=sha256:6d9d5391143a6e21aac1a15df3bb34eaa1297ac12e012744bfe8fb59625ffc4b

Observation 9102bc7d-9cd1-4e9f-9cdb-cb94c64b4694 · outbound

This paper cites Focus on **whether both descriptions fundamentally describe the same thing.**.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Focus on **whether both descriptions fundamentally describe the same thing.**

Reference 66

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source=pdf_text observed=2026-08-07T13:35:56.615468Z digest=sha256:7698af67ee7eccf18805998aa26430d32aea4cbca4aecd38ff4fa600c07f4ea0

Observation 7ad76c61-c4b0-44d4-94bc-a818c5a2ba9a · outbound

This paper cites an unresolved cited work.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment Unresolved cited work

Reference 67

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:35:56.727415Z digest=sha256:e31796f0f1f5599f79a9934cf720edada05af0f43c5dfaeb32f4e6b10fb1da6b

Observation e8aa0a1f-03e6-45f3-9920-6fe284f78bb6 · outbound

This paper cites {input_text_1}.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment {input_text_1}

Reference 68

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malformed identifier
raw_fallback, observed 2026-08-07T13:35:57.923750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:35:56.832563Z digest=sha256:88be4889e994867719e5fbbf9af76f7fd73987ef30714183d998472517e82059

Pith citing papers

Observation a035745a-d293-41b7-8b98-5a9cef997ecb · inbound

3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage Generation cites this paper.

3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage Generation Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:58:51.899166Z digest=sha256:8681e0e3159d8bde92785df3bf4546eadfdd5fde489c4dfbde5f6bba96b0f3f7

Observation 52dcbe22-e334-4464-890f-9786224efb40 · inbound

Hidden Tail: Adversarial Image Causing Stealthy Resource Consumption in Vision-Language Models cites this paper.

Hidden Tail: Adversarial Image Causing Stealthy Resource Consumption in Vision-Language Models Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment

Reference 10

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no resolver link, observed 2026-08-05T16:15:27.911200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:15:27.911200Z digest=sha256:d7f6fd3dd8d387eb43bc0ee60473e801ca2e5a65b5851f59220dfcae9600334e

Observation 49bd0555-2734-4236-a374-51874690cb3c · inbound

Contrastive Spectral Rectification: Test-Time Defense towards Zero-shot Adversarial Robustness of CLIP cites this paper.

Contrastive Spectral Rectification: Test-Time Defense towards Zero-shot Adversarial Robustness of CLIP Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment

Reference 2019

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unresolved
no resolver link, observed 2026-08-03T07:47:37.534486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T07:47:37.534486Z digest=sha256:d4c24d3a265d536551a2eff0d63c77a8bb7277add4c98ed553028dea7eb09028

Observation 8a86ff78-7cc6-4025-aba1-4b0d1c0c270c · inbound

Universal Adversarial Attacks against Closed-Source MLLMs via Target-View Routed Meta Optimization cites this paper.

Universal Adversarial Attacks against Closed-Source MLLMs via Target-View Routed Meta Optimization Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-16T09:27:40.973099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-16T09:26:14.799360Z digest=sha256:33b9249044ba1c937af83fc28c684d437056531c4712e152f3c73d45d4be8f8f

Observation b7cf3046-9cbb-4aa6-969c-a972b9a70890 · inbound

Mosaic: Multimodal Jailbreak against Closed-Source VLMs via Multi-View Ensemble Optimization cites this paper.

Mosaic: Multimodal Jailbreak against Closed-Source VLMs via Multi-View Ensemble Optimization Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-11T05:35:59.178907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T18:03:36.805784Z digest=sha256:0f7d5662cce9e2538dcc004ef6f5ee56ea786fd6d66bf7c909cda292dddb5a1d

Observation 50504f78-419d-4279-a532-7846a7ccc129 · inbound

Adversarial Attacks Against MLLMs via Progressive Resolution Processing and Adaptive Feature Alignment cites this paper.

Adversarial Attacks Against MLLMs via Progressive Resolution Processing and Adaptive Feature Alignment Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:21:27.955839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-12T04:19:31.570783Z digest=sha256:9437d65db39516b9847c3ccebab1115967c7b469f780924256ac3165452a137a

Observation b6f53335-55b6-46a9-9415-5f5118429e7c · inbound

DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models cites this paper.

DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment

Reference 22

Resolution
verified exact
arxiv_id, observed 2026-05-20T18:33:37.919303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-20T18:31:48.770507Z digest=sha256:ffb7063e275723599ce13adc4359da6e900b8c3118c3e6271c2c8e8b4a2c93bc

Observation c77dc30e-d393-4c5b-91c3-47d074ce88e6 · inbound

CogniVerse: Revolutionizing Multi-Modal Retrieval-Augmented Generation with Cognitive Reflection and Geometric Reasoning cites this paper.

CogniVerse: Revolutionizing Multi-Modal Retrieval-Augmented Generation with Cognitive Reflection and Geometric Reasoning Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-06-29T08:23:15.763386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-29T08:14:20.558527Z digest=sha256:13fdff0e5ee4910ca12841647ae0792ab4d81a9f5111271ebd1adc7e4280827f

Observation 66e48b9e-d251-4a04-ac7d-ecc359911ffe · inbound

REALM: A Unified Red-Teaming Benchmark for Physical-World VLMs cites this paper.

REALM: A Unified Red-Teaming Benchmark for Physical-World VLMs Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-07-04T10:29:45.732836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T08:44:45.853401Z digest=sha256:97ed6cfc23cc8907ec4b1dda8b724cce030ae61d8245fd429a012301e53017a8

Observation aa01307b-df84-46ea-bef2-bf5abc3ae45a · inbound

MIRAGE: Protecting against Malicious Image Editing via False Moderation cites this paper.

MIRAGE: Protecting against Malicious Image Editing via False Moderation Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-07-04T15:09:54.649235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T01:52:12.291420Z digest=sha256:451919ec2205d3dde553ad0fdafe748c04dcb75b5084ad2270c5dd5e85f5b9f5

Observation 9268db2d-a89a-4bb6-9779-924540dce770 · inbound

MIRAGE: Protecting against Malicious Image Editing via False Moderation cites this paper.

MIRAGE: Protecting against Malicious Image Editing via False Moderation Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-06-29T19:13:53.523673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-29T04:46:56.552601Z digest=sha256:5bb9463f0eb7d8bad52695ec3fd6aefad190f9642d8186b7694bbf9b08f8b9d2

Observation 56724a41-4eb3-4444-bf07-4892ab00bc0f · inbound

XPlainVerse: A Million-Scale Benchmark for Explainable Deepfake Detection cites this paper.

XPlainVerse: A Million-Scale Benchmark for Explainable Deepfake Detection Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment

Reference 14

Resolution
unresolved
no resolver link, observed 2026-07-12T01:35:43.979206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T01:35:43.979206Z digest=sha256:ea55d05f5f9175c0b947f88ff68e49a8bdc0133e48ae1401569144d5442676e2

Observation 02808c47-6ced-4729-bbde-4aaa411a6f22 · inbound

On Adversarial Vulnerability of Vision-Language Models through the Lens of Intermediate Spectral Subspaces cites this paper.

On Adversarial Vulnerability of Vision-Language Models through the Lens of Intermediate Spectral Subspaces Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment

Reference 37

Resolution
verified exact
local_arxiv, observed 2026-07-09T12:56:14.864124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-09T12:55:34.631248Z digest=sha256:c85eb18e8ef053c59257a513654fd7ab269c50130f6ae4637c92aae9ff1ebaa8