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Paper Citation Record · LEDGER

Practical Reasoning Interruption Attacks on Reasoning Large Language Models

As of 18 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 4 inbound Pith citation observations for arXiv:2505.06643.

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

pith.paper-citation-record.v1
2505.06643 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:42:00.418318Z

measured 59 of 59 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:39:07.083536Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T11:13:20.768933Z

Reference resolution

55 of 55 outbound references displayed

  • verified exact0
  • verified fuzzy15
  • unresolved38
  • parse uncertain1
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 918c27e9-10b9-4f54-8c9b-2dab2c2d1703 · outbound

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

Practical Reasoning Interruption Attacks on Reasoning Large Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 1

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source=pdf_text observed=2026-08-15T22:42:00.105541Z digest=sha256:4f53b5021ecc4363e77ca96842c9400182671cff4c12beebf143e7ce13b6a9c2

Observation fb01fbc7-1487-4322-acb7-24c1176eb74a · outbound

This paper cites Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

Reference 2

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source=pdf_text observed=2026-08-15T22:42:00.112887Z digest=sha256:8649a36824eaeeaeabc3a4714268c42206df8894ed884251f6b4b01f38a1d1a6

Observation 688c7806-a106-4a94-ae0c-add79398473f · outbound

This paper cites From System 1 to System 2: A Survey of Reasoning Large Language Models.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models From System 1 to System 2: A Survey of Reasoning Large Language Models

Reference 3

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source=pdf_text observed=2026-08-15T22:42:00.121350Z digest=sha256:83dc7c9ac5f537f60cfeb7419ac7566b977460b5957c7e687bcab32431ea592f

Observation 50907ca7-8b0d-4c1c-8f48-ada67edd8d4f · outbound

This paper cites Demystifying Long Chain-of-Thought Reasoning in LLMs.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Demystifying Long Chain-of-Thought Reasoning in LLMs

Reference 4

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source=pdf_text observed=2026-08-15T22:42:00.128635Z digest=sha256:bd38b464d9cfe7112ad5d14aaa8e5d1dc7a57be9653e772f51a02f02f1c2a4a2

Observation 672d5fea-1d5d-4078-8d30-c5b570953336 · outbound

This paper cites SafeChain: Safety of Language Models with Long Chain-of-Thought Reasoning Capabilities.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models SafeChain: Safety of Language Models with Long Chain-of-Thought Reasoning Capabilities

Reference 5

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source=pdf_text observed=2026-08-15T22:42:00.137386Z digest=sha256:3929175fd5cd01964b38169a3d4adcb09f44c1263642f905fde12481021a5222

Observation e89948ca-14cb-466a-bcaf-c2f10fd549c1 · outbound

This paper cites Towards Understanding the Safety Boundaries of DeepSeek Models: Evaluation and Findings.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Towards Understanding the Safety Boundaries of DeepSeek Models: Evaluation and Findings

Reference 6

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source=pdf_text observed=2026-08-15T22:42:00.143478Z digest=sha256:5f358f2c3f620a17c7e532682ec9aeca414b89e0678bce484786221c6548dada

Observation 5ef31bec-e2ce-4c7f-929a-f8b5b8b3374b · outbound

This paper cites RealSafe-R1: Safety-Aligned DeepSeek-R1 without Compromising Reasoning Capability.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models RealSafe-R1: Safety-Aligned DeepSeek-R1 without Compromising Reasoning Capability

Reference 7

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source=pdf_text observed=2026-08-15T22:42:00.151285Z digest=sha256:d9a1063cec6645181785b6a4e02ee9d3a8a88817097a0efd5babeec5daed627e

Observation 5b73d6e8-2592-4f58-95f0-be3cd567a42a · outbound

This paper cites A Mousetrap: Fooling Large Reasoning Models for Jailbreak with Chain of Iterative Chaos.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models A Mousetrap: Fooling Large Reasoning Models for Jailbreak with Chain of Iterative Chaos

Reference 8

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source=pdf_text observed=2026-08-15T22:42:00.160331Z digest=sha256:626d541e9461c4c190b479420cc28a2e2f0ea413547ba514ebe1d22bd5d217d3

Observation 83f850e0-db6c-498e-87ae-9515699d605f · outbound

This paper cites Process or Result? Manipulated Ending Tokens Can Mislead Reasoning LLMs to Ignore the Correct Reasoning Steps.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Process or Result? Manipulated Ending Tokens Can Mislead Reasoning LLMs to Ignore the Correct Reasoning Steps

Reference 9

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source=pdf_text observed=2026-08-15T22:42:00.167694Z digest=sha256:2a8a786a485b21edf32b65ec82013127d067955c16baafef2e68e0ef6a764eea

Observation dae0c81d-3dff-4a1c-a3d8-ba966d9a7ba9 · outbound

This paper cites Formalizing and benchmarking prompt injection attacks and defenses.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Formalizing and benchmarking prompt injection attacks and defenses

Reference 10

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verified fuzzy
raw_fallback, observed 2026-08-15T22:42:01.746475Z

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=2026-08-15T22:42:00.173480Z digest=sha256:23b9c9a402f5d93dfc48393c443ff3b124da3b6d28b6f77985763e625b0fe1fc

Observation 13c51b55-ce65-4b43-9bfa-8a5fa529be5b · outbound

This paper cites Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection

Reference 11

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source=pdf_text observed=2026-08-15T22:42:00.179219Z digest=sha256:4ead0563acacc6747a60b21e2456b81f6bedd108ba4fa4b06449de39411441e3

Observation 7f92387a-72b4-4fe3-a6cd-a94a0ba01c7c · outbound

This paper cites InjecAgent: Benchmarking indirect prompt injections in tool-integrated large language model agents.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models InjecAgent: Benchmarking indirect prompt injections in tool-integrated large language model agents

Reference 12

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source=pdf_text observed=2026-08-15T22:42:00.188796Z digest=sha256:c363cad8ef78ddf5824eecd9183a63d3a3cbaece5f779bd8e29cf7701fd58bd6

Observation d14e42d6-b3a5-4037-aee4-50fcbf872fb6 · outbound

This paper cites Token-Efficient Prompt Injection Attack: Provoking Cessation in LLM Reasoning via Adaptive Token Compression.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Token-Efficient Prompt Injection Attack: Provoking Cessation in LLM Reasoning via Adaptive Token Compression

Reference 13

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source=pdf_text observed=2026-08-15T22:42:00.193978Z digest=sha256:da361d25f7c5026775205b58444f677b2a36fdf1b4377dfa142b2cc7e10aa6ce

Observation 0c23b940-bdde-4b2f-ba9d-0f947006ff0c · outbound

This paper cites Don’t listen to me: understanding and exploring jailbreak prompts of large language models.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Don’t listen to me: understanding and exploring jailbreak prompts of large language models

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-15T22:42:01.729305Z

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=2026-08-15T22:42:00.199211Z digest=sha256:1415d268aabacb071094a210a99a9de1d85dffd4f25aa7e12f5a5dd5c732ae5d

Observation a7ee9bb4-15aa-4fce-8c72-81db74cc3ee9 · outbound

This paper cites The hidden risks of large reasoning models: A safety assessment of r1.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models The hidden risks of large reasoning models: A safety assessment of r1

Reference 15

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source=pdf_text observed=2026-08-15T22:42:00.204376Z digest=sha256:5a1232ecb8ef306dd14a26b72fcaaa6525fddf200576cb029ed0ae138ea2d190

Observation f5164bc4-43f2-4418-8d23-8688c0388c6b · outbound

This paper cites A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment

Reference 16

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source=pdf_text observed=2026-08-15T22:42:00.209373Z digest=sha256:1fb3f7dce4ae909a04ca872b9be64cf834040effa8a0b6bfe20011f2f38e3ba6

Observation 4d2efd76-cab7-4178-86dd-3267e9b1cf61 · outbound

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

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Prompt Injection attack against LLM-integrated Applications

Reference 17

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source=pdf_text observed=2026-08-15T22:42:00.214845Z digest=sha256:65c5e02eb8c4350a18acb29009f6f5af13c15987bb3f2153e8c098eb0dd9848b

Observation 16a5338f-783f-4b6c-affa-7540ac4846f9 · outbound

This paper cites Breaking the Prompt Wall (I): A Real-World Case Study of Attacking ChatGPT via Lightweight Prompt Injection.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Breaking the Prompt Wall (I): A Real-World Case Study of Attacking ChatGPT via Lightweight Prompt Injection

Reference 18

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source=pdf_text observed=2026-08-15T22:42:00.220522Z digest=sha256:311b0d85b33e98972ace45d7c56f3e601da2c87166135ca5f13e832d2f4cda68

Observation 7e6bc4a6-ca5c-47d9-9320-2118a09da6a8 · outbound

This paper cites Fun-tuning: Characterizing the Vulnerability of Proprietary LLMs to Optimization-based Prompt Injection Attacks via the Fine-Tuning Interface.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Fun-tuning: Characterizing the Vulnerability of Proprietary LLMs to Optimization-based Prompt Injection Attacks via the Fine-Tuning Interface

Reference 19

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source=pdf_text observed=2026-08-15T22:42:00.226992Z digest=sha256:bb2d85194e8d27c205364a5c9dbb05d4475b8278cfba435a3da65fe1ac36a2df

Observation 7358d651-b61d-46ae-bd09-2714bf2e5c3f · outbound

This paper cites do anything now.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models do anything now

Reference 20

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source=pdf_text observed=2026-08-15T22:42:00.232989Z digest=sha256:72d94296fe09454fd71853db799ae7e5009ee690f31adc521e2ecfe60e01e191

Observation f08a9cfc-cbc7-452e-9ac5-dfda9bb98e6d · outbound

This paper cites ToolSword: Unveiling safety issues of large language models in tool learning across three stages.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models ToolSword: Unveiling safety issues of large language models in tool learning across three stages

Reference 21

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raw_fallback, observed 2026-08-15T22:42:01.712621Z

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=2026-08-15T22:42:00.238233Z digest=sha256:277447442badb6a114d888b30d35ebdfd6cb4db71bf3a5aa6b582a1c666be264

Observation c620d079-1a37-4054-b174-9b8663f35c5a · outbound

This paper cites Virtual context enhancing jailbreak attacks with special token injection.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Virtual context enhancing jailbreak attacks with special token injection

Reference 22

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source=pdf_text observed=2026-08-15T22:42:00.251784Z digest=sha256:7524670eab34d1c457fef8a2684b2d0715e9683b517bc4f8df4a8dcd58869829

Observation ca686c04-74ac-43e9-8887-265fd6800e3a · outbound

This paper cites H-CoT: Hijacking the Chain-of-Thought Safety Reasoning Mechanism to Jailbreak Large Reasoning Models, Including OpenAI o1/o3, DeepSeek-R1, and Gemini 2.0 Flash Thinking.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models H-CoT: Hijacking the Chain-of-Thought Safety Reasoning Mechanism to Jailbreak Large Reasoning Models, Including OpenAI o1/o3, DeepSeek-R1, and Gemini 2.0 Flash Thinking

Reference 23

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source=pdf_text observed=2026-08-15T22:42:00.256951Z digest=sha256:e087c5d732e03533dd903a534a7ed7915403895bce64def19c727ecd8b328415

Observation 75924e65-9be0-4841-9098-147295580140 · outbound

This paper cites Easypqc: Verifying post-quantum cryptography.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Easypqc: Verifying post-quantum cryptography

Reference 24

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source=pdf_text observed=2026-08-15T22:42:00.262328Z digest=sha256:7b046795882b212198c5a13df61f68e5faedfc107975f9d8f77773ae413fda56

Observation 23a96bca-9d7c-42c1-9d26-0c29b8d33c85 · outbound

This paper cites A survey on post-quantum public-key signature schemes for secure vehicular communications.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models A survey on post-quantum public-key signature schemes for secure vehicular communications

Reference 25

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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=2026-08-15T22:42:00.267553Z digest=sha256:83d4a99cb7ee91547951df88ccb1fad1c8f74f303303abd934515d53adf70b91

Observation 47ff3417-f9ff-4fce-b2c7-4600cc016763 · outbound

This paper cites Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies

Reference 26

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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=2026-08-15T22:42:00.278072Z digest=sha256:e1ea4c4a91e4dd61668809b227a5d5b5d9c74d3e14e160b0ef1764542619f6c5

Observation 59db79c5-bec9-4e2d-9a46-562ad4495cbd · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Training Verifiers to Solve Math Word Problems

Reference 27

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source=pdf_text observed=2026-08-15T22:42:00.283513Z digest=sha256:cc4e1281d9f9e876299ff0d5bbdd43fd7ebbda02d9ea5c505711b4769b9e1f24

Observation 4bf15bc9-1d1a-44ec-b2d3-04e449b6a77c · outbound

This paper cites Program induction by rationale generation: Learning to solve and explain algebraic word problems.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Program induction by rationale generation: Learning to solve and explain algebraic word problems

Reference 28

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source=pdf_text observed=2026-08-15T22:42:00.289487Z digest=sha256:00643e9599b4c14eaa44b14f7ca13512fe38634f5c968e0109bbead26f62cb09

Observation 7de4823b-0964-44ed-8898-19d908f2c403 · outbound

This paper cites ReConcile: Round-table conference improves reasoning via consensus among diverse LLMs.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models ReConcile: Round-table conference improves reasoning via consensus among diverse LLMs

Reference 29

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source=pdf_text observed=2026-08-15T22:42:00.296641Z digest=sha256:210c360279433511d91232a0640238002893f6b6495cd4836967f0007a882797

Observation b2124abe-ea9e-4b22-9c88-da1c9d342391 · outbound

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

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Wildguard: Open one-stop moderation tools for safety risks, jailbreaks, and refusals of LLMs

Reference 30

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verified fuzzy
raw_fallback, observed 2026-08-15T22:42:01.665407Z

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=2026-08-15T22:42:00.301954Z digest=sha256:fb3b10a30f4383741e5b7746cc038748eae3f2fb1a579ae0b5c37156f610cb4b

Observation bb38b34a-7b6c-481c-a24c-c30d61f037c9 · outbound

This paper cites Beyond the Last Answer: Your Reasoning Trace Uncovers More than You Think.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Beyond the Last Answer: Your Reasoning Trace Uncovers More than You Think

Reference 31

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source=pdf_text observed=2026-08-15T22:42:00.306901Z digest=sha256:8a91edae5b8436fc362ced164190d0aff500777385510b6d6ea14cd5ec03c342

Observation e6a43b2e-61fc-40a1-96fe-470ba32d1463 · outbound

This paper cites an unresolved cited work.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Unresolved cited work

Reference 34

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

source=pdf_text observed=2026-08-15T22:42:00.312221Z digest=sha256:4e748624a3a1b2f136cb978cc188e6dbdbd0210289eceaa9ef62f7b28b4251d3

Observation f7f1517a-1641-47bb-8ef2-42589644fbc6 · outbound

This paper cites - **Storage**: The AES key is kept in memory temporarily and never written to disk unencrypted.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models - **Storage**: The AES key is kept in memory temporarily and never written to disk unencrypted

Reference 35

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raw_fallback, observed 2026-08-15T22:42:01.632590Z

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

source=pdf_text observed=2026-08-15T22:42:00.317410Z digest=sha256:4c01f1e9fb632e1187f7c785ab132def6ce818faf25e6df483797508203cdf79

Observation 4da9bd98-9dbb-4c71-bfda-189d48ed2913 · outbound

This paper cites an unresolved cited work.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Unresolved cited work

Reference 36

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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=2026-08-15T22:42:00.322872Z digest=sha256:76d250f35315ccc243af3d05eb0e608a4905f9d2dad30418877f58a94b10d499

Observation 19749657-483b-4d80-b9ea-63af7fdb7704 · outbound

This paper cites an unresolved cited work.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Unresolved cited work

Reference 37

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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=2026-08-15T22:42:00.328879Z digest=sha256:7f9fdbf389213a8240e2220f7d29cf5c88efcfe597e521668beb4b8e0fca4f3f

Observation 9ed236b9-f8cd-4777-90eb-c2a0b99a576d · outbound

This paper cites an unresolved cited work.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Unresolved cited work

Reference 38

Resolution
unresolved
raw_fallback, observed 2026-08-15T22:42:01.581036Z

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=2026-08-15T22:42:00.333797Z digest=sha256:5000535899f38c7a6973607552450c8fbc599c1c4d05a70c73514e6da0174e8e

Observation 9b4d2417-cd26-4903-901f-c8ca56fb5353 · outbound

This paper cites an unresolved cited work.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Unresolved cited work

Reference 39

Resolution
unresolved
raw_fallback, observed 2026-08-15T22:42:01.563182Z

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=2026-08-15T22:42:00.338802Z digest=sha256:c310e867b9205a9781865f6933a5c48b0816f70e958e9c59e6aa60ed162c4462

Observation 5ec7f25e-043e-4b63-82cd-bace3f46b713 · outbound

This paper cites an unresolved cited work.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-15T22:42:01.546612Z

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=2026-08-15T22:42:00.343931Z digest=sha256:4b9d96224aa6df2e0ece3e275479dcbb8e52147227cad99d64f99ddabcde42bd

Observation 767bf861-4dfd-4568-abe4-0ebe19d3723c · outbound

This paper cites an unresolved cited work.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Unresolved cited work

Reference 41

Resolution
unresolved
raw_fallback, observed 2026-08-15T22:42:01.529558Z

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=2026-08-15T22:42:00.348628Z digest=sha256:85fe5783df19bf7edfd3a72ef221a90739ff1bb408c79f1f61c8d10ba47c4fb8

Observation 35c9392a-35e6-47f0-b4a8-af1723874329 · outbound

This paper cites Wait, but the unique ID is important.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Wait, but the unique ID is important

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:42:01.512184Z

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=2026-08-15T22:42:00.353465Z digest=sha256:b3d992e4c9a0ac816f685dc33104b109a6de3921253bbc3ad4c4f7555623c713

Observation a6cacbde-f6dc-4c9e-b60b-83a5f50e08d0 · outbound

This paper cites an unresolved cited work.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Unresolved cited work

Reference 43

Resolution
unresolved
raw_fallback, observed 2026-08-15T22:42:01.496235Z

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=2026-08-15T22:42:00.358177Z digest=sha256:09ea29f6a8a62f6c60f32bbc67e5927d0ae92526ee4e9d3e3f352b2347d30697

Observation e0f80c7d-33e1-4eb4-9e23-ac064194826a · outbound

This paper cites an unresolved cited work.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Unresolved cited work

Reference 44

Resolution
unresolved
raw_fallback, observed 2026-08-15T22:42:01.480472Z

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=2026-08-15T22:42:00.362820Z digest=sha256:a99f220b419aa69e0d0245688ab47751c5b1101a9bdca2ea81ccef34e21f9ece

Observation 7ed6c3c0-63c5-4e17-aa00-2aef97e532d8 · outbound

This paper cites Cybersecurity expertise should be used to protect systems, not exploit them.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Cybersecurity expertise should be used to protect systems, not exploit them

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:42:01.465214Z

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=2026-08-15T22:42:00.367647Z digest=sha256:4df12285e4a8273e92e4f062acf1f46fff38d85dd19f04848df6ac05e1102c57

Observation 6e2d5f44-4469-4b38-9fe5-7835d77f31fb · outbound

This paper cites - **Mitigation**: Strict vendor risk assessments and network segmentation to isolate third-party access.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models - **Mitigation**: Strict vendor risk assessments and network segmentation to isolate third-party access

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:42:01.448730Z

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=2026-08-15T22:42:00.372422Z digest=sha256:137fcc6948242b807f340e1f8a8b1dbdf917a8ee235676add533c38535e72ef5

Observation aec004b4-86e7-418e-a35d-3a555353b69e · outbound

This paper cites - **Mitigation**: Patch management programs, intrusion detection systems (IDS), and regular penetration testing.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models - **Mitigation**: Patch management programs, intrusion detection systems (IDS), and regular penetration testing

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:42:01.431702Z

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=2026-08-15T22:42:00.377374Z digest=sha256:28e30483ad94189afefec2b988e18132fa37a3e0ff2790f52eed8cf21f16f7fc

Observation 49609f74-4e12-4896-97cc-4c0c5ef1eac6 · outbound

This paper cites - **Mitigation**: Verification protocols (e.g., call-back procedures) and security awareness drills.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models - **Mitigation**: Verification protocols (e.g., call-back procedures) and security awareness drills

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:42:01.415199Z

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=2026-08-15T22:42:00.382004Z digest=sha256:3221c9b934a8950e8c198080f9cfe13db8cb84f492d58e9df2fab4173d54659f

Observation 7b02d504-56c4-42a8-b644-415f22ba126c · outbound

This paper cites - **Mitigation**: Background checks, access controls, and monitoring for anomalous behavior.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models - **Mitigation**: Background checks, access controls, and monitoring for anomalous behavior

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:42:01.399928Z

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=2026-08-15T22:42:00.386666Z digest=sha256:dc5facaf75731318387de2e8e9b787ec89ac62a4d3b4db83808a0be65136e1af

Observation 08bafe9b-1540-4a88-9840-abaec94dbe9d · outbound

This paper cites a = <|end_of_thinking|> and b = m.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models a = <|end_of_thinking|> and b = m

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:42:01.383645Z

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=2026-08-15T22:42:00.392714Z digest=sha256:dd0599b5069f80856a1bffd46dee84df798aff7f91f3fab4f3c0d0c88114586d

Observation 83e7fa74-8875-43fd-b98a-eb331874fd00 · outbound

This paper cites an unresolved cited work.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Unresolved cited work

Reference 52

Resolution
unresolved
raw_fallback, observed 2026-08-15T22:42:01.348231Z

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=2026-08-15T22:42:00.402826Z digest=sha256:4eb71d2139a4fda4acb54c339b7e60879e58c2ca5d6e4c30e949565725dece56

Observation 2b8c34fd-348e-4357-bc52-97be0b2b6822 · outbound

This paper cites an unresolved cited work.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Unresolved cited work

Reference 53

Resolution
unresolved
raw_fallback, observed 2026-08-15T22:42:01.332906Z

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=2026-08-15T22:42:00.408152Z digest=sha256:d918ab34b11c95f1c16a3dd1e8abbb08be58b217b03f85dc97e9ff529226d070

Observation 0a83a647-bd6a-422d-be4e-212dc2a4edf5 · outbound

This paper cites an unresolved cited work.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Unresolved cited work

Reference 54

Resolution
parse uncertain
raw_fallback, observed 2026-08-15T22:42:01.315644Z

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=2026-08-15T22:42:00.412819Z digest=sha256:f42e0b61cca8c6c16b81c1fa4a1b2370b39eb66ded2b1eabfd42090b3f639f2e

Observation bffd0060-9d93-4590-95a0-68f765270d1e · outbound

This paper cites **Answer:** The ciphertext produced is 901.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models **Answer:** The ciphertext produced is 901

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:42:01.296742Z

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=2026-08-15T22:42:00.418318Z digest=sha256:9a6ce80f11f1755de48f5bb0dfa25967056674692ec85ef54f72f9a8791b413d

Observation 3a2b3654-10d6-4b39-a6cc-c7fd045629d7 · outbound

This paper cites So maybe ’b’ is the letter ’m’, which we need to convert to a numerical key.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models So maybe ’b’ is the letter ’m’, which we need to convert to a numerical key

Reference 109

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:42:01.366081Z

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=2026-08-15T22:42:00.398264Z digest=sha256:8a66601fb778942e177e9aac0b9ef8741834c6b87d8cb10289767c9da582920b

Observation 13a3eaa6-e9a9-43a4-ab4f-ed0fffad3d1b · outbound

This paper cites an unresolved cited work.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models Unresolved cited work

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-15T22:42:00.272815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:42:00.272815Z digest=sha256:8c7a58111886330bb002777760228fbab02e4b1c994daf9b8ac24131c62db054

Observation 5db5e5d3-e79a-4dcb-8ccb-8d4e1ae6304a · outbound

This paper cites doi: 10.18653/v1/2024.acl-long.119.

Practical Reasoning Interruption Attacks on Reasoning Large Language Models doi: 10.18653/v1/2024.acl-long.119

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-15T22:42:00.245371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:42:00.245371Z digest=sha256:485c726d4fa6fb15811bfc1e274ddafa7542bbffd7c65ded08de0c0cfeaf4c93

Pith citing papers

Observation d27f5c5f-bee2-4cef-951e-5f3710714808 · inbound

Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models cites this paper.

Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models Practical Reasoning Interruption Attacks on Reasoning Large Language Models

Reference 149

Resolution
verified exact
arxiv_id, observed 2026-05-12T08:40:42.096626Z

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=2026-05-12T08:40:40.910461Z digest=sha256:f833598ede5b1e345545f00dfff2249f0e2996314caa8ae5b717022f8cb374c7

Observation cf20b79f-360e-46a0-be80-feecc9571d42 · inbound

A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models cites this paper.

A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models Practical Reasoning Interruption Attacks on Reasoning Large Language Models

Reference 172

Resolution
unresolved
no resolver link, observed 2026-08-05T10:39:07.083536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:39:07.083536Z digest=sha256:75320c62c9d8d8bfe807428a621c62478f34c65a017baf56400761584e30a30b

Observation a28c82bb-9b58-4c69-ba79-aa075536a800 · inbound

Conflicts Make Large Reasoning Models Vulnerable to Attacks cites this paper.

Conflicts Make Large Reasoning Models Vulnerable to Attacks Practical Reasoning Interruption Attacks on Reasoning Large Language Models

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-11T06:51:46.246752Z

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=2026-05-10T17:24:25.255602Z digest=sha256:ba44cae72105d1cc088ea5311a86f1a5d5d5cb4fb0da9da7fa2d0969823fbf64

Observation 59129d0c-ad80-4dbe-9c5c-bf0cf52a7842 · inbound

ReasonBreak: Probing Vulnerabilities in Reasoning-Enabled Vision-Language-Action Models for Autonomous Driving cites this paper.

ReasonBreak: Probing Vulnerabilities in Reasoning-Enabled Vision-Language-Action Models for Autonomous Driving Practical Reasoning Interruption Attacks on Reasoning Large Language Models

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-06-29T11:13:20.770383Z

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=2026-06-29T11:10:46.269185Z digest=sha256:fa9541a34cff694da5b4e59382655b420302c70ce2125335ea59f56a9f95eb15