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Paper Citation Record · LEDGER

Global Challenge for Safe and Secure LLMs Track 1

As of 15 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 4 inbound Pith citation observations for arXiv:2411.14502.

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

pith.paper-citation-record.v1
2411.14502 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:48:29.710195Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:17:02.653518Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T01:15:50.548762Z

Reference resolution

23 of 23 outbound references displayed

  • verified exact0
  • verified fuzzy3
  • unresolved20
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f63be505-e68d-4f99-aa51-5aec998863d4 · outbound

This paper cites Ai index report 2023, 2023.

Global Challenge for Safe and Secure LLMs Track 1 Ai index report 2023, 2023

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:48:30.093047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:48:29.429397Z digest=sha256:c9a1230809790798100b6f19d2ef4827c6eb6dff05c44442624e2a2160e5b174

Observation f6298422-f9f5-40a9-9eb0-4a8552136e1f · outbound

This paper cites Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks.

Global Challenge for Safe and Secure LLMs Track 1 Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks

Reference 4

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no resolver link, observed 2026-08-12T15:48:29.441895Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T15:48:29.441895Z digest=sha256:e72a3afb1eaecec41e306f9778319262791bc794ecdcd8411b84f3f59729cd60

Observation d6761485-a528-4ee7-af19-2eff497a9c74 · outbound

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

Global Challenge for Safe and Secure LLMs Track 1 Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 5

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no resolver link, observed 2026-08-12T15:48:29.444951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:48:29.444951Z digest=sha256:2ff6cbffe89242cdf7117f71945143c52f3ac2520d612e83229ab280fa8125c5

Observation 40295038-e2da-45be-adb9-fb2755df9f96 · outbound

This paper cites Pappas, and Eric Wong.

Global Challenge for Safe and Secure LLMs Track 1 Pappas, and Eric Wong

Reference 6

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no resolver link, observed 2026-08-12T15:48:29.448601Z

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source=pdf_text observed=2026-08-12T15:48:29.448601Z digest=sha256:faa4fc4715d42aba146aaf6370259714165fcf21f2ea8beff7a5a9114a149030

Observation fc7f01bc-1a5c-450a-86fa-51474d248c7d · outbound

This paper cites Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?.

Global Challenge for Safe and Secure LLMs Track 1 Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?

Reference 7

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unresolved
no resolver link, observed 2026-08-12T15:48:29.452721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:48:29.452721Z digest=sha256:820ab1f08c617b87b1318863bc00d582b474cc66a723df766823bea57f1da918

Observation 5a4595ad-a6d7-40a2-8008-6c9dab346879 · outbound

This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.

Global Challenge for Safe and Secure LLMs Track 1 Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality

Reference 8

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no resolver link, observed 2026-08-12T15:48:29.456106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:48:29.456106Z digest=sha256:4cf3bf15d8b4c1e53c9cc3a5c7e3b57de7876988ce0b1ec2f8a5a2ecc2e3aac4

Observation d23795c1-04fa-4676-a70e-983ba50a9ccf · outbound

This paper cites A Survey on Responsible Generative AI: What to Generate and What Not.

Global Challenge for Safe and Secure LLMs Track 1 A Survey on Responsible Generative AI: What to Generate and What Not

Reference 9

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no resolver link, observed 2026-08-12T15:48:29.459417Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T15:48:29.459417Z digest=sha256:1445e79bfe4f206ea973fa322816d0f627d65275763f30be974ca58fecc4c765

Observation 87431442-4a21-45d2-975b-0dea9be7aa07 · outbound

This paper cites Improved Techniques for Optimization-Based Jailbreaking on Large Language Models.

Global Challenge for Safe and Secure LLMs Track 1 Improved Techniques for Optimization-Based Jailbreaking on Large Language Models

Reference 10

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unresolved
no resolver link, observed 2026-08-12T15:48:29.463003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:48:29.463003Z digest=sha256:5019f23632b822acb4c00434143a512dcb694c4b81e010435c0dcdb0c6a3659f

Observation d6b6422a-d018-431a-8ecf-6e622c9a9a45 · outbound

This paper cites Open Sesame! Universal Black Box Jailbreaking of Large Language Models.

Global Challenge for Safe and Secure LLMs Track 1 Open Sesame! Universal Black Box Jailbreaking of Large Language Models

Reference 11

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unresolved
no resolver link, observed 2026-08-12T15:48:29.466781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:48:29.466781Z digest=sha256:608fa3a4820a45027e0a18128f746f0568f4d96445d586cff9913d187fc58398

Observation fd12878c-f058-40b3-a4a4-e187b87f76ec · outbound

This paper cites Making them ask and answer: Jailbreaking large language models in few queries via disguise and reconstruction.

Global Challenge for Safe and Secure LLMs Track 1 Making them ask and answer: Jailbreaking large language models in few queries via disguise and reconstruction

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:48:30.037766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:48:29.498264Z digest=sha256:bbd5768bae01f96c2842c1771b9611f0d49c969978911be19767a1affd1b8fcb

Observation e2af0aaf-5d1e-47bd-bf73-2b406677f123 · outbound

This paper cites Prompt Injection attack against LLM-integrated Applications.

Global Challenge for Safe and Secure LLMs Track 1 Prompt Injection attack against LLM-integrated Applications

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-12T15:48:29.527069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:48:29.527069Z digest=sha256:973aed40fa04a49671898f556016857adc769e864c51b155541adff889f8aeba

Observation 969fc0b9-f443-4445-b101-19f818c3e304 · outbound

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

Global Challenge for Safe and Secure LLMs Track 1 Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study

Reference 14

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no resolver link, observed 2026-08-12T15:48:29.591423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:48:29.591423Z digest=sha256:a82ad1c54869de679c88402c52a42e1b7a706458cd64019e7511ed4b6410339b

Observation ab945e37-1c99-4183-8dbf-6f1383f65749 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Global Challenge for Safe and Secure LLMs Track 1 Gemma: Open Models Based on Gemini Research and Technology

Reference 15

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unresolved
no resolver link, observed 2026-08-12T15:48:29.627277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:48:29.627277Z digest=sha256:2f36544635c9a50a35a5227104237d4c2dc4ec23fb2745587699a8c378c8282b

Observation 1c9ba328-8865-4999-95c1-5ac51ad06f92 · outbound

This paper cites Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks.

Global Challenge for Safe and Secure LLMs Track 1 Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks

Reference 16

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unresolved
no resolver link, observed 2026-08-12T15:48:29.649627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:48:29.649627Z digest=sha256:b67186e208cebfd8ccf334eb0feaa4d6d63a8f37635e4d4238dcdaf27cb2bbd2

Observation 7509a7c5-31ed-4034-a2c7-0bd194fc7fa7 · outbound

This paper cites Global AI challenge on security and safety.

Global Challenge for Safe and Secure LLMs Track 1 Global AI challenge on security and safety

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:48:29.940167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T15:48:29.659573Z digest=sha256:a45df10e44323b8bd12b742b0458155509720e425c7336edb0a7b7b4681e90c0

Observation e47721ba-a3a9-4bda-a280-4d68ec650ab0 · outbound

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

Global Challenge for Safe and Secure LLMs Track 1 Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 18

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no resolver link, observed 2026-08-12T15:48:29.669065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:48:29.669065Z digest=sha256:0e6fa172aacad613c53d83a37555ad7bf5fa54966899880647ca5bb4c8e290e9

Observation 4402516a-7e05-4246-9984-b620aea73ce3 · outbound

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

Global Challenge for Safe and Secure LLMs Track 1 Jailbroken: How Does LLM Safety Training Fail?

Reference 19

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no resolver link, observed 2026-08-12T15:48:29.682695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:48:29.682695Z digest=sha256:1ad4a1600038eaee803215e2e200beafe3731b436289f0a58f4146a1df742b7e

Observation a165a6eb-26a5-4324-a1a3-21434dc0a498 · outbound

This paper cites Jailbroken: How does llm safety training fail? Advances in Neural Information Processing Systems, 36, 2024.

Global Challenge for Safe and Secure LLMs Track 1 Jailbroken: How does llm safety training fail? Advances in Neural Information Processing Systems, 36, 2024

Reference 20

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no resolver link, observed 2026-08-12T15:48:29.690386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:48:29.690386Z digest=sha256:96ecc7626428c30d0e4320bc57db4eb8f4fd7be3741a83fc28722ad8973a8d72

Observation a06d26ba-9b91-40bd-b4d4-c1a734a71509 · outbound

This paper cites SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decoding.

Global Challenge for Safe and Secure LLMs Track 1 SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decoding

Reference 21

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no resolver link, observed 2026-08-12T15:48:29.693230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:48:29.693230Z digest=sha256:9a89e06431eb86522e2dd20d61d88727af69bc4b3d0ed643dbc0bb2dee377fdc

Observation c0b8841c-8554-4605-bb1b-e07387094599 · outbound

This paper cites Jailbreak attacks and defenses against large language models: A survey, 2024.

Global Challenge for Safe and Secure LLMs Track 1 Jailbreak attacks and defenses against large language models: A survey, 2024

Reference 22

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no resolver link, observed 2026-08-12T15:48:29.696265Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T15:48:29.696265Z digest=sha256:f983db9cffd0a2018deaf7c10d4c3031fa87561319d773d7295cd61472ece729

Observation 78708a62-0cfa-4d54-b0c8-91f6714eae4c · outbound

This paper cites Mind the Inconspicuous: Revealing the Hidden Weakness in Aligned LLMs' Refusal Boundaries.

Global Challenge for Safe and Secure LLMs Track 1 Mind the Inconspicuous: Revealing the Hidden Weakness in Aligned LLMs' Refusal Boundaries

Reference 23

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unresolved
no resolver link, observed 2026-08-12T15:48:29.699922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:48:29.699922Z digest=sha256:709c8fdbdb15b84317b9f7f6ffc69b6de31ac3516d0d1d44657ffd51f288e14c

Observation 90ebb1e2-c614-4160-8b5a-3f5af51bf83a · outbound

This paper cites Zico Kolter, and Matt Fredrikson.

Global Challenge for Safe and Secure LLMs Track 1 Zico Kolter, and Matt Fredrikson

Reference 25

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no resolver link, observed 2026-08-12T15:48:29.706980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:48:29.706980Z digest=sha256:38c0ffd8d786f872d17c20433e5741b33ef2e3360ebdd86dd13874fcfcc52e48

Observation ad5d29ba-f4d9-483f-bf6a-51dec8dbe32f · outbound

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

Global Challenge for Safe and Secure LLMs Track 1 Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 26

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source=pdf_text observed=2026-08-12T15:48:29.710195Z digest=sha256:595d901cddd8daadcfd02606f27d616cc02da1376616078e1b8dc0717ad2be57

Pith citing papers

Observation a2413ef9-7a6b-4f30-9094-2e76f83ea0cd · inbound

Heuristic-Induced Multimodal Risk Distribution Jailbreak Attack for Multimodal Large Language Models cites this paper.

Heuristic-Induced Multimodal Risk Distribution Jailbreak Attack for Multimodal Large Language Models Global Challenge for Safe and Secure LLMs Track 1

Reference 22

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no resolver link, observed 2026-08-11T20:17:02.653518Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:17:02.653518Z digest=sha256:e55a3132372cc1f9fe49b2102755f562fddc6f3ab9223272ce0196e78e550600

Observation 755c308a-0d01-4d06-baf0-3b73067290d0 · inbound

Safety Alignment Should Be Made More Than Just A Few Attention Heads cites this paper.

Safety Alignment Should Be Made More Than Just A Few Attention Heads Global Challenge for Safe and Secure LLMs Track 1

Reference 17

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no resolver link, observed 2026-08-05T15:37:43.435885Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T15:37:43.435885Z digest=sha256:d7e029e7c96016857f8c1c396bd7cbd84c630f170843573cef01daa880c24e37

Observation 32456f1d-e599-4e84-84d4-5c2c14f31ba9 · inbound

Trustworthy AI LLM Scalability Risk Index (LSRI): A Cybersecurity Framework Assessing Agentic-AI Security & Software Model Supply Chain Safety Boosting AI-Generated Malware Defense & Explainability Mitigating Emerging Risks of Generative AI cites this paper.

Trustworthy AI LLM Scalability Risk Index (LSRI): A Cybersecurity Framework Assessing Agentic-AI Security & Software Model Supply Chain Safety Boosting AI-Generated Malware Defense & Explainability Mitigating Emerging Risks of Generative AI Global Challenge for Safe and Secure LLMs Track 1

Reference 6

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no resolver link, observed 2026-08-02T21:50:14.388727Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T21:50:14.388727Z digest=sha256:7146803cbba5c7ed08d4a41d5ed14437232f824ab74e317d33691e56b3d6a532

Observation cc96cacd-8a8a-4b7b-b2f9-c94861101317 · inbound

Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies cites this paper.

Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies Global Challenge for Safe and Secure LLMs Track 1

Reference 20

Resolution
verified exact
local_arxiv, observed 2026-07-09T01:15:50.549889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-07-09T01:11:36.944815Z digest=sha256:bcd7443d46f2dc8f0ead4b816aadc9224d8d8948fed50a0faab4c90aa0c5cdf5