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Paper Citation Record · LEDGER

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection

As of 21 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2508.07139.

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

pith.paper-citation-record.v1
2508.07139 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:24:23.461464Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

  • verified exact4
  • verified fuzzy7
  • unresolved31
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7391a8be-7c97-42c9-bb97-0298bf8a5c69 · outbound

This paper cites Figure is the first-of-its-kind ai robotics company bringing a general purpose humanoid to life.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Figure is the first-of-its-kind ai robotics company bringing a general purpose humanoid to life

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T22:24:23.338114Z digest=sha256:59c80d259cac177809889df443439144c4a31193d1ce62627e2dac4ed9911d1b

Observation 5d1f4862-45a8-48fe-a310-10ff7bc6b967 · outbound

This paper cites LLM4SR: A Survey on Large Language Models for Scientific Research.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection LLM4SR: A Survey on Large Language Models for Scientific Research

Reference 2

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source=pdf_text observed=2026-08-05T22:24:23.341478Z digest=sha256:94574ef43ed1021dd6817dc46702f1acdff411758fe400d0169a4ab5bfd7d33c

Observation 6b90a4a2-f2f7-499b-a985-a59f4be0b0a1 · outbound

This paper cites Programming with AI: Evaluating ChatGPT, Gemini, AlphaCode, and GitHub Copilot for Programmers.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Programming with AI: Evaluating ChatGPT, Gemini, AlphaCode, and GitHub Copilot for Programmers

Reference 3

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local_arxiv, observed 2026-08-05T22:24:24.102705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T22:24:23.344893Z digest=sha256:f9a157095faf52b39f8675f823611144f7853ef2e9657790aa5c0ca27d9ebee7

Observation 5638842d-1e1e-4241-8613-6e8323b004b9 · outbound

This paper cites Transparency & con- tent moderation.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Transparency & con- tent moderation

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T22:24:23.348342Z digest=sha256:485865c93ceee78cb1975bc0030fd3955c8d0efa0bc2ebf127710c1a0636012b

Observation e7308aa5-db82-4b9c-8e55-a541a0873104 · outbound

This paper cites Safety in Large Reasoning Models: A Survey.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Safety in Large Reasoning Models: A Survey

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:24:23.351363Z digest=sha256:c9efcebf986f424b6ba00bcf6f6617f51aaa8afa0e5fe9e7edc0c0d06a650ec1

Observation d9ced9b5-dfba-486c-9578-eca36764c4f8 · outbound

This paper cites Generative AI Security: Challenges and Countermeasures.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Generative AI Security: Challenges and Countermeasures

Reference 6

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verified exact
local_arxiv, observed 2026-08-05T22:24:24.081871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T22:24:23.354632Z digest=sha256:7eefe0f88b38dbb446cddea0cc65f57edc20a3b025c79799acfeff07a4b7e2f4

Observation 8e786645-fec1-4eaa-b3f5-521d89246074 · outbound

This paper cites An Early Categorization of Prompt Injection Attacks on Large Language Models.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection An Early Categorization of Prompt Injection Attacks on Large Language Models

Reference 7

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:24:23.358065Z digest=sha256:c8f7b287a468ac44701ab344db2fd12368c987cd2cf67c6447e42b9c9e945d59

Observation 25f5f323-7136-4f16-b93b-e827df8a1a61 · outbound

This paper cites A Comprehensive Study of Jailbreak Attack versus Defense for Large Language Models.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection A Comprehensive Study of Jailbreak Attack versus Defense for Large Language Models

Reference 8

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source=pdf_text observed=2026-08-05T22:24:23.361494Z digest=sha256:afaa90e4058f480d5fcf8a068dd487585dfc57433acf60bf2949915b569945ae

Observation aeb3f901-57ff-4af9-acb5-5111c7461fc8 · outbound

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

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 9

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source=pdf_text observed=2026-08-05T22:24:23.364780Z digest=sha256:ba3abe3a3d2507855f99c5091073c3ab3aebdccaf8b10e6e25b7271349ab46a3

Observation f9154070-1a34-4e73-923e-6ee24ea89336 · outbound

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

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 10

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source=pdf_text observed=2026-08-05T22:24:23.367757Z digest=sha256:c140af86144c3a9482a819ddcd2a78bb21288deb642272c184361890118edb8e

Observation 7d61f6a0-21f0-49ac-8c78-8c4f567ec487 · outbound

This paper cites Training language models to follow instructions with human feedback.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Training language models to follow instructions with human feedback

Reference 11

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:24:23.370952Z digest=sha256:fcba409028138e97436a473e6f773f4b921e967b26a69a5bf5d50248226809dd

Observation 36f81cd8-a2af-4bc7-9036-38570bad6593 · outbound

This paper cites Automated Progressive Red Teaming.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Automated Progressive Red Teaming

Reference 12

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source=pdf_text observed=2026-08-05T22:24:23.374081Z digest=sha256:61f2861592e71946d2cd2f816974300013eb12ff679e79d0d741ef4e36e9ddd0

Observation f5881f87-bd05-4c12-9048-d554c8f0467d · outbound

This paper cites Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:24:23.377564Z digest=sha256:10c20ebcf2b32af6a8c5a35737694d347da678a4493c223112d8b4809e4063cb

Observation 4091594c-9b3b-432b-a662-ee436b4b52d9 · outbound

This paper cites Guardreasoner: Towards reasoning-based llm safeguards.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Guardreasoner: Towards reasoning-based llm safeguards

Reference 14

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source=pdf_text observed=2026-08-05T22:24:23.380458Z digest=sha256:c8e0e85e5fc223302c5c970218754b457f7bededa8040accb3050c13f3dcb909

Observation 27b1f05c-b712-400d-9fbf-e9f05964dca7 · outbound

This paper cites Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming

Reference 15

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source=pdf_text observed=2026-08-05T22:24:23.383158Z digest=sha256:69eab4666cd232002aa41243047fb55c156eef3fb02f7527a119e8313dc509ef

Observation a14cefd3-7265-4657-a8ff-e57cf764095e · outbound

This paper cites Detecting Language Model Attacks with Perplexity.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Detecting Language Model Attacks with Perplexity

Reference 16

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:24:23.386156Z digest=sha256:8ea8e86e1bdc600b37db65999949bd49b3603c1e4efa8258b5fdc1213a921a51

Observation cadf9baa-dbf5-470b-94e7-9452b8d9df02 · outbound

This paper cites Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks

Reference 17

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:24:23.388868Z digest=sha256:8d7bd065de70888b6ba39f09a35d22349eb6b617ce3a76073beea3126ddca8e7

Observation eae6fbe7-eee7-4c94-98b8-9d683797b3b1 · outbound

This paper cites Darkmind: Latent chain-of-thought backdoor in customized llms.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Darkmind: Latent chain-of-thought backdoor in customized llms

Reference 18

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source=pdf_text observed=2026-08-05T22:24:23.391656Z digest=sha256:091856a3d0200d8ffcd3fdc39e7294e9c87de8582b80e9aa6254c476471f59df

Observation 65c8ba88-8154-4c93-9a36-c3bc90625b88 · outbound

This paper cites FlipAttack: Jailbreak LLMs via Flipping.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection FlipAttack: Jailbreak LLMs via Flipping

Reference 19

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source=pdf_text observed=2026-08-05T22:24:23.394303Z digest=sha256:fd404966998446f314bfa6256a229496f7ac880d33305561d7a7e3ce8c5a6c8d

Observation a0fce363-85d8-440d-a75a-6b4fbf955769 · outbound

This paper cites Cognitive Overload Attack:Prompt Injection for Long Context.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Cognitive Overload Attack:Prompt Injection for Long Context

Reference 20

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source=pdf_text observed=2026-08-05T22:24:23.397460Z digest=sha256:374455efa0727fde7bfb36f0116022b29ca1e448b3983addc698328e1c034bb2

Observation c9d93443-48d4-44b2-a70e-26cad4fef8f2 · outbound

This paper cites Defending Large Language Models Against Jailbreaking Attacks Through Goal Prioritization.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Defending Large Language Models Against Jailbreaking Attacks Through Goal Prioritization

Reference 21

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source=pdf_text observed=2026-08-05T22:24:23.400378Z digest=sha256:f6d7e5fbea350399291599f5add4f62905e97827333356ebe6b932ee7a2f2198

Observation c02eec3d-2f04-4f2e-9a7c-0af33298520a · outbound

This paper cites Certifying LLM Safety against Adversarial Prompting.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Certifying LLM Safety against Adversarial Prompting

Reference 22

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source=pdf_text observed=2026-08-05T22:24:23.403420Z digest=sha256:3f7944b391f2ce9160540881b4938e06ea68b77e003f9004d86212c1238ac8ac

Observation c18ad7e4-5ccd-4f0c-9ba0-097ab8d99443 · outbound

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

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection StruQ: Defending Against Prompt Injection with Structured Queries

Reference 23

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source=pdf_text observed=2026-08-05T22:24:23.406310Z digest=sha256:769626da543af3e1ac2d1317b2be809766426c087d7b3a2d45ed2329a178be73

Observation 6a85e04b-2048-4be5-8a44-fb8cd3b497a4 · outbound

This paper cites Chain-of-Defensive-Thought: Structured Reasoning Elicits Robustness in Large Language Models against Reference Corruption.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Chain-of-Defensive-Thought: Structured Reasoning Elicits Robustness in Large Language Models against Reference Corruption

Reference 24

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source=pdf_text observed=2026-08-05T22:24:23.409209Z digest=sha256:278da3036e7934346c0de7464f50248bdff1513add7d47f8c17031c11080efc4

Observation 5914d115-67d8-40fc-9bc3-059d850101bb · outbound

This paper cites Bergeron: Combating Adversarial Attacks through a Conscience-Based Alignment Framework.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Bergeron: Combating Adversarial Attacks through a Conscience-Based Alignment Framework

Reference 25

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source=pdf_text observed=2026-08-05T22:24:23.412222Z digest=sha256:a275ae8963e811255dd5601360e339231f960bb0576de939f4e92950cedbba38

Observation 0b083229-859f-45e9-ba9a-04ba2282bedb · outbound

This paper cites [WIP] Jailbreak Paradox: The Achilles' Heel of LLMs.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection [WIP] Jailbreak Paradox: The Achilles' Heel of LLMs

Reference 26

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verified exact
local_arxiv, observed 2026-08-05T22:24:23.773604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T22:24:23.415082Z digest=sha256:707f417b9fc5af06221d486c6042871441aa53dcc7296b15d05b8d32daa05cf3

Observation bc9fc4d6-c4ad-4bc0-9fea-29acd3183568 · outbound

This paper cites Inverse scaling in test-time compute.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Inverse scaling in test-time compute

Reference 27

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source=pdf_text observed=2026-08-05T22:24:23.417921Z digest=sha256:bda67b340670d3511382e02d63c25aa01d60434b70baa980952c2f96e6e35944

Observation 292270f7-49e3-409b-9f56-b2d029405619 · outbound

This paper cites AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models

Reference 28

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source=pdf_text observed=2026-08-05T22:24:23.420770Z digest=sha256:99a88309c544510954a426523eee220f8334fc3d450971d5cb827d07a4459c95

Observation 0a29a3b9-574a-4820-b283-be94d39e5285 · outbound

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

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Tree of Attacks: Jailbreaking Black-Box LLMs Automatically

Reference 29

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source=pdf_text observed=2026-08-05T22:24:23.423635Z digest=sha256:56efdd3f6b27cdaf4d3a5cab3d8bda5e4279d1ca4971977252249bf5d3af9377

Observation a0d877c6-550c-49dc-ad53-43e90eb75a82 · outbound

This paper cites Is your prompt safe? inves- tigating prompt injection attacks against open- source llms.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Is your prompt safe? inves- tigating prompt injection attacks against open- source llms

Reference 30

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verified exact
raw_fallback, observed 2026-08-05T22:24:23.671877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T22:24:23.426274Z digest=sha256:2076210759921d45d6788b1c6cd5880901c68535e147699587d4c94c260fa129

Observation 11d2042c-6acd-4c2f-8ba3-21a2845c9a02 · outbound

This paper cites Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations

Reference 31

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source=pdf_text observed=2026-08-05T22:24:23.428703Z digest=sha256:f67239eb0c5f3f46b33461d48b2f0a02e89e74c5ba6aec91cfe355c9d5954b12

Observation 169c30b9-ac90-405b-ad14-488529ed8d30 · outbound

This paper cites Novel uni- versal bypass for all major llms: The pol- icy puppetry prompt injection technique.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Novel uni- versal bypass for all major llms: The pol- icy puppetry prompt injection technique

Reference 32

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verified fuzzy
raw_fallback, observed 2026-08-05T22:24:24.164461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T22:24:23.431436Z digest=sha256:ce36f55866b71d59cf6bf85e53de2c7b4ea924299ecd2ec0d349ab5c062314ec

Observation bc7c92e6-da17-4e6a-bb46-6f84710aca3a · outbound

This paper cites Gasp: Ef- ficient black-box generation of adversarial suf- fixes for jailbreaking llms.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Gasp: Ef- ficient black-box generation of adversarial suf- fixes for jailbreaking llms

Reference 33

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source=pdf_text observed=2026-08-05T22:24:23.437009Z digest=sha256:be0cf0e9f12bfcabfb03fd99e025d168c4de5cbe4bbccfbbd2b3e966cf0832e3

Observation 175940b2-da68-45f0-a1c1-2ea318351b51 · outbound

This paper cites Jailbreakchat.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Jailbreakchat

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:24:24.147077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T22:24:23.439933Z digest=sha256:17003b8829f4f8af9ee0f4964bcfb389539175fedef455d4f6f9f4febf1d9e6a

Observation a2d3994d-878a-40a9-9533-c321ca8cf22f · outbound

This paper cites Gemini loki gem (no limits).

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Gemini loki gem (no limits)

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:24:24.138665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T22:24:23.442501Z digest=sha256:598b504a51dd7cd0849ed4ef12370fcf83b1bd1133fb1447111fd3c7f8f990f5

Observation 3afbdf64-a2d6-4552-8966-43ceb54b7426 · outbound

This paper cites Jailbreaks.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Jailbreaks

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-05T22:24:24.129526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T22:24:23.445028Z digest=sha256:d18426f629a51aabb4318ffff63d8a8d34c51c895d870e622e606c13c0c6f7ad

Observation a0f85fb0-18e7-4f1b-9981-7406b00454ee · outbound

This paper cites SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-05T22:24:23.447796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:24:23.447796Z digest=sha256:721a8be03fc05bf913b2fa6c646fefc362787e98ef8f303ed62a6573144d2f83

Observation f41039d3-9d9d-411b-99f0-e8eda572e6bf · outbound

This paper cites AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-05T22:24:23.450421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:24:23.450421Z digest=sha256:9fe2e33554fea513d689d2727ce33bdfdca6ee90ce8890bd7310415edc6931ea

Observation e78c59b7-4194-4955-bbbb-d5b4e002dda8 · outbound

This paper cites AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-05T22:24:23.453331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:24:23.453331Z digest=sha256:78b4f5f23f565c041af43eacc2711b24746161a17eae2901d22cf4f2ea6e0105

Observation 33142d37-3f13-4dd2-a5dd-8f29cc5f9291 · outbound

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

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-05T22:24:23.455996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:24:23.455996Z digest=sha256:2ffecc93defc4703816d48f2da071a3a9c500af35718772a9cd3be112101471a

Observation 7acea42f-75e7-47b0-adfb-176c818a5870 · outbound

This paper cites Automatic Pseudo-Harmful Prompt Generation for Evaluating False Refusals in Large Language Models.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Automatic Pseudo-Harmful Prompt Generation for Evaluating False Refusals in Large Language Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-05T22:24:23.458700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:24:23.458700Z digest=sha256:0497741cfbc3a59ee444d291410dd3d00fad44ef9666ff1af3016a89ba16fe0b

Observation 38cfd376-935a-4299-bfdd-150911536cc0 · outbound

This paper cites Prompt injections bench- mark.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Prompt injections bench- mark

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T22:24:24.120690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T22:24:23.461464Z digest=sha256:7d7fc53b32bd520e537592016354c84519d77c1a88175bd8e62de462f9752fac

Observation 5768ea9b-0038-4677-b9f1-61873ad621bf · outbound

This paper cites an unresolved cited work.

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection Unresolved cited work

Reference 2025

Resolution
parse uncertain
raw_fallback, observed 2026-08-05T22:24:24.155549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-05T22:24:23.434501Z digest=sha256:a3edfbc77b5adb3286607eadc836c7981ede517f30074c323ed1c0199913e3a0

Pith citing papers

No inbound Pith citation observations are available.