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Paper Citation Record · LEDGER

A Survey of Attacks on Large Language Models

As of 19 August 2026, this Paper Citation Record lists 90 of 90 outbound references and 8 inbound Pith citation observations for arXiv:2505.12567.

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

pith.paper-citation-record.v1
2505.12567 v1

Coverage vector

measured 90 of 90 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:34:34.832662Z

measured 98 of 98 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:23:14.155540Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T11:05:42.376291Z

Reference resolution

90 of 90 outbound references displayed

  • verified exact0
  • verified fuzzy24
  • unresolved66
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6e927fab-1f0d-4e14-991a-54396924dd3b · outbound

This paper cites Pre-Trained Language Models for Text Generation: A Survey,.

A Survey of Attacks on Large Language Models Pre-Trained Language Models for Text Generation: A Survey,

Reference 1

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source=pdf_text observed=2026-08-15T20:34:34.191420Z digest=sha256:705fd2b28021ab69b87d25b9d330b188be06ef5a2e682b1e908cf9f5cf006657

Observation 606bc696-3ee8-40fa-97f6-9ebff78bb358 · outbound

This paper cites Reasoning with Large Language Models, A Survey,.

A Survey of Attacks on Large Language Models Reasoning with Large Language Models, A Survey,

Reference 2

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source=pdf_text observed=2026-08-15T20:34:34.196723Z digest=sha256:a17da74f59074e830868b2550ffdc920f41d0c9679315b9c9df766bcb3ec3118

Observation 5b1b2fbc-4bed-4327-9204-f37ec6583798 · outbound

This paper cites Sentiment Analysis in the Era of Large Language Models: A Reality Check.

A Survey of Attacks on Large Language Models Sentiment Analysis in the Era of Large Language Models: A Reality Check

Reference 3

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source=pdf_text observed=2026-08-15T20:34:34.201549Z digest=sha256:8d4bb490f18e8271f03bf21e419644210667fa44739f40cec7ba59780715d96b

Observation 2737c039-9f37-48cc-b26f-2502ad76cdcf · outbound

This paper cites ChatGPT (Feb 20 Version),.

A Survey of Attacks on Large Language Models ChatGPT (Feb 20 Version),

Reference 4

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source=pdf_text observed=2026-08-15T20:34:34.206866Z digest=sha256:fd871ccea6f4c751d42e655e4f103093d774e1f74e5fe276462e4e4cc1ad6785

Observation 8856b0ae-c7b1-406a-977e-b0cacd9b5667 · outbound

This paper cites The Llama 3 Herd of Models.

A Survey of Attacks on Large Language Models The Llama 3 Herd of Models

Reference 5

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source=pdf_text observed=2026-08-15T20:34:34.212910Z digest=sha256:2a1b04019a40f08ba7f8e8960598a1656b1acb2df2c22d8d56251c1e82d23d67

Observation 6189d6aa-8674-4f31-92e7-a1986c2cf79b · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

A Survey of Attacks on Large Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 6

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source=pdf_text observed=2026-08-15T20:34:34.219706Z digest=sha256:187b4a9305a09e7e22c04b874c2079a8eedd50ec1902dd1b4229396ecfa68ecf

Observation bd1f6d34-04f0-4c4c-a2c9-b55e0f901363 · outbound

This paper cites [Online].

A Survey of Attacks on Large Language Models [Online]

Reference 7

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source=pdf_text observed=2026-08-15T20:34:34.226630Z digest=sha256:4dbde99eee76d195729c7f6b23cfbd86a5c317208ea6c59a2f56dc28bb12e07d

Observation 0b92c603-c773-4090-ad48-2e8743ba095b · outbound

This paper cites Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond,.

A Survey of Attacks on Large Language Models Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond,

Reference 8

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source=pdf_text observed=2026-08-15T20:34:34.233775Z digest=sha256:37a0d5933b77536478be660535c30c0132b8b8ffb32d38c09fc04016b9f351f0

Observation aca0cad4-b407-49d9-ab58-495b7aff1ab0 · outbound

This paper cites A survey on Large Language Model Based Autonomous Agents,.

A Survey of Attacks on Large Language Models A survey on Large Language Model Based Autonomous Agents,

Reference 9

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source=pdf_text observed=2026-08-15T20:34:34.238698Z digest=sha256:4b202e738db3cd6d6094e52592faa468259d2238b53a6b0cf06f16db0433e389

Observation ee1f4f2b-125b-481c-8355-6fea5c496f3d · outbound

This paper cites A Comprehensive Overview of Large Language Models.

A Survey of Attacks on Large Language Models A Comprehensive Overview of Large Language Models

Reference 10

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source=pdf_text observed=2026-08-15T20:34:34.244175Z digest=sha256:882fa36e514486c163bb6a5ff3fb923b1c56e2b060fe6442aec36fc54e648621

Observation 02300cd2-38ae-4ed8-bf0e-3ed8fd4890ea · outbound

This paper cites Attention Is All You Need.

A Survey of Attacks on Large Language Models Attention Is All You Need

Reference 11

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source=pdf_text observed=2026-08-15T20:34:34.250543Z digest=sha256:14f2e912942a063e95c60d547e3499b52b7fd955f2b9b8c39526ec8a3f20834e

Observation 64ef5e71-d0bc-481e-9960-e7eb247dbcb0 · outbound

This paper cites A Survey of Recent Backdoor Attacks and Defenses in Large Language Models,.

A Survey of Attacks on Large Language Models A Survey of Recent Backdoor Attacks and Defenses in Large Language Models,

Reference 12

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source=pdf_text observed=2026-08-15T20:34:34.256952Z digest=sha256:2674ee7551452ed7837cd735dcf0f77fee90603a14a6680dca3b05f718a54afe

Observation 4d95a2ce-d15a-4b46-98aa-edfc1d063585 · outbound

This paper cites BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks and Defenses on Large Language Models.

A Survey of Attacks on Large Language Models BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks and Defenses on Large Language Models

Reference 13

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source=pdf_text observed=2026-08-15T20:34:34.263721Z digest=sha256:b0e6f55e4be0553ac325c37c2581cd832eb67dfc37b18ee8af5df4600dd5b9c6

Observation 2cae0c49-65ca-4767-baba-9eac0d44d635 · outbound

This paper cites Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger.

A Survey of Attacks on Large Language Models Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger

Reference 14

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source=pdf_text observed=2026-08-15T20:34:34.269857Z digest=sha256:7e8cd92cd972139e3bc94e22affe385196665b91864651bbe5080a716bd52afa

Observation 4690a156-df51-41b0-a499-0f0acb8fd993 · outbound

This paper cites Hid- den Backdoors in Human-Centric Language Models,.

A Survey of Attacks on Large Language Models Hid- den Backdoors in Human-Centric Language Models,

Reference 15

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source=pdf_text observed=2026-08-15T20:34:34.275807Z digest=sha256:6beabf6b13017a4796535d9e1d125a4941e2329f3f02ec2d8b0a4d49d1c02821

Observation 43f8b2f3-f942-487e-8bc7-81fe8eb454f6 · outbound

This paper cites Composite Backdoor Attacks Against Large Language Models.

A Survey of Attacks on Large Language Models Composite Backdoor Attacks Against Large Language Models

Reference 16

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source=pdf_text observed=2026-08-15T20:34:34.282095Z digest=sha256:ecd8015f9fc198ae49fc7bd80f36cc6fa3ecc29f522c13e765bccd7af2a2ec48

Observation 6eae61db-a30a-4373-8ec5-4e8cff431d01 · outbound

This paper cites PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models.

A Survey of Attacks on Large Language Models PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models

Reference 17

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source=pdf_text observed=2026-08-15T20:34:34.286851Z digest=sha256:8291d2690cd51107f5d306a16f69c6bc016b4a36ad50ceac0f96ab4d8b2a4377

Observation a8022600-dfa7-433d-a4d4-544321b4bb49 · outbound

This paper cites Instruction Backdoor Attacks Against Customized LLMs,.

A Survey of Attacks on Large Language Models Instruction Backdoor Attacks Against Customized LLMs,

Reference 18

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source=pdf_text observed=2026-08-15T20:34:34.291195Z digest=sha256:694caf4a33c480836f402ab7aebdca76089075034fc8cb0623f6c311389ebd08

Observation 5d399286-3721-42ae-aee2-3ccc8b4b0206 · outbound

This paper cites Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection.

A Survey of Attacks on Large Language Models Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection

Reference 19

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source=pdf_text observed=2026-08-15T20:34:34.295275Z digest=sha256:1f4eb88628eb4428675db01aec85a2da08bb795c68ace77326c629fe916107ff

Observation f903d963-56cb-499e-824d-c4a8c81368f3 · outbound

This paper cites BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT.

A Survey of Attacks on Large Language Models BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT

Reference 20

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source=pdf_text observed=2026-08-15T20:34:34.299537Z digest=sha256:97bd03dbe9fe2bac18f50aa6db0fc7a4be295ce89df12d3a8242fff9ca8db763

Observation 639a51af-7e2b-4423-bad2-7478cf0fbf35 · outbound

This paper cites RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models.

A Survey of Attacks on Large Language Models RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models

Reference 21

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source=pdf_text observed=2026-08-15T20:34:34.306384Z digest=sha256:4b8f9372241471d02d7bb201958c661da253578c656b3394b9d3630dba967f5f

Observation 8c72e250-7cf8-46cd-a26b-2bf3fd0446e9 · outbound

This paper cites TrojLLM: A Black-box Trojan Prompt Attack on Large Language Models,.

A Survey of Attacks on Large Language Models TrojLLM: A Black-box Trojan Prompt Attack on Large Language Models,

Reference 22

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source=pdf_text observed=2026-08-15T20:34:34.323183Z digest=sha256:1dd26345978759bac67ce7beae43fb452c042b9a4c5554a7cff8358cb04a882b

Observation bef1fbbc-71d5-4c76-80bf-fa7d3d9d5c86 · outbound

This paper cites PoisonPrompt: Backdoor Attack on Prompt-Based Large Language Models,.

A Survey of Attacks on Large Language Models PoisonPrompt: Backdoor Attack on Prompt-Based Large Language Models,

Reference 23

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source=pdf_text observed=2026-08-15T20:34:34.329119Z digest=sha256:2f48bf8f28787dbd8d109d47f2de760bead1eaeca19451bca7642a1225060ec2

Observation fc92ec7a-302d-4d4a-b93c-805ff9a70614 · outbound

This paper cites BadEdit: Backdooring large language models by model editing.

A Survey of Attacks on Large Language Models BadEdit: Backdooring large language models by model editing

Reference 24

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source=pdf_text observed=2026-08-15T20:34:34.334790Z digest=sha256:6d4a794f279fa1d2792b72a97c70c07765b3b12049ebc65cbb1405c516169fa2

Observation ddba28a2-7eae-4a4a-85d4-f527620c45d3 · outbound

This paper cites LoRATK: LoRA Once, Backdoor Everywhere in the Share-and-Play Ecosystem.

A Survey of Attacks on Large Language Models LoRATK: LoRA Once, Backdoor Everywhere in the Share-and-Play Ecosystem

Reference 25

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source=pdf_text observed=2026-08-15T20:34:34.340871Z digest=sha256:381ae06cc86689415b922ccba0a565e71e44a2bd94a9bb632298f6d5ac3b5ca9

Observation 32f05ac9-714b-43df-acd8-c9cbbb16a103 · outbound

This paper cites The Philosopher's Stone: Trojaning Plugins of Large Language Models.

A Survey of Attacks on Large Language Models The Philosopher's Stone: Trojaning Plugins of Large Language Models

Reference 26

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source=pdf_text observed=2026-08-15T20:34:34.347283Z digest=sha256:47eaa6022ba46fce35fcbcbca553983834a3eefced5b039b1e4d9f0712dbbac6

Observation ea36b0c2-fd72-4dda-86a6-d9dff21b81b2 · outbound

This paper cites A Gradient Control Method for Backdoor Attacks on Parameter-Efficient Tuning,.

A Survey of Attacks on Large Language Models A Gradient Control Method for Backdoor Attacks on Parameter-Efficient Tuning,

Reference 27

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source=pdf_text observed=2026-08-15T20:34:34.353573Z digest=sha256:370d31312cab1e98d262b87a0162a04468f3a705b4c9bda55a1a374a565ead9b

Observation ea5fdb87-2e78-4dfb-9370-991952db1837 · outbound

This paper cites Weak-to-Strong Jailbreaking on Large Language Models.

A Survey of Attacks on Large Language Models Weak-to-Strong Jailbreaking on Large Language Models

Reference 28

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source=pdf_text observed=2026-08-15T20:34:34.361272Z digest=sha256:655cada7d957818f1b1de2a4a21c65315b78375fc074ea71fe56adf8c0aceccc

Observation fbefdb4a-f310-4c2a-98cd-b930afcfbb91 · outbound

This paper cites Trojan Activation Attack: Red-Teaming Large Language Models using Activation Steering for Safety-Alignment.

A Survey of Attacks on Large Language Models Trojan Activation Attack: Red-Teaming Large Language Models using Activation Steering for Safety-Alignment

Reference 29

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source=pdf_text observed=2026-08-15T20:34:34.367090Z digest=sha256:3a3fd854c33f2b308da5e92fc146af4efa00977072de094dbd57312cdf84f9ae

Observation 12f7b979-4c0c-46f5-bf07-f70f35a9d194 · outbound

This paper cites BadChain: Backdoor Chain-of-Thought Prompting for Large Language Models.

A Survey of Attacks on Large Language Models BadChain: Backdoor Chain-of-Thought Prompting for Large Language Models

Reference 30

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source=pdf_text observed=2026-08-15T20:34:34.374059Z digest=sha256:3f56ce8f8d6c081df977947a112de092ca10eaab546d4a41f2a65d0045f10209

Observation a9634968-3e05-4280-ad90-0c51b60ff403 · outbound

This paper cites To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models.

A Survey of Attacks on Large Language Models To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models

Reference 31

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source=pdf_text observed=2026-08-15T20:34:34.380935Z digest=sha256:f50d8c2ff00bd721cd2815e3c2be33011631cbcccc4efc21af7440f5fbca77ce

Observation b704f2c9-92ac-4151-a4e1-976192723395 · outbound

This paper cites Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning.

A Survey of Attacks on Large Language Models Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning

Reference 32

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source=pdf_text observed=2026-08-15T20:34:34.391187Z digest=sha256:c34db0417b73a7cf75cf31eb13c5354b43793591d21ecf197a6d8146d0760245

Observation a8e20b5b-f1df-4789-9a25-e5f05cc3bb03 · outbound

This paper cites Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems.

A Survey of Attacks on Large Language Models Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems

Reference 33

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source=pdf_text observed=2026-08-15T20:34:34.404165Z digest=sha256:f7da69a13163ccad22c9835513899a2d00ad6b070feb48ad46036549bbaf7d8f

Observation d8ae9b86-b57f-4c6a-a605-6eba4af0a623 · outbound

This paper cites BadAgent: Inserting and Activating Backdoor Attacks in LLM Agents,.

A Survey of Attacks on Large Language Models BadAgent: Inserting and Activating Backdoor Attacks in LLM Agents,

Reference 34

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verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.243777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.411489Z digest=sha256:4413f2012fa36a2d5ab2720bd7c213009db4b38d65e42cc68b4dbc42555a3ccd

Observation a0ed7131-dc75-4490-b38f-a08282b9ae99 · outbound

This paper cites DemonAgent: Dynamically Encrypted Multi-Backdoor Implantation Attack on LLM- based Agent,.

A Survey of Attacks on Large Language Models DemonAgent: Dynamically Encrypted Multi-Backdoor Implantation Attack on LLM- based Agent,

Reference 35

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source=pdf_text observed=2026-08-15T20:34:34.417986Z digest=sha256:e653caee7e9ebdd0cb23406c9cadf3303a70912c02a7dea9a6afe05c10aaf0fc

Observation 53d81efb-f0f7-494a-a832-a51aae414212 · outbound

This paper cites Adversarial Example Generation with Syntactically Controlled Paraphrase Networks.

A Survey of Attacks on Large Language Models Adversarial Example Generation with Syntactically Controlled Paraphrase Networks

Reference 36

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source=pdf_text observed=2026-08-15T20:34:34.424174Z digest=sha256:ba95eebad9646b7b7047ac2e3aab186ff1c5f05443d524be3dcc89480d98502e

Observation affe0cd0-06b0-47b5-b24a-f50efed574d7 · outbound

This paper cites TrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation.

A Survey of Attacks on Large Language Models TrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation

Reference 37

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source=pdf_text observed=2026-08-15T20:34:34.429484Z digest=sha256:974321c101d25235e758e83327e807159698204c7907a05d99daff26087337e9

Observation 42526f95-ea73-4530-b4b5-4a99589122fc · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,.

A Survey of Attacks on Large Language Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.439375Z digest=sha256:74304d8e1d1de2458ac2680a27fd195a5012684bac79797fad09bbc4fcba3079

Observation 276ceb59-8064-4b29-b27f-6dd046e9126b · outbound

This paper cites ONION: A Simple and Effective Defense Against Textual Backdoor Attacks.

A Survey of Attacks on Large Language Models ONION: A Simple and Effective Defense Against Textual Backdoor Attacks

Reference 39

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.445221Z digest=sha256:032fc7b28f92c434d9df41bce7c48f58bbaf123830d4d84f830e84356a70ab7b

Observation d4f24197-de3a-4195-867d-d1a417450a2a · outbound

This paper cites BAIT: Large Language Model Backdoor Scanning by Inverting Attack Target,.

A Survey of Attacks on Large Language Models BAIT: Large Language Model Backdoor Scanning by Inverting Attack Target,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.220538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.453535Z digest=sha256:97d86e9f04ca4ab1c60ae8c7210578b596f69ea24a6b18527bda0bbdd1eafa80

Observation 2d6e0e34-e12f-4f6f-bd85-bb5bf8d5786c · outbound

This paper cites Scenic: A language for scenario specifi- cation and scene generation,.

A Survey of Attacks on Large Language Models Scenic: A language for scenario specifi- cation and scene generation,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.208524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.458792Z digest=sha256:3c44f884e5c72e5de203335529380879db007b25df6dd0bfe147078e833899fd

Observation f07aff55-1948-4b7a-bedb-7b55be3b0eb6 · outbound

This paper cites Principles and methods of testing finite state machines-a survey,.

A Survey of Attacks on Large Language Models Principles and methods of testing finite state machines-a survey,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.194698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.465184Z digest=sha256:5d8ea08c43031ad32feacf0bb675cf28d84445de974fbcd0cb5ddc6d6d3cb799

Observation 217bfb8b-21a4-42ae-9a40-d2c3e293a387 · outbound

This paper cites Security and Privacy Challenges of Large Language Models: A Survey,.

A Survey of Attacks on Large Language Models Security and Privacy Challenges of Large Language Models: A Survey,

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.472575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.472575Z digest=sha256:2d22794f907f4b5fb340af09f651a89576b946780a97dbf34fe55d103ef55529

Observation 2e0a6d24-7e25-4dc2-b84c-1e743d403264 · outbound

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

A Survey of Attacks on Large Language Models Jailbroken: How Does LLM Safety Training Fail?

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.181998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.479472Z digest=sha256:d670832ad4b01f4e1b93d8b3c81a7cff1b0a907294a3f9375006c32507780689

Observation a695a783-7505-48ca-ae1e-ae545c92439d · outbound

This paper cites Prompt Hacking: Jailbreaking,.

A Survey of Attacks on Large Language Models Prompt Hacking: Jailbreaking,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.170491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.489867Z digest=sha256:e9820b55b20deea1ee9ab44ba0b7f8ab728492ca022cac5766b89664bc26aca4

Observation 9dd7d00e-bead-45e6-a5a3-c2c30d9af675 · outbound

This paper cites JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models.

A Survey of Attacks on Large Language Models JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.496974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.496974Z digest=sha256:abc2279a3807e55fd91632edbce40c6738eabaa804b6fe5c0e3eea6d1403999c

Observation 5a5eae45-45b3-4d28-b68b-3494f803f34b · outbound

This paper cites GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts.

A Survey of Attacks on Large Language Models GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.503271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.503271Z digest=sha256:fc06d2e5a62b6ffc7a56d1fded461db070121b9d543b7d733261b7a308a02302

Observation aeb67266-583a-42ab-8ccf-f02a82c16008 · outbound

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

A Survey of Attacks on Large Language Models Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.511007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.511007Z digest=sha256:ee739bb26ef56764e99176014dcfd155683ed97ff5e1f7b98c7ce8ca4dc60744

Observation 9df709e0-0791-45f8-80b3-991a04e39171 · outbound

This paper cites Tree of Attacks: Jailbreaking Black-Box LLMs Automatically,.

A Survey of Attacks on Large Language Models Tree of Attacks: Jailbreaking Black-Box LLMs Automatically,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.158921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.518864Z digest=sha256:799cc1856e7a7f5658fdcf8cc5316fe214af0ce42c241fc14dafd3da8fbb0cd5

Observation cc920407-b97d-4470-b445-96c8b683fbf3 · outbound

This paper cites Low-Resource Languages Jailbreak GPT-4.

A Survey of Attacks on Large Language Models Low-Resource Languages Jailbreak GPT-4

Reference 50

Resolution
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no resolver link, observed 2026-08-15T20:34:34.526897Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.526897Z digest=sha256:b6ddf75c96b89fa0288efaaf15a55e766b22fc9b45d46eacc8ff181763e70014

Observation 0606193a-82ce-43cf-ac47-b4eff148cb26 · outbound

This paper cites Multilingual Jailbreak Challenges in Large Language Models.

A Survey of Attacks on Large Language Models Multilingual Jailbreak Challenges in Large Language Models

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.534869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.534869Z digest=sha256:9573234a9f5d94ebe672330f782fa7cfa531634703a1f13b5486f68ef70810f2

Observation fc5d4f19-a2d9-41da-b808-0f28704f2c55 · outbound

This paper cites Jailbreaking to Jailbreak.

A Survey of Attacks on Large Language Models Jailbreaking to Jailbreak

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.543776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.543776Z digest=sha256:acd4bca39b0b01c31132cf8041615e417f912c9bbd131afad3773bfaa939a102

Observation 59170d5e-b832-4506-a55f-64a2e8fafbb6 · outbound

This paper cites SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks.

A Survey of Attacks on Large Language Models SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.552997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.552997Z digest=sha256:a04a5bd81422924efb8e02f201a5b8d72b4f03e56a3425e807bacc6e261be989

Observation 85853ff3-d6d3-44e8-a458-1046af9e05b3 · outbound

This paper cites Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit Clues.

A Survey of Attacks on Large Language Models Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit Clues

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.563208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.563208Z digest=sha256:fb08885ff46a6402a46d9ddaad11c99310e09458edc2686f752df1fa83da2f8d

Observation 4439373d-710d-4491-a7ab-f4106ab175ed · outbound

This paper cites Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation.

A Survey of Attacks on Large Language Models Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.572871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.572871Z digest=sha256:a910a22a50d8ab352e69ddd4d60c148420e98b08fd6aa16724de4bbbfa24a28d

Observation 162cf38f-adde-4d6c-937f-d25412ecea26 · outbound

This paper cites How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs,.

A Survey of Attacks on Large Language Models How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.144761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.578498Z digest=sha256:0ef852145ac29b12162f8efbd7ab3f2bb00a66667b215a8e07c4c0dbcf726e15

Observation 2d6f1973-7f78-4fff-a99c-912963ba9242 · outbound

This paper cites Reasoning-Augmented Conversation for Multi-Turn Jailbreak Attacks on Large Language Models.

A Survey of Attacks on Large Language Models Reasoning-Augmented Conversation for Multi-Turn Jailbreak Attacks on Large Language Models

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.587307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.587307Z digest=sha256:e862628c431affecae3310769dc15ad18b2696f465ada995303d7cf13a69ad0b

Observation fc2d06d4-734e-4496-af43-211713c0ecd1 · outbound

This paper cites Dual Intention Escape: Jailbreak Attack against Large Language Models,.

A Survey of Attacks on Large Language Models Dual Intention Escape: Jailbreak Attack against Large Language Models,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.131879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.595200Z digest=sha256:f6d26152bbf0d2c4c16cba888c17601cd102fa6063dd31fa356fa893cb35f5f2

Observation 92ed050d-b41d-45ab-b3f2-b2e10b21c5b7 · outbound

This paper cites Formalizing and Benchmarking Prompt Injection Attacks and Defenses,.

A Survey of Attacks on Large Language Models Formalizing and Benchmarking Prompt Injection Attacks and Defenses,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.119363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.601408Z digest=sha256:a0200e8c0aaf1693127edaa1e91819282738da1dd9b7e88cf6ee00e25adf6a5e

Observation 249b716a-fe87-4156-8f64-2bd39e507473 · outbound

This paper cites A Study on Prompt Injection Attack Against LLM-Integrated Mobile Robotic Systems,.

A Survey of Attacks on Large Language Models A Study on Prompt Injection Attack Against LLM-Integrated Mobile Robotic Systems,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.105453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.609726Z digest=sha256:6e3ea84dcda053951f7a90e20b4ea08db48f3a0df06c211c4c9235948c7f50d7

Observation 3f980cf6-97ef-419e-80ae-dfda0790b972 · outbound

This paper cites Vocabulary Attack to Hijack Large Language Model Applications.

A Survey of Attacks on Large Language Models Vocabulary Attack to Hijack Large Language Model Applications

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.614961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.614961Z digest=sha256:7826a1722cffbb43ead75d01417031c3ae78fdcc566ce74ff1b1d5da2cb0ceea

Observation 93281239-2d0b-4c04-aae3-08e983b8e62c · outbound

This paper cites Automatic and Universal Prompt Injection Attacks against Large Language Models.

A Survey of Attacks on Large Language Models Automatic and Universal Prompt Injection Attacks against Large Language Models

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.621066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.621066Z digest=sha256:0f73544794c9510d9171dfb65003b4ef99041e862a4b8c98d68a666d1c96a156

Observation f3a1dbf6-95b3-4e59-b920-2813ab1c9fd1 · outbound

This paper cites Optimization-based Prompt Injection Attack to LLM-as-a-Judge,.

A Survey of Attacks on Large Language Models Optimization-based Prompt Injection Attack to LLM-as-a-Judge,

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.091949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.630590Z digest=sha256:e24886e571132e8d1605c201897a12233dcf0429a4049a08889a0b1bbc2bec29

Observation 18412a91-f0f0-4f09-9565-1aa63a9c4f9f · outbound

This paper cites Goal-guided Generative Prompt Injection Attack on Large Language Models.

A Survey of Attacks on Large Language Models Goal-guided Generative Prompt Injection Attack on Large Language Models

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.636320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.636320Z digest=sha256:fc49c16065ff963ce41452286508d10489f9ca79b9a3d7ce784c57dadee7f51c

Observation ae9e1c3c-9d15-4d0b-beba-d19e9ba8b1ff · outbound

This paper cites Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems.

A Survey of Attacks on Large Language Models Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.644015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.644015Z digest=sha256:69db53b3180870feb33cc42e281d18099eac0d4bfc6820c081efe8bd32e9169a

Observation 610d95b6-3684-4db4-838e-f7bbb2817568 · outbound

This paper cites Ignore Previous Prompt: Attack Techniques For Language Models.

A Survey of Attacks on Large Language Models Ignore Previous Prompt: Attack Techniques For Language Models

Reference 66

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.651555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.651555Z digest=sha256:68128cfbce950a9147434354ed7db8e036500904a53d7d5930c4e707ed938ceb

Observation 465923f2-c46e-4d1b-8f08-ea573cc01eaf · outbound

This paper cites Delimiters Won’t Save You,.

A Survey of Attacks on Large Language Models Delimiters Won’t Save You,

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.080650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.657965Z digest=sha256:9c29dba39e15c67c88452d9fcd7cdf953455f1712e98ba061b737a96cb58fe61

Observation f8b0b41c-e1a3-41c8-91c9-a097382c40e6 · outbound

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

A Survey of Attacks on Large Language Models Baseline Defenses for Adversarial Attacks Against Aligned Language Models

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.663920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.663920Z digest=sha256:758a0bfb3d098fb18a0122506f57bb4abe41bc66e3ca89c187a2ddca0ecd05e0

Observation 70de337b-b896-451a-9a49-f76303e650de · outbound

This paper cites Ultimate ChatGPT Prompt Engineering Guide for General Users and Developers,.

A Survey of Attacks on Large Language Models Ultimate ChatGPT Prompt Engineering Guide for General Users and Developers,

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.068567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.670061Z digest=sha256:7245afb28064a0fb11f128afb1837a86b6a16943ec986a556d669c7f4c2d15b0

Observation bf908670-5bf9-45ea-a9bf-23a94aabe916 · outbound

This paper cites Sandwich defense,.

A Survey of Attacks on Large Language Models Sandwich defense,

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.056403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.675561Z digest=sha256:2559a08b4f58b6c091c32bc1d693662ce1ba287abe1a07c8d25fc5b80602fec3

Observation ec786244-f26f-4698-8d76-aebe33e0d5c5 · outbound

This paper cites Instruction defense,.

A Survey of Attacks on Large Language Models Instruction defense,

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.043375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.680829Z digest=sha256:4fbe421d53dd78f46c33b6668daecea9c0f8a02a06c0fedb2ce6f71237dc9dba

Observation e7b6e999-9bb9-4ef7-8a31-9e5e5aeea3ca · outbound

This paper cites Detecting Language Model Attacks with Perplexity.

A Survey of Attacks on Large Language Models Detecting Language Model Attacks with Perplexity

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.686308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.686308Z digest=sha256:cd3164ac3477b667d3e1336cf913a799131bd703866c7da811940c0ea8877719

Observation 270e9c0a-1d06-40b2-a565-2bdb9726e325 · outbound

This paper cites Using gpt: Eliezer against chatgpt jailbreaking,.

A Survey of Attacks on Large Language Models Using gpt: Eliezer against chatgpt jailbreaking,

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.031110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.691775Z digest=sha256:3b708cb2d352c21a2c1a0a6f6821fc2301e1b34af1600bb7258199fd3116d8a5

Observation aae60384-94b0-4303-8daf-64f65cab986a · outbound

This paper cites Exploring prompt injection attacks.

A Survey of Attacks on Large Language Models Exploring prompt injection attacks

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.006044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.710105Z digest=sha256:4ddec4834f5aa752e6b051909c42c202301819ff2aa926cfd19d6453f92edcd3

Observation 9c4f16f7-2c30-4d8d-9910-370037abe360 · outbound

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

A Survey of Attacks on Large Language Models Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.726147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.726147Z digest=sha256:b7594983e34b408cfa1ae7f4e28ba844505f278df37bec85846c40f3c1bf5b0e

Observation 566ceb58-c2e3-48f1-86b3-3257ed672434 · outbound

This paper cites Defending Against Indirect Prompt Injection Attacks With Spotlighting.

A Survey of Attacks on Large Language Models Defending Against Indirect Prompt Injection Attacks With Spotlighting

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.741425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.741425Z digest=sha256:618cf3c52e49a193345a74ea59ad2094eee8034d31e3d93b39f00b34c423dae1

Observation fc94e52a-b113-403a-8675-d156df9e5ad6 · outbound

This paper cites Denial-of-Service Poisoning Attacks against Large Language Models.

A Survey of Attacks on Large Language Models Denial-of-Service Poisoning Attacks against Large Language Models

Reference 77

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unresolved
no resolver link, observed 2026-08-15T20:34:34.747573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.747573Z digest=sha256:a1236171b825f01f8291b28d3a6169253fb65367dadf4d8765bbe718c77b5fc1

Observation 9ddfb50f-3ba8-4b1d-bc3e-e8e028f121ef · outbound

This paper cites LLM Denial of Service,.

A Survey of Attacks on Large Language Models LLM Denial of Service,

Reference 78

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verified fuzzy
raw_fallback, observed 2026-08-15T20:34:35.991479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.759615Z digest=sha256:a18a43da0cdae3d7e747379142c7eb4693a9e00e846c2a2a879d47a81b19aaa5

Observation 471f4d1e-a7c8-4116-ab27-0e95774b5cf9 · outbound

This paper cites Understanding Regular Expression Denial of Service (ReDoS): Insights from LLM-Generated Regexes and Developer Forums,.

A Survey of Attacks on Large Language Models Understanding Regular Expression Denial of Service (ReDoS): Insights from LLM-Generated Regexes and Developer Forums,

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:35.979218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.766333Z digest=sha256:c55775f4935ccadb60443f387716ed109cf119db4f43096d662486b03889e2f7

Observation c5a11f2c-14cd-46db-93b6-da81bad6b178 · outbound

This paper cites LLM Safeguard is a Double-Edged Sword: Exploiting False Positives for Denial-of-Service Attacks.

A Survey of Attacks on Large Language Models LLM Safeguard is a Double-Edged Sword: Exploiting False Positives for Denial-of-Service Attacks

Reference 80

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no resolver link, observed 2026-08-15T20:34:34.773621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.773621Z digest=sha256:3dbb8a0674a00a7c9c7d77077cde78f381d56529e2dc5522dbcb1d475684d41d

Observation c50b9911-646d-4cab-9416-6f4143ac5136 · outbound

This paper cites Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.

A Survey of Attacks on Large Language Models Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Reference 81

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no resolver link, observed 2026-08-15T20:34:34.779860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.779860Z digest=sha256:f8b69bc08399f7f8167613e81da2d606f3b5de894cb43a40e7d5b3eb4f8e883a

Observation d3a855c5-1474-4c67-b20a-247567e9cb81 · outbound

This paper cites Textbooks Are All You Need.

A Survey of Attacks on Large Language Models Textbooks Are All You Need

Reference 82

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no resolver link, observed 2026-08-15T20:34:34.788766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.788766Z digest=sha256:6a740793b7f31185f109fe1427327e7e95e66864daed0b9c26b5b3179c56094e

Observation 0f291e9b-af98-4d91-a5b0-705836057257 · outbound

This paper cites Para- phrasing evades detectors of AI-generated text, but retrieval is an effective defense,.

A Survey of Attacks on Large Language Models Para- phrasing evades detectors of AI-generated text, but retrieval is an effective defense,

Reference 83

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:35.964554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.797129Z digest=sha256:d69a4c766cf69deca1d64810c0f37d5c0b1d313e1d98fdea69c520c3e8b7070c

Observation 71615c22-b4a6-4866-8cb1-67e8b22b3a50 · outbound

This paper cites Can AI-Generated Text be Reliably Detected?.

A Survey of Attacks on Large Language Models Can AI-Generated Text be Reliably Detected?

Reference 84

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no resolver link, observed 2026-08-15T20:34:34.802192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.802192Z digest=sha256:9ec48a30a64e49b786d38c580b4e9c9d644cdbf7d54349d9daa85a7a197879f9

Observation ed8688dc-ccba-44fd-a7ad-38214f83122f · outbound

This paper cites Red Teaming Language Model Detectors with Language Models,.

A Survey of Attacks on Large Language Models Red Teaming Language Model Detectors with Language Models,

Reference 85

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:35.951395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.807323Z digest=sha256:59e104a6c0e24666a97b5ded492b7482ba8e10a6ea2268141620211de368564f

Observation 6d3a0909-7f28-4dbe-8c09-ff6efb00fb31 · outbound

This paper cites Undetectable Watermarks for Lan- guage Models,.

A Survey of Attacks on Large Language Models Undetectable Watermarks for Lan- guage Models,

Reference 86

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:35.937984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.813447Z digest=sha256:f9fd2e0310355ea28cf97b9c6d3953292d8f1925cf07c4e7d3fc0c8dd7a82010

Observation 106bf49e-c9a7-43e2-b85c-48e4db129973 · outbound

This paper cites Large Language Models can be Guided to Evade AI-Generated Text Detection.

A Survey of Attacks on Large Language Models Large Language Models can be Guided to Evade AI-Generated Text Detection

Reference 87

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no resolver link, observed 2026-08-15T20:34:34.818911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.818911Z digest=sha256:1e75106b089ab880b90f72114814de2701c4af18ce1be8cc582a52dd73ec5a6d

Observation a592ce91-f788-45d4-b2cc-10a22fb78030 · outbound

This paper cites Bypassing LLM Watermarks with Color-Aware Substitutions.

A Survey of Attacks on Large Language Models Bypassing LLM Watermarks with Color-Aware Substitutions

Reference 88

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:34.825208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.825208Z digest=sha256:6e1e5846c73a890446b520ca1a066a82ab59569496e6d598ef88ba3fb6e8066c

Observation cc376204-3355-4dc5-95be-1d7e458d6a93 · outbound

This paper cites $B^4$: A Black-Box Scrubbing Attack on LLM Watermarks.

A Survey of Attacks on Large Language Models $B^4$: A Black-Box Scrubbing Attack on LLM Watermarks

Reference 89

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no resolver link, observed 2026-08-15T20:34:34.832662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:34.832662Z digest=sha256:8353ad311f7de962404c7d1e6a31d16299f2433523e4fbd63c446c412342b935

Observation 29f64c01-c193-4fb6-a04d-39308f2718fb · outbound

This paper cites Available:\url{https://www.alignmentforum.org/posts/ pNcFYZnPdXyL2RfgA/using-gpt-eliezer-against-chatgpt-jailbreaking}.

A Survey of Attacks on Large Language Models Available:\url{https://www.alignmentforum.org/posts/ pNcFYZnPdXyL2RfgA/using-gpt-eliezer-against-chatgpt-jailbreaking}

Reference 2023

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:34:36.018050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T20:34:34.701550Z digest=sha256:8fa78ba5492f3b638b5049cb96a6c744772eb17ebc7beb51dbb405ff6dc74207

Pith citing papers

Observation 0ce7fa8f-8630-4980-9495-8b48523e056b · inbound

A Survey on Model Extraction Attacks and Defenses for Large Language Models cites this paper.

A Survey on Model Extraction Attacks and Defenses for Large Language Models A Survey of Attacks on Large Language Models

Reference 86

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no resolver link, observed 2026-08-06T22:23:14.155540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:23:14.155540Z digest=sha256:43f27ce2658469d25841a18b33a73a27e31529ccd9ce72e40b8d4a64702b8adc

Observation 4437ed19-2bef-44be-8e2b-3b2765d5a548 · inbound

Exploiting Web Search Tools of AI Agents for Data Exfiltration cites this paper.

Exploiting Web Search Tools of AI Agents for Data Exfiltration A Survey of Attacks on Large Language Models

Reference 10

Resolution
metadata mismatch
arxiv_id, observed 2026-05-18T08:36:07.186914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-18T08:36:04.262528Z digest=sha256:5ccbf6a14e89e87df21f4c03384493ba8a16ce00667a6f3b2b5327bfcfe8637d

Observation b3b4624a-5506-431b-8382-3254edd8199a · inbound

Step-Wise Refusal Dynamics in Autoregressive and Diffusion Language Models cites this paper.

Step-Wise Refusal Dynamics in Autoregressive and Diffusion Language Models A Survey of Attacks on Large Language Models

Reference 23

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unresolved
no resolver link, observed 2026-08-03T05:45:43.424556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:45:43.424556Z digest=sha256:ec159cbab7ff6dc6e639149964091132953ab1641dc828a5232358adf6922ca6

Observation c0b1cc9f-0519-459c-bdd1-fcfc6bc94ae5 · inbound

Why Do Large Language Models Generate Harmful Content? cites this paper.

Why Do Large Language Models Generate Harmful Content? A Survey of Attacks on Large Language Models

Reference 20

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verified exact
arxiv_id, observed 2026-05-11T10:21:04.698068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-10T15:31:13.545599Z digest=sha256:747e5f512a25e5a8fed2d4cba707c3bbf6481d7697e1dea34e4d783814c8d278

Observation f0ea97eb-3299-4eee-bcf9-d6849840aa43 · inbound

SRTJ: Self-Evolving Rule-Driven Training-Free LLM Jailbreaking cites this paper.

SRTJ: Self-Evolving Rule-Driven Training-Free LLM Jailbreaking A Survey of Attacks on Large Language Models

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-05-11T16:01:12.426820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-09T18:56:46.692954Z digest=sha256:1640360de5abf9c4c287c2cda17fe45c00a66d603004677bac2ab68002534ad1

Observation c2da5128-f804-4b81-bdbd-f4f90322c51e · inbound

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits cites this paper.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits A Survey of Attacks on Large Language Models

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-15T01:53:29.081821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-15T01:50:21.013336Z digest=sha256:2c749e839042190451eb4ce417679921af30738979f662bca76b5d95e73d22db

Observation d74a1925-d0ef-4aed-b636-5f55d00c4d90 · inbound

MemAudit: Post-hoc Auditing of Poisoned Agent Memory via Causal Attribution and Structural Anomaly Detection cites this paper.

MemAudit: Post-hoc Auditing of Poisoned Agent Memory via Causal Attribution and Structural Anomaly Detection A Survey of Attacks on Large Language Models

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-25T04:05:20.417334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-25T04:05:15.708438Z digest=sha256:1de765acc48b2ff54bf0613d54de9c089d73c68c8e74e41dc75db717d34c2464

Observation b8ccbc72-829b-40be-a8cf-72766e565523 · inbound

A Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open Problems cites this paper.

A Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open Problems A Survey of Attacks on Large Language Models

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-07-01T11:05:42.377798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-01T04:44:23.543728Z digest=sha256:4cb6d97ae2cd5b068da451a1f48c5ca6802353e0b454f33ebca9a786a0171c43