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Paper Citation Record · LEDGER

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses

As of 15 August 2026, this Paper Citation Record lists 100 of 255 outbound references and 3 inbound Pith citation observations for arXiv:2510.15476.

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

pith.paper-citation-record.v1
2510.15476 v3

Coverage vector

measured 100 of 255 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T09:25:45.961893Z

measured 103 of 103 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:32:02.668360Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:00:07.826531Z

Reference resolution

100 of 255 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved99
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

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Outbound references

Observation 9b9a8f4d-0a44-4fef-974e-f8169200dfc4 · outbound

This paper cites glaive-code-assistant, 2023.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses glaive-code-assistant, 2023

Reference 1

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source=pdf_text observed=2026-08-04T09:25:35.805221Z digest=sha256:17aa97b5fed463038f7298defd9cdff4b97fa16e965c74dec46dae315b68f268

Observation baf3582e-15f8-4574-8754-8951849bde68 · outbound

This paper cites Detecting Language Model Attacks with Perplexity.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Detecting Language Model Attacks with Perplexity

Reference 2

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Observation 8b077a8a-59d7-4555-b6f9-9457827ace9d · outbound

This paper cites Are PPO-ed Language Models Hackable?.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Are PPO-ed Language Models Hackable?

Reference 3

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Observation a359a567-a5ea-48b8-bab4-853163c268df · outbound

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

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks

Reference 4

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source=pdf_text observed=2026-08-04T09:25:35.977601Z digest=sha256:041713095b0f2283834e967acced879d300e127447d33114a7065b58e0c5be97

Observation ed84495c-ce25-4a75-8c76-f0d6ba08505b · outbound

This paper cites Jailbreaking leading safety-aligned llms with simple adaptive attacks.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Jailbreaking leading safety-aligned llms with simple adaptive attacks

Reference 5

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source=pdf_text observed=2026-08-04T09:25:36.047680Z digest=sha256:8bd25972527f60dda2706e41ab2ed46085853cd6704947afc7bb39aa930048fc

Observation 0fea0ec6-61d4-4d00-aa42-eef9b3cf69bf · outbound

This paper cites Does Refusal Training in LLMs Generalize to the Past Tense?.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Does Refusal Training in LLMs Generalize to the Past Tense?

Reference 6

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source=pdf_text observed=2026-08-04T09:25:36.084546Z digest=sha256:32424a9d7e56e02f2a73c56b9e202189e3e1a21b3f7f71a544308a2d0613251c

Observation ec056e70-a0bf-40d1-87a5-ffe022dfc284 · outbound

This paper cites Does refusal training in llms generalize to the past tense? InThe Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Does refusal training in llms generalize to the past tense? InThe Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025

Reference 7

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source=pdf_text observed=2026-08-04T09:25:36.213690Z digest=sha256:86fb30e55c32992c6aaae80a7c38acc2d5f8eff728cd6862776654826f5d7872

Observation 5a7de3c8-ddd0-42b6-9c48-785eba686cbe · outbound

This paper cites Bowman, Ethan Perez, Roger B.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Bowman, Ethan Perez, Roger B

Reference 8

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source=pdf_text observed=2026-08-04T09:25:36.303948Z digest=sha256:02a947d0081bc93a3d27612e5bb22b7ed9d23ed6aed8280614317147229785aa

Observation 9f92f486-5722-4298-9119-79da1760dfc2 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Gemini: A Family of Highly Capable Multimodal Models

Reference 9

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source=pdf_text observed=2026-08-04T09:25:36.428626Z digest=sha256:dc3121e1fcc5c009d8abe2d031a2db46743e050f6dcd933831f8ad1be285526e

Observation d3aba117-b1ff-40dd-921b-6140cac603f1 · outbound

This paper cites Surro- gateprompt: Bypassing the safety filter of text-to-image models via substitution.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Surro- gateprompt: Bypassing the safety filter of text-to-image models via substitution

Reference 10

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Observation 7739b3f9-3994-46b0-826b-960823cce346 · outbound

This paper cites Qwen Technical Report.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Qwen Technical Report

Reference 11

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source=pdf_text observed=2026-08-04T09:25:36.554045Z digest=sha256:abf61da3e5a8ded34155b1b08bc0365f5c8169dde42fe8d622d28a35f54562a2

Observation b346aeeb-be63-4fe1-9478-41fe118ee20b · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 12

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source=pdf_text observed=2026-08-04T09:25:36.686698Z digest=sha256:e2007f23af56a44ed4ae8ac96459d1e4063d29447dd861c8f91cf06f619719ad

Observation a633e9ce-f650-46b3-bd21-9df0549dcbef · outbound

This paper cites How (un)ethical are instruction-centric responses of LLMs? Unveiling the vulnerabilities of safety guardrails to harmful queries.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses How (un)ethical are instruction-centric responses of LLMs? Unveiling the vulnerabilities of safety guardrails to harmful queries

Reference 13

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source=pdf_text observed=2026-08-04T09:25:36.791950Z digest=sha256:d88de531dd4b288f3c2174fe07c4367cc260ffb4e66d67ed9c598a63977c02ff

Observation e41f6a8a-e9f9-4042-9641-5bcc6b2df7a1 · outbound

This paper cites Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment

Reference 14

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source=pdf_text observed=2026-08-04T09:25:36.898264Z digest=sha256:60b7d8add6aacd674158d33c31bd523f0d5676dc1b4777033c22578e818647c7

Observation e3018bdf-9640-480a-8863-347328d5ded5 · outbound

This paper cites CLARE: Cognitive Load Assessment in REaltime with Multimodal Data.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses CLARE: Cognitive Load Assessment in REaltime with Multimodal Data

Reference 15

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source=pdf_text observed=2026-08-04T09:25:36.977659Z digest=sha256:1e78e5c461c73b74e5ba82829a08c1b1544021259a15b2a3d015b207a3fb1ef1

Observation e6386057-4902-4bd3-8347-511b75b75692 · outbound

This paper cites Safety-tuned llamas: Lessons from improving the safety of large language models that follow instructions.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Safety-tuned llamas: Lessons from improving the safety of large language models that follow instructions

Reference 16

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Observation 9714b2ec-69f2-40db-9f43-a930fd320da8 · outbound

This paper cites an unresolved cited work.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Unresolved cited work

Reference 17

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Observation 34530978-c609-4bd9-a876-bac5191efeda · outbound

This paper cites Camelai domain expert datasets (physics, math, chemistry & biology), 2023.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Camelai domain expert datasets (physics, math, chemistry & biology), 2023

Reference 18

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source=pdf_text observed=2026-08-04T09:25:37.321962Z digest=sha256:1e6cafd894f567e77552427836e880091d9cc4ad9d899dc4d8aa11a8f39c9cef

Observation 0596d4c6-4e43-4580-9e75-31ef44c45463 · outbound

This paper cites Abu-Ghazaleh, M.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Abu-Ghazaleh, M

Reference 19

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source=pdf_text observed=2026-08-04T09:25:37.468338Z digest=sha256:036f2b0da2cba901debedcb766583c98268c4d71c54a786952c7aba9f387de4a

Observation 049cd096-529f-41f3-a266-a0ea331e1ad1 · outbound

This paper cites Synneure: Intelligent human-machine teamwork in virtual space.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Synneure: Intelligent human-machine teamwork in virtual space

Reference 20

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source=pdf_text observed=2026-08-04T09:25:37.530953Z digest=sha256:33138df5820570beb2585418dec4eb47bd4229ea76f7f2b4f45c15e3b7df945c

Observation 12a379a3-78ae-46e8-95f9-842f97684580 · outbound

This paper cites Pappas, Florian Tram` er, Hamed Hassani, and Eric Wong.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Pappas, Florian Tram` er, Hamed Hassani, and Eric Wong

Reference 21

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source=pdf_text observed=2026-08-04T09:25:37.594081Z digest=sha256:e89cd15b62f87e0d544b281d5b2d682527cf32999d71ca4e842b5b124882c1a2

Observation 77568c13-4dc4-4203-bf10-059230c62010 · outbound

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

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 22

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source=pdf_text observed=2026-08-04T09:25:37.655290Z digest=sha256:4f34c4cc2b044d1fbd2ddf9678502d5758b4ab1fc6d951e7b8671a810bdf5056

Observation 30779e11-7784-4282-91b0-1954946d0ce4 · outbound

This paper cites Pappas, and Eric Wong.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Pappas, and Eric Wong

Reference 23

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source=pdf_text observed=2026-08-04T09:25:37.745718Z digest=sha256:fb692d76a72c1aa433d65aff8752d6e39b517c1c826e60ce60c5c4654aa19a16

Observation 32e7f717-16b9-4033-a048-8ed85123dc67 · outbound

This paper cites StruQ: Defending Against Prompt Injection with Structured Queries.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses StruQ: Defending Against Prompt Injection with Structured Queries

Reference 24

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source=pdf_text observed=2026-08-04T09:25:37.843859Z digest=sha256:68b618b7c58c6aeeb8dbe6c46f0c38049ac66ae5520cabcc33115f09c87fafed

Observation 431632c8-01bb-403c-b47c-a9451766b5ef · outbound

This paper cites When LLM meets DRL: advancing jailbreaking efficiency via drl-guided search.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses When LLM meets DRL: advancing jailbreaking efficiency via drl-guided search

Reference 25

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source=pdf_text observed=2026-08-04T09:25:37.955176Z digest=sha256:e07a8b8d387e95d2e7f40f00be019eb9a19067003a8132afaf2f7b521efe938a

Observation 83850c27-a627-4be6-86ff-bd3abf98e28d · outbound

This paper cites RL-JACK: Reinforcement Learning-powered Black-box Jailbreaking Attack against LLMs.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses RL-JACK: Reinforcement Learning-powered Black-box Jailbreaking Attack against LLMs

Reference 26

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source=pdf_text observed=2026-08-04T09:25:38.012962Z digest=sha256:e26f8c5926f422766316e2238bff1690f0f7ec8411db0f3a1f27aeb9ec8946d7

Observation 50577956-3357-40d7-a052-48ca48250d2a · outbound

This paper cites Leveraging the Context through Multi-Round Interactions for Jailbreaking Attacks.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Leveraging the Context through Multi-Round Interactions for Jailbreaking Attacks

Reference 27

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source=pdf_text observed=2026-08-04T09:25:38.087372Z digest=sha256:b8cf9ab522d235817099103fe4626efe54209338f32f9dafbfe5c72f3fe63609

Observation 3b6fa656-4f8b-4db8-b93a-60076a35138b · outbound

This paper cites Prompt Injection Attacks on Large Language Models in Oncology.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Prompt Injection Attacks on Large Language Models in Oncology

Reference 28

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source=pdf_text observed=2026-08-04T09:25:38.152499Z digest=sha256:09636cfb87dd45ee91e46db95ee209c8d22533f893daaa2914e0296d4861ecae

Observation bedcacc9-ef63-49fd-9418-127f1168ef4c · outbound

This paper cites Cogstack, 2023.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Cogstack, 2023

Reference 29

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source=pdf_text observed=2026-08-04T09:25:38.235834Z digest=sha256:bd666f78ae05024c14c1b3d6206ab8bbe6cca10a74a052049b8fef879df5b2cb

Observation 93f9ec51-6210-42a8-a429-997c188f1592 · outbound

This paper cites A Jailbroken GenAI Model Can Cause Substantial Harm: GenAI-powered Applications are Vulnerable to PromptWares.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses A Jailbroken GenAI Model Can Cause Substantial Harm: GenAI-powered Applications are Vulnerable to PromptWares

Reference 30

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source=pdf_text observed=2026-08-04T09:25:38.278442Z digest=sha256:983cb1e1bfe7dc66ba4646209f95001d8c271b18902bd3743f7cbdfe35c221d8

Observation a1a0e7d8-929c-45da-9d63-741195854150 · outbound

This paper cites MoJE: Mixture of Jailbreak Experts, Naive Tabular Classifiers as Guard for Prompt Attacks.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses MoJE: Mixture of Jailbreak Experts, Naive Tabular Classifiers as Guard for Prompt Attacks

Reference 31

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source=pdf_text observed=2026-08-04T09:25:38.354404Z digest=sha256:15b931aa0021dab9d80a1f48f71ebe169c9449ec4a88602d9003ba879d4db5f7

Observation c45bcb92-58bf-43b9-a57b-83d2ccbcb919 · outbound

This paper cites Moje: Mixture of jailbreak experts, naive tabular classifiers as guard for prompt attacks.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Moje: Mixture of jailbreak experts, naive tabular classifiers as guard for prompt attacks

Reference 32

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source=pdf_text observed=2026-08-04T09:25:38.438918Z digest=sha256:29f9962d1caa05c2dfd34c01ade493fb1d2ca3363aec4117af11cf8e9d49c7a1

Observation ab74301b-214b-4d37-b953-ee73a08f750b · outbound

This paper cites alpaca-gpt4-cot, 2024.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses alpaca-gpt4-cot, 2024

Reference 33

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Observation 0f38120c-10fc-4679-954b-6e5b77f129bb · outbound

This paper cites Dataset and lessons learned from the 2024 satml LLM capture-the-flag competition.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Dataset and lessons learned from the 2024 satml LLM capture-the-flag competition

Reference 34

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source=pdf_text observed=2026-08-04T09:25:38.623405Z digest=sha256:45bea98f4c2d4ad1dc78a6ada5e7ee385b3506783a14935fddf935c8b986cec6

Observation 0a0ab53c-274f-4eea-9840-214d7c8b968b · outbound

This paper cites AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents

Reference 35

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Observation 00863392-c6b6-418d-a1df-f21c64a6756a · outbound

This paper cites Attack prompt generation for red teaming and defending large language models.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Attack prompt generation for red teaming and defending large language models

Reference 36

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Observation 9817fec3-0c68-4669-a3f1-b040e5fcea88 · outbound

This paper cites MasterKey: Automated Jailbreak Across Multiple Large Language Model Chatbots.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses MasterKey: Automated Jailbreak Across Multiple Large Language Model Chatbots

Reference 37

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Observation 5c0115b3-6007-4d3b-a37c-5413d25effe7 · outbound

This paper cites Pandora: Jailbreak GPTs by Retrieval Augmented Generation Poisoning.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Pandora: Jailbreak GPTs by Retrieval Augmented Generation Poisoning

Reference 38

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Observation fe063ec7-405b-46c2-bccd-e3c714248afd · outbound

This paper cites Multilingual jailbreak challenges in large language models.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Multilingual jailbreak challenges in large language models

Reference 39

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Observation 8e4a384a-071d-4ea2-9755-6c5ee37db158 · outbound

This paper cites A wolf in sheep’s clothing: Generalized nested jailbreak prompts can fool large language models easily.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses A wolf in sheep’s clothing: Generalized nested jailbreak prompts can fool large language models easily

Reference 40

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Observation 03e90c74-720b-4f37-b830-b4c646a49900 · outbound

This paper cites Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation Models.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation Models

Reference 41

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Observation e7191af2-caa0-40af-ba24-745f0d852e4f · outbound

This paper cites Harnessing Task Overload for Scalable Jailbreak Attacks on Large Language Models.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Harnessing Task Overload for Scalable Jailbreak Attacks on Large Language Models

Reference 42

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Observation a7645fa9-ea0d-47a0-a6de-365ab87a2eb1 · outbound

This paper cites BELLS: A Framework Towards Future Proof Benchmarks for the Evaluation of LLM Safeguards.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses BELLS: A Framework Towards Future Proof Benchmarks for the Evaluation of LLM Safeguards

Reference 43

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Observation 12484ebc-aed0-47c4-b236-83de6b3fcc95 · outbound

This paper cites h4rm3l: A language for Composable Jailbreak Attack Synthesis.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses h4rm3l: A language for Composable Jailbreak Attack Synthesis

Reference 44

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Observation 9f7eb929-631e-42f1-a104-96d669327d06 · outbound

This paper cites Multi-turn jailbreaking large language models via attention shifting.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Multi-turn jailbreaking large language models via attention shifting

Reference 45

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Observation 9594e70f-3e35-4f86-aa52-c6c430098b00 · outbound

This paper cites VLMGuard: Bootstrapping Malicious Prompt Detectors from Unlabeled Vision-Language Prompts in the Wild.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses VLMGuard: Bootstrapping Malicious Prompt Detectors from Unlabeled Vision-Language Prompts in the Wild

Reference 46

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Observation 02103ce5-3b21-4a7d-aa4c-47433d021e50 · outbound

This paper cites Atoxia: Red-teaming Large Language Models with Target Toxic Answers.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Atoxia: Red-teaming Large Language Models with Target Toxic Answers

Reference 47

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Observation e408f8ef-b6a3-48a5-bccf-d2bfce19d96e · outbound

This paper cites Vr-hand-in-hand: Using virtual reality (VR) hand tracking for hand-object data annotation.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Vr-hand-in-hand: Using virtual reality (VR) hand tracking for hand-object data annotation

Reference 48

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Observation ddb40eed-ee9a-48ad-af23-4dd5eaa62854 · outbound

This paper cites A comprehensive survey of attack techniques, implementation, and mitigation strategies in large language models.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses A comprehensive survey of attack techniques, implementation, and mitigation strategies in large language models

Reference 49

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Observation 621c4223-71ea-4e8f-8f65-626e4fd54b5f · outbound

This paper cites Unbridled icarus: A survey of the potential perils of image inputs in multimodal large language model security.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Unbridled icarus: A survey of the potential perils of image inputs in multimodal large language model security

Reference 50

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Observation ff93b40b-3006-4fca-bfc7-8ceef6c257e9 · outbound

This paper cites JailbreakLens: Visual Analysis of Jailbreak Attacks Against Large Language Models.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses JailbreakLens: Visual Analysis of Jailbreak Attacks Against Large Language Models

Reference 51

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Observation 41d41791-47f3-478b-8b93-8b1a43fcb9fe · outbound

This paper cites Safety alignment in NLP tasks: Weakly aligned summarization as an in-context attack.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Safety alignment in NLP tasks: Weakly aligned summarization as an in-context attack

Reference 52

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Observation fc437b38-c7f0-44d9-9a3f-f019cf882af7 · outbound

This paper cites Cross-Task Defense: Instruction-Tuning LLMs for Content Safety.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Cross-Task Defense: Instruction-Tuning LLMs for Content Safety

Reference 53

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Observation 0104abb8-b93e-4a6d-ba73-08658c0f765b · outbound

This paper cites Merging Improves Self-Critique Against Jailbreak Attacks.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Merging Improves Self-Critique Against Jailbreak Attacks

Reference 54

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Observation db601228-2c5c-4aa0-865b-c89d4baef68e · outbound

This paper cites MART: improving LLM safety with multi-round automatic red-teaming.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses MART: improving LLM safety with multi-round automatic red-teaming

Reference 55

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source=pdf_text observed=2026-08-04T09:25:40.543033Z digest=sha256:cd4a9f07f41036ee7e3cd1efacaa847d7c883d69939e221260fa9ce1330fb749

Observation a4c1acff-d1c7-4c9a-8f25-cb5f0c6c7363 · outbound

This paper cites Coercing LLMs to do and reveal (almost) anything.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Coercing LLMs to do and reveal (almost) anything

Reference 56

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Observation dbb98816-ca6a-40f5-96a0-89c93ed6d197 · outbound

This paper cites Attacking Large Language Models with Projected Gradient Descent.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Attacking Large Language Models with Projected Gradient Descent

Reference 57

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Observation c2d13794-9a2b-4399-9469-ee2f79f5efd0 · outbound

This paper cites AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Experts.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Experts

Reference 58

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Observation ea59c527-9285-4805-8af7-78b1f2e8efef · outbound

This paper cites Emerging Vulnerabilities in Frontier Models: Multi-Turn Jailbreak Attacks.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Emerging Vulnerabilities in Frontier Models: Multi-Turn Jailbreak Attacks

Reference 59

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source=pdf_text observed=2026-08-04T09:25:40.938534Z digest=sha256:f4973746c975714e7977d0c19cb54e7bb2988123c3b8047670bdc675344fbb4c

Observation dcd95c5d-ffb9-484a-b7fc-9d929a2e496a · outbound

This paper cites Breach By A Thousand Leaks: Unsafe Information Leakage in `Safe' AI Responses.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Breach By A Thousand Leaks: Unsafe Information Leakage in `Safe' AI Responses

Reference 60

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Observation 7f41054c-378c-4f36-a00b-8c506c3c32b4 · outbound

This paper cites Gradient-based adversarial attacks against text transformers.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Gradient-based adversarial attacks against text transformers

Reference 61

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source=pdf_text observed=2026-08-04T09:25:41.188536Z digest=sha256:5af708f4c4862c280d8553a3ba0a741c60097ec658c5a72a120a667e82e213e2

Observation a4ad2add-d635-41d4-859f-6f32aa0172ad · outbound

This paper cites Cold-attack: Jailbreaking llms with stealthiness and controllability.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Cold-attack: Jailbreaking llms with stealthiness and controllability

Reference 62

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source=pdf_text observed=2026-08-04T09:25:41.328475Z digest=sha256:4c86c8f9381dbf231e5990cc314517c32dfcc0958e8f85dd6f69b0d6f1653bc8

Observation d461019a-a9cc-49b4-bdad-6cf6e54bf68f · outbound

This paper cites From chatgpt to threatgpt: Impact of generative AI in cybersecurity and privacy.IEEE Access, 11:80218–80245, 2023.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses From chatgpt to threatgpt: Impact of generative AI in cybersecurity and privacy.IEEE Access, 11:80218–80245, 2023

Reference 63

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source=pdf_text observed=2026-08-04T09:25:41.550062Z digest=sha256:93644ecf51d4f12a45ad0dcfd5d5d642d8b057f7ab50992630ecf6c4eef90eb5

Observation 3301909c-d3bb-4f92-aac1-796af4abbf4f · outbound

This paper cites an unresolved cited work.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Unresolved cited work

Reference 64

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source=pdf_text observed=2026-08-04T09:25:41.479378Z digest=sha256:b0250ec997535f705ed4e55c4c55f168c671d21a41f8f5490f2a1a83264466e6

Observation 42915daa-e987-4308-a05b-cfa38d400ce0 · outbound

This paper cites Gajera, and Chitta Baral.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Gajera, and Chitta Baral

Reference 65

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source=pdf_text observed=2026-08-04T09:25:41.823447Z digest=sha256:bb9974c97dae7f1c5523a12350e7ad84c242caad55be2814ff4035e3b970f05d

Observation 6791c844-f679-405d-9538-5d0b15001a93 · outbound

This paper cites Wildguard: Open one-stop moderation tools for safety risks, jailbreaks, and refusals of llms.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Wildguard: Open one-stop moderation tools for safety risks, jailbreaks, and refusals of llms

Reference 66

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source=pdf_text observed=2026-08-04T09:25:41.701831Z digest=sha256:8816a76615990efbe892145f4f3882bdf1605cd5fd6d25b1e48b971a478e828a

Observation 4d9b2e1d-9a45-4dac-b6bc-f9f8ed73cb27 · outbound

This paper cites Privacy of federated QR decomposition using additive secure multiparty compu- tation.IEEE Trans.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Privacy of federated QR decomposition using additive secure multiparty compu- tation.IEEE Trans

Reference 67

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source=pdf_text observed=2026-08-04T09:25:42.148660Z digest=sha256:6fb27b3b15af14868e19c9b51020eba9e8830e29a0f465746843e2a45e8de331

Observation 43b66e3c-e880-4716-a81e-adb2da2b930e · outbound

This paper cites an unresolved cited work.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Unresolved cited work

Reference 68

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source=pdf_text observed=2026-08-04T09:25:41.985748Z digest=sha256:47d7fdee53ecbbeea1e6f828398bc4b558a806b6c08f88346163bb72abd21005

Observation 3fe12d56-4d6b-427d-8c8b-16655549bc71 · outbound

This paper cites Springer Briefs in Computer Science.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Springer Briefs in Computer Science

Reference 69

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source=pdf_text observed=2026-08-04T09:25:42.429264Z digest=sha256:ae7b5fdc5dfd2c80ea41c51d32e2e4b839559a9fd88b4d7070de651013712a0b

Observation 5cc54660-f3a2-4a99-918c-f20b7b49f2c7 · outbound

This paper cites Query-based adversarial prompt generation.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Query-based adversarial prompt generation

Reference 70

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source=pdf_text observed=2026-08-04T09:25:42.308793Z digest=sha256:ed1c3fd70da293b24c45b14b6bb16fe79e11de2f3217755b9d17bfabff64403d

Observation 0735b77b-8282-401f-8bfe-7b518102fb1a · outbound

This paper cites Gradient cuff: Detecting jailbreak attacks on large language models by exploring refusal loss landscapes.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Gradient cuff: Detecting jailbreak attacks on large language models by exploring refusal loss landscapes

Reference 71

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Observation 455d9866-9f50-4a02-8f64-8bf5c5e63266 · outbound

This paper cites Defending against indirect prompt injection attacks with spotlighting.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Defending against indirect prompt injection attacks with spotlighting

Reference 72

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Observation 9c51da07-703f-4b26-94b6-c2c89dfdf775 · outbound

This paper cites an unresolved cited work.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Unresolved cited work

Reference 73

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source=pdf_text observed=2026-08-04T09:25:42.795114Z digest=sha256:55a3314fc382e241268acde03a38c05f5af5b004f6b4ffea7b1bcc8edbcdfd7c

Observation 16c3402c-6f54-42f2-840b-f2182031128a · outbound

This paper cites SafeAligner: Safety Alignment against Jailbreak Attacks via Response Disparity Guidance.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses SafeAligner: Safety Alignment against Jailbreak Attacks via Response Disparity Guidance

Reference 74

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source=pdf_text observed=2026-08-04T09:25:42.743728Z digest=sha256:a861bd669c263c2e3e198531bbeae1f5bf2d0ad4cf35a2500de3079eb1624af8

Observation 96ae2779-3cc2-4ded-9764-a56c00db3b65 · outbound

This paper cites Summon a Demon and Bind it: A Grounded Theory of LLM Red Teaming.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Summon a Demon and Bind it: A Grounded Theory of LLM Red Teaming

Reference 75

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source=pdf_text observed=2026-08-04T09:25:42.921586Z digest=sha256:2a282a34cd75f53ead147213da3b139ef032256a31b1b1cca7d220718d89f670

Observation 592b08cf-b8fc-47db-84f2-61193b21fecc · outbound

This paper cites Catastrophic jailbreak of open-source llms via exploiting generation.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Catastrophic jailbreak of open-source llms via exploiting generation

Reference 76

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source=pdf_text observed=2026-08-04T09:25:42.848076Z digest=sha256:ea4a5def2ec0023ff1cd2fe0a471fc8ad116c8c65da12c7f81bb67c845ff68f1

Observation a4caaa46-b992-4e71-9ba7-e9340233975d · outbound

This paper cites Beavertails: Towards improved safety alignment of LLM via a human-preference dataset.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Beavertails: Towards improved safety alignment of LLM via a human-preference dataset

Reference 77

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source=pdf_text observed=2026-08-04T09:25:43.019507Z digest=sha256:4fd1fc20673f36d29db3f8f6fd877469914ffef89302c23eb044e4b67a0fd719

Observation dd32e0e4-2fb8-4cb4-882b-c3330f9b24c6 · outbound

This paper cites Baseline Defenses for Adversarial Attacks Against Aligned Language Models.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Baseline Defenses for Adversarial Attacks Against Aligned Language Models

Reference 78

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source=pdf_text observed=2026-08-04T09:25:42.972944Z digest=sha256:0bf97d40d18da2e204b719b3c5bc8f408b38ae64590d47a3eca25e36c1a6e7e8

Observation 9a4f3330-ff82-440f-b9a5-8e93d4572fc1 · outbound

This paper cites Automated progressive red teaming.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Automated progressive red teaming

Reference 79

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source=pdf_text observed=2026-08-04T09:25:43.222934Z digest=sha256:c4cd67ce3e29f034a78625dd505cf210f3510a927d8b1eadcfa428fd4dbab3ce

Observation 2a66055a-f2f6-4ede-9f09-0dac4dcfb177 · outbound

This paper cites Improved techniques for optimization-based jailbreaking on large language models.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Improved techniques for optimization-based jailbreaking on large language models

Reference 80

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source=pdf_text observed=2026-08-04T09:25:43.117969Z digest=sha256:df10b28da93b425e87f3d8daaee7f9f43fbedbaf119aa4946ae61aab8e161de0

Observation b67286f1-d679-48a7-8b1a-f8d062120594 · outbound

This paper cites How Well Do LLMs Handle Cantonese? Benchmarking Cantonese Capabilities of Large Language Models.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses How Well Do LLMs Handle Cantonese? Benchmarking Cantonese Capabilities of Large Language Models

Reference 81

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source=pdf_text observed=2026-08-04T09:25:43.462117Z digest=sha256:5d3ce96071c29b56b70bbf5a7951cf6431c85bf9c44bfcf30f0a5cfb57efd7e5

Observation 993f03bd-2797-4073-b236-6c4772f2f376 · outbound

This paper cites Artprompt: ASCII art-based jailbreak attacks against aligned llms.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Artprompt: ASCII art-based jailbreak attacks against aligned llms

Reference 82

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source=pdf_text observed=2026-08-04T09:25:43.358431Z digest=sha256:be85711579a7053ca06542a6e2b4e3902bf853f9e8fcf4ea6cc0f094f1c627bc

Observation dc745b0d-06b8-4b6d-aa5d-f51d89c7c6e0 · outbound

This paper cites An Optimizable Suffix Is Worth A Thousand Templates: Efficient Black-box Jailbreaking without Affirmative Phrases via LLM as Optimizer.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses An Optimizable Suffix Is Worth A Thousand Templates: Efficient Black-box Jailbreaking without Affirmative Phrases via LLM as Optimizer

Reference 83

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source=pdf_text observed=2026-08-04T09:25:43.691276Z digest=sha256:7c95fefd39bd557a39c6c2660579850f94fb1045a1480464ba471bcbbe0a1454

Observation b48ce965-8368-4aac-839f-57502c835e31 · outbound

This paper cites Wildteaming at scale: From in-the-wild jailbreaks to (adversarially) safer language models.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Wildteaming at scale: From in-the-wild jailbreaks to (adversarially) safer language models

Reference 84

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source=pdf_text observed=2026-08-04T09:25:43.586510Z digest=sha256:3a91fec249c68034661646c19a2f1a34b8bb8ca133b15e9bc334f6b5a4fe79e6

Observation 58b33b15-decc-4f83-94c5-397ff037b586 · outbound

This paper cites GUARD: role-playing to generate natural-language jailbreakings to test guideline adherence of large language models.CoRR, abs/2402.03299, 2024.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses GUARD: role-playing to generate natural-language jailbreakings to test guideline adherence of large language models.CoRR, abs/2402.03299, 2024

Reference 85

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source=pdf_text observed=2026-08-04T09:25:43.924124Z digest=sha256:6af9752b189c4a060b2b95962178da2071ff99040222a575692faed5562fa1b7

Observation 72cb0e55-3ef8-44af-b1be-3c2f0f1f0849 · outbound

This paper cites RED QUEEN: Safeguarding Large Language Models against Concealed Multi-Turn Jailbreaking.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses RED QUEEN: Safeguarding Large Language Models against Concealed Multi-Turn Jailbreaking

Reference 86

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source=pdf_text observed=2026-08-04T09:25:43.834739Z digest=sha256:cca78d7a6e78cece8d7f89fd2642df88d691cd0f1a73d5ad384a9bdb9bd6706d

Observation 3ac69714-b70b-42f7-9c0a-9d2a565ae173 · outbound

This paper cites airoboros-2.2, 2023.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses airoboros-2.2, 2023

Reference 87

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source=pdf_text observed=2026-08-04T09:25:44.167467Z digest=sha256:edcf87cf92da18fa81c82f0531cdccf6f59607dc9b82a2f5cee29f988dc01ac4

Observation 41e4b29d-9027-4200-8c48-8ec8fb5729b6 · outbound

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

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Jailbreakzoo: Survey, landscapes, and horizons in jailbreaking large language and vision-language models.CoRR, abs/2407.01599, 2024

Reference 88

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source=pdf_text observed=2026-08-04T09:25:44.048608Z digest=sha256:84e230b817d0f7289ec29478246932b62aa437534f5e6dfc7f5ec2f9e440d05a

Observation 61c77a38-f94c-43af-8797-e095fed36918 · outbound

This paper cites Adversaries Can Misuse Combinations of Safe Models.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Adversaries Can Misuse Combinations of Safe Models

Reference 89

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source=pdf_text observed=2026-08-04T09:25:44.450683Z digest=sha256:2aaf7c4072c7898d51e6ee99c9aabcbe5112d27f7f34cd06e9d5d8e491b785b9

Observation d4b97758-ce27-4c7d-9aab-6405ad414d27 · outbound

This paper cites Dragan, Aditi Raghunathan, and Jacob Steinhardt.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Dragan, Aditi Raghunathan, and Jacob Steinhardt

Reference 90

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source=pdf_text observed=2026-08-04T09:25:44.303783Z digest=sha256:286751b48f742056c52ba368b64ef9dc50e8e15b547417b93c9f11a2caa7d805

Observation 335c8800-3be9-4431-add4-ab1500a44bb0 · outbound

This paper cites Prompt Injection Attacks in Defended Systems.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Prompt Injection Attacks in Defended Systems

Reference 91

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source=pdf_text observed=2026-08-04T09:25:44.752795Z digest=sha256:a868280508f3ce2f5d98f82c8097ed1a4a07a07aab3ab0a00c54ffefe753ee3b

Observation c932a8fe-9906-402a-b4f3-6e6a69f40eab · outbound

This paper cites Exploiting programmatic behavior of llms: Dual-use through standard security attacks.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Exploiting programmatic behavior of llms: Dual-use through standard security attacks

Reference 92

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source=pdf_text observed=2026-08-04T09:25:44.645801Z digest=sha256:ad45fe080306b50ffb2ba29fd384d1824f11548f006e9461e0bfc713ab74896f

Observation d03ab2ff-9879-4317-98e8-9dd6494ca598 · outbound

This paper cites Testing the Limits of Jailbreaking Defenses with the Purple Problem.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Testing the Limits of Jailbreaking Defenses with the Purple Problem

Reference 93

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source=pdf_text observed=2026-08-04T09:25:45.047622Z digest=sha256:aee32c079f7c2d15a4d37ee6731e273c0ed15b7d3eadd6ec8102e03bddde7eab

Observation 59b80ef8-a9d3-4ade-9607-bfd534c63dad · outbound

This paper cites Break the Breakout: Reinventing LM Defense Against Jailbreak Attacks with Self-Refinement.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Break the Breakout: Reinventing LM Defense Against Jailbreak Attacks with Self-Refinement

Reference 94

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source=pdf_text observed=2026-08-04T09:25:44.871354Z digest=sha256:2626350fc1c8c7a6da89fe4dd03246e453e8050d655b3551a1c2a50afe53ed3e

Observation 510fa07b-1d80-43a3-9c68-e3914854dc9c · outbound

This paper cites Certifying LLM Safety against Adversarial Prompting.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Certifying LLM Safety against Adversarial Prompting

Reference 95

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source=pdf_text observed=2026-08-04T09:25:45.397309Z digest=sha256:a4763b2b34dcc819540285fb506f70caf3bf3d9875c8cd28d94855f8bfc9f62f

Observation e9abbf7f-1f7f-47bf-9ac7-84ff298caac9 · outbound

This paper cites Empirical Analysis of Large Vision-Language Models against Goal Hijacking via Visual Prompt Injection.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Empirical Analysis of Large Vision-Language Models against Goal Hijacking via Visual Prompt Injection

Reference 96

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source=pdf_text observed=2026-08-04T09:25:45.204025Z digest=sha256:0f05e356f19acf47bb8e308d26a15acbab4ece84bde242dc741e6bb60bf599a4

Observation 91d877c2-d706-4788-9dcf-4d42d5b19472 · outbound

This paper cites Platypus: Quick, Cheap, and Powerful Refinement of LLMs.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Platypus: Quick, Cheap, and Powerful Refinement of LLMs

Reference 97

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source=pdf_text observed=2026-08-04T09:25:45.762344Z digest=sha256:c1f0da95f28e1822443a9cfeeb125e51709c210ea6396439bf75272b8a2d4ac7

Observation 8ab876c1-6575-4d55-b711-3fb3217f109b · outbound

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

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Open Sesame! Universal Black Box Jailbreaking of Large Language Models

Reference 98

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source=pdf_text observed=2026-08-04T09:25:45.557792Z digest=sha256:cfd519c2f9f45c6899c74d69a32497f2be1c9a8b49172e49063c477b2c7dbca8

Observation 16f4b4f1-0897-4ce5-b30d-3bc42dd98d7d · outbound

This paper cites A Cross-Language Investigation into Jailbreak Attacks in Large Language Models.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses A Cross-Language Investigation into Jailbreak Attacks in Large Language Models

Reference 99

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no resolver link, observed 2026-08-04T09:25:45.961893Z

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source=pdf_text observed=2026-08-04T09:25:45.961893Z digest=sha256:1a8a2ca8d40077dac76ff298b5aa0c2f48168683a87b9fb5cfe0745d82708388

Observation 2c28a4b1-0b8e-4d39-9786-d5530b3e1275 · outbound

This paper cites Multi-step jailbreaking privacy attacks on chatgpt.

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses Multi-step jailbreaking privacy attacks on chatgpt

Reference 100

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source=pdf_text observed=2026-08-04T09:25:45.887588Z digest=sha256:476323f5dce40ad6f023231573e943ceba83e277b8a5b4e360b57d7833e80903

Pith citing papers

Observation c6354cbb-4b4a-47b8-b2c4-482deab1990e · inbound

One Goal, Many Commands: Characterizing Denylist Fragility in AI Agents cites this paper.

One Goal, Many Commands: Characterizing Denylist Fragility in AI Agents SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses

Reference 51

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arxiv_id, observed 2026-07-07T03:17:13.442555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-27T04:42:45.420398Z digest=sha256:165891a29682e2b28f338b42b4914694ef02d6b2ec76a837d7d95d2fd9b3cabd

Observation dcc69231-6c3f-4338-9e8e-94f2e02cdc18 · inbound

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems cites this paper.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses

Reference 17

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arxiv_id, observed 2026-07-07T03:17:13.442555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-25T20:56:20.603088Z digest=sha256:cf443ffdbbe9383738bc9e3a89606637ad54fa83694792c045b62f853f5ae884

Observation f9cbcb8c-5570-4e46-a76b-b0d6fa736dd8 · inbound

SoK: Intent-Oriented Systematization of Multi-Turn LLM Jailbreaks cites this paper.

SoK: Intent-Oriented Systematization of Multi-Turn LLM Jailbreaks SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses

Reference 67

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source=pdf_text observed=2026-08-06T00:32:02.668360Z digest=sha256:ef4017bf8c1c0960440e6f6d832306b355925daff907dcb79057492322340323