From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models | Paper Citation Record

Observation e35ecabd-0d1e-4c7b-9611-f606becda309 · outbound

This paper cites Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:32.447358Z

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Observation c1d163ed-ece8-4eee-8c4a-5f1de2657dcd · outbound

This paper cites "Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models "Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:32.513188Z

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Observation 6b6dafde-1e18-4ff5-a51d-c0f0326916f1 · outbound

This paper cites Defending chatgpt against jailbreak attack via self-reminders.Nature Machine Intelligence, 5(12):1486–1496, 2023.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Defending chatgpt against jailbreak attack via self-reminders.Nature Machine Intelligence, 5(12):1486–1496, 2023

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:32.562775Z

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Observation ab5dc5e0-dbe8-408b-ad5b-74309992baaa · outbound

This paper cites Visual instruction tuning.Advances in neural information processing systems, 36, 2024.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Visual instruction tuning.Advances in neural information processing systems, 36, 2024

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:32.729370Z

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Observation 7d16e534-ae4a-4e03-a9e5-8de253fa91a5 · outbound

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

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:32.878990Z

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Observation 228d84c1-4589-483e-83c3-3e103d475d26 · outbound

This paper cites Opera: Alleviating hallucination in multi-modal large language models via over-trust penalty and retrospection-allocation.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Opera: Alleviating hallucination in multi-modal large language models via over-trust penalty and retrospection-allocation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:33.055462Z

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Observation 4c6fcf8d-c3d7-485e-b0e3-0974b692ccc8 · outbound

This paper cites Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:33.183946Z

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Observation 7ca8e42f-9646-4f40-b65c-8ee8cde55c58 · outbound

This paper cites Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:33.321878Z

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Observation bf596b9f-910d-46d3-b4a9-aa9a91c2ae6b · outbound

This paper cites The Internal State of an LLM Knows When It's Lying.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models The Internal State of an LLM Knows When It's Lying

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:33.468297Z

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Observation 8e9f5913-57a1-4726-9a22-4937b27042c4 · outbound

This paper cites Llm factoscope: Uncovering llms’ factual discernment through measuring inner states.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Llm factoscope: Uncovering llms’ factual discernment through measuring inner states

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:34:39.576187Z

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.

Observation 0bf08dfa-e27a-450f-a48a-f511d3f98adb · outbound

This paper cites Detecting Hallucinations in Large Language Model Generation: A Token Probability Approach.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Detecting Hallucinations in Large Language Model Generation: A Token Probability Approach

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:33.685570Z

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Observation 99ffbb33-564d-47a1-b37d-2b06830a0d9e · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:33.845038Z

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Observation 25e2e540-3aba-44b3-850c-3469a159a4b6 · outbound

This paper cites AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:34.003149Z

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Observation a0cad85c-55d5-4f37-9815-ac974c527827 · outbound

This paper cites Guard: Role-playing to generate natural-language jailbreakings to test guideline adherence of large language models.arXiv preprint arXiv:2402.03299, 2024.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Guard: Role-playing to generate natural-language jailbreakings to test guideline adherence of large language models.arXiv preprint arXiv:2402.03299, 2024

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:34.167588Z

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Observation 79c19ef7-639f-4dca-99db-c8d3a743b0e5 · outbound

This paper cites Jailbreaking Large Language Models Against Moderation Guardrails via Cipher Characters.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Jailbreaking Large Language Models Against Moderation Guardrails via Cipher Characters

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:34.241608Z

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Observation 29787c52-bcc5-40eb-8475-36a31bd4a65a · outbound

This paper cites Jailbreaking Black Box Large Language Models in Twenty Queries.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:34.347192Z

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Observation 06467daa-7a79-41c9-a2a9-30d088d57aaa · outbound

This paper cites Jailbreakzoo: Survey, landscapes, and horizons in jailbreaking large language and vision-language models.arXiv preprint arXiv:2407.01599, 2024.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Jailbreakzoo: Survey, landscapes, and horizons in jailbreaking large language and vision-language models.arXiv preprint arXiv:2407.01599, 2024

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:34.434538Z

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Observation b71f90d3-ed83-411a-953f-8f89b9ec73c1 · outbound

This paper cites Multi-step Jailbreaking Privacy Attacks on ChatGPT.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Multi-step Jailbreaking Privacy Attacks on ChatGPT

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:34.521629Z

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Observation 75a00167-735f-4d5b-84bf-38140ee83424 · outbound

This paper cites In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:34.621558Z

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Observation 47e5f63f-0824-4a7c-a89a-75e1252df0cb · outbound

This paper cites Jailbroken: How Does LLM Safety Training Fail?.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Jailbroken: How Does LLM Safety Training Fail?

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:34.714953Z

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Observation cc291607-87bc-4601-8fff-56431417bdf9 · outbound

This paper cites Query-Based Adversarial Prompt Generation.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Query-Based Adversarial Prompt Generation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:34.809699Z

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Observation b24cd802-df9b-4580-8ccb-2f148221cb3a · outbound

This paper cites CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:34.930392Z

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Observation bdcec99b-775e-4fe4-8083-8ac83e1925fc · outbound

This paper cites DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:35.024627Z

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Observation 25ce4ff6-5ffe-4b86-864f-aa98df872c3b · outbound

This paper cites GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:35.156845Z

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Observation c52ceef5-c819-4a08-b794-1da06089cc8c · outbound

This paper cites Jailbreaking proprietary large language models using word substitution cipher.arXiv preprint arXiv:2402.10601, 2024.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Jailbreaking proprietary large language models using word substitution cipher.arXiv preprint arXiv:2402.10601, 2024

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:35.268065Z

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Observation 308a610b-bc2e-431e-b044-508bc991be46 · outbound

This paper cites Are aligned neural networks adversarially aligned?Advances in Neural Information Processing Systems, 36, 2024.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Are aligned neural networks adversarially aligned?Advances in Neural Information Processing Systems, 36, 2024

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:35.415641Z

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Observation a9546250-14fd-4632-86b1-3034e8ad0d68 · outbound

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

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models On Evaluating Adversarial Robustness of Large Vision-Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:35.513830Z

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Observation cfa67e39-094b-494c-8967-ee361b543bc3 · outbound

This paper cites Visual Adversarial Examples Jailbreak Aligned Large Language Models.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Visual Adversarial Examples Jailbreak Aligned Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:35.624649Z

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Observation 75ed4a4d-394e-4782-845e-8d1011d2d211 · outbound

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

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models On the adversarial robustness of multi-modal founda- tion models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:34:39.409736Z

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Observation 4fbd0ab7-905c-4e47-86f0-224db5bb024e · outbound

This paper cites JailBreakV: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models JailBreakV: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:35.811622Z

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Observation 8d5d5faa-34ed-4891-aed0-77149d82e322 · outbound

This paper cites InternalInspector $I^2$: Robust Confidence Estimation in LLMs through Internal States.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models InternalInspector $I^2$: Robust Confidence Estimation in LLMs through Internal States

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:35.908111Z

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Observation 88b0f8bd-44d8-4046-9f84-f6627fb9abde · outbound

This paper cites Do LLMs Know about Hallucination? An Empirical Investigation of LLM's Hidden States.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Do LLMs Know about Hallucination? An Empirical Investigation of LLM's Hidden States

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:35.999122Z

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Observation c1cd4eaf-7b93-47b5-9414-62d703309478 · outbound

This paper cites Adaptive Activation Steering: A Tuning-Free LLM Truthfulness Improvement Method for Diverse Hallucinations Categories.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Adaptive Activation Steering: A Tuning-Free LLM Truthfulness Improvement Method for Diverse Hallucinations Categories

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:36.079992Z

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Observation 3f94b444-3932-4159-a2b9-fb24652fb22b · outbound

This paper cites INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:36.185423Z

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Observation 3baef64a-4f67-42b3-a59d-ec7f2c243386 · outbound

This paper cites Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:36.282420Z

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Observation 0fde98d1-3178-4553-82be-f66021aa2fbc · outbound

This paper cites LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:36.391398Z

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Observation 91ee52b9-20e9-4240-a91b-7ce7c1e36166 · outbound

This paper cites Are sixteen heads really better than one? Advances in neural information processing systems, 32, 2019.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Are sixteen heads really better than one? Advances in neural information processing systems, 32, 2019

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:36.498025Z

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Observation 38e9fd75-f90e-4821-880c-ac10222c7841 · outbound

This paper cites What Does BERT Look At? An Analysis of BERT's Attention.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models What Does BERT Look At? An Analysis of BERT's Attention

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:36.609925Z

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Observation 0b2ecfab-c7d8-4841-aad9-c93d781795bd · outbound

This paper cites Improved baselines with visual instruction tuning.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Improved baselines with visual instruction tuning

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:36.681348Z

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Observation 85533a89-f4f5-42e4-a519-bbd9dd3cbd31 · outbound

This paper cites AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:36.773583Z

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Observation 94a69ac0-6493-44e9-9945-eece68ad9f8d · outbound

This paper cites AdaShield: Safeguarding Multimodal Large Language Models from Structure-based Attack via Adaptive Shield Prompting.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models AdaShield: Safeguarding Multimodal Large Language Models from Structure-based Attack via Adaptive Shield Prompting

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:36.872796Z

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Unavailable: canonical work link unavailable.

Observation 9337c7f5-1e48-4495-81cd-ac7b7dc8f23f · outbound

This paper cites Mitigating object hallucinations in large vision-language models through visual contrastive decoding.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Mitigating object hallucinations in large vision-language models through visual contrastive decoding

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:36.950087Z

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Observation 30cdca5a-afda-4c9b-a8a7-42cc3f014507 · outbound

This paper cites Safebench: A benchmarking platform for safety evaluation of autonomous vehicles.Advances in Neural Information Processing Systems, 35:25667–25682, 2022.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Safebench: A benchmarking platform for safety evaluation of autonomous vehicles.Advances in Neural Information Processing Systems, 35:25667–25682, 2022

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:34:39.232440Z

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.

Observation 197b9f6a-0da3-4cf5-a240-b7adcd798e32 · outbound

This paper cites FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual Prompts.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual Prompts

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:37.104583Z

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Observation 6db2aa4b-4670-4fc3-9425-f3fce365f5cb · outbound

This paper cites Jailbreak Large Vision-Language Models Through Multi-Modal Linkage.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Jailbreak Large Vision-Language Models Through Multi-Modal Linkage

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T12:34:37.173951Z

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Observation 02a122a8-9246-4bef-8e66-453709d59ad7 · outbound

This paper cites If detected, immediately stop processing the instruction.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models If detected, immediately stop processing the instruction

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:34:39.057567Z

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

Observation d3afef16-ddb8-443a-81ab-4a4ebfc164fe · outbound

This paper cites an unresolved cited work.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:34:38.841755Z

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

Observation 53ba6730-2b5e-4422-8c0f-47914173ae80 · outbound

This paper cites I am sorry.

From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models I am sorry

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:34:38.663788Z

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.

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