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Paper Citation Record · LEDGER

Safeguarding Large Language Models: A Survey

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 26 inbound Pith citation observations for arXiv:2406.02622.

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

pith.paper-citation-record.v1
2406.02622 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 26 of 26 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 26 of 26 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:58:23.401769Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

8
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ea39e46d-0ebf-4b3c-93e8-b8b0a6da34fe · inbound

Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey cites this paper.

Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey Safeguarding Large Language Models: A Survey

Reference 37

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arxiv_id, observed 2026-05-23T20:58:25.775933Z

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=2026-05-23T20:58:16.237327Z digest=sha256:ff20a7576eb5603f7869aea5e1c94fa0ad7d5d145efaa6aab25944f395e00998

Observation cb7f38ab-7832-4f35-845e-3c7167ec926e · inbound

Challenges in Guardrailing Large Language Models for Science cites this paper.

Challenges in Guardrailing Large Language Models for Science Safeguarding Large Language Models: A Survey

Reference 6

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no resolver link, observed 2026-08-12T21:54:43.918501Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T21:54:43.918501Z digest=sha256:9874e1d0f1fe851076fdcc30bff28d6f5ac0cbe148e67c7ed6ce4eb85d5c03ae

Observation 48ebb702-e662-4876-b6cc-c828a4ae7860 · inbound

A Flexible Large Language Models Guardrail Development Methodology Applied to Off-Topic Prompt Detection cites this paper.

A Flexible Large Language Models Guardrail Development Methodology Applied to Off-Topic Prompt Detection Safeguarding Large Language Models: A Survey

Reference 6

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no resolver link, observed 2026-08-12T17:05:25.192147Z

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source=arxiv_source observed=2026-08-12T17:05:25.192147Z digest=sha256:7a82f6140b19d405d2170b074c0662e5cfe64b5bf5a076a951cba96a66330334

Observation c8989374-b901-4edf-9035-23c83495dff9 · inbound

The TIP of the Iceberg: Revealing a Hidden Class of Task-in-Prompt Adversarial Attacks on LLMs cites this paper.

The TIP of the Iceberg: Revealing a Hidden Class of Task-in-Prompt Adversarial Attacks on LLMs Safeguarding Large Language Models: A Survey

Reference 6

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no resolver link, observed 2026-08-10T13:53:10.933726Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T13:53:10.933726Z digest=sha256:25e6a30814d73204132454e0bc5aec1ae1ed04788aeffcbb9c5d0da32661cfb3

Observation 32d914df-89e9-4282-a656-032a9425cd88 · inbound

Bridging HCI and AI Research for the Evaluation of Conversational SE Assistants cites this paper.

Bridging HCI and AI Research for the Evaluation of Conversational SE Assistants Safeguarding Large Language Models: A Survey

Reference 26

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no resolver link, observed 2026-08-08T11:20:48.457016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T11:20:48.457016Z digest=sha256:7c34bd2e1311d94fe07d522e17885ffd4aa31f35ae90ab93b36b1bf20bdc9da1

Observation 598a2eba-0a84-4d01-901b-00357cb2ee92 · inbound

AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents cites this paper.

AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents Safeguarding Large Language Models: A Survey

Reference 11

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arxiv_id, observed 2026-05-14T21:24:32.636615Z

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=2026-05-14T21:24:32.615129Z digest=sha256:da42d7987b3fdab3b27c3f83a0a7c5194519395db8f514d63be63419ed3296ac

Observation 18cf9d4e-b37d-4332-9ad9-c8d27532a5c4 · inbound

Bias Analysis and Mitigation through Protected Attribute Detection and Regard Classification cites this paper.

Bias Analysis and Mitigation through Protected Attribute Detection and Regard Classification Safeguarding Large Language Models: A Survey

Reference 9

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no resolver link, observed 2026-08-16T11:58:23.401769Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T11:58:23.401769Z digest=sha256:e62585976a504b77bffadf888184a2c03144a2c560d0e7dc7735f420a24027c6

Observation 9b5100ce-d1a3-468b-a8c3-d0c10efc8503 · inbound

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

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment Safeguarding Large Language Models: A Survey

Reference 53

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no resolver link, observed 2026-08-16T11:24:12.103750Z

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source=pdf_text observed=2026-08-16T11:24:12.103750Z digest=sha256:399628408bc4b94c5979766be608a8ddfddadf8e8bd04a26adb5c105643774d1

Observation 5123397a-c1c2-43cf-9d1c-94c9042bd145 · inbound

The Tower of Babel Revisited: Multilingual Jailbreak Prompts on Closed-Source Large Language Models cites this paper.

The Tower of Babel Revisited: Multilingual Jailbreak Prompts on Closed-Source Large Language Models Safeguarding Large Language Models: A Survey

Reference 15

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no resolver link, observed 2026-08-15T20:41:29.119914Z

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source=pdf_text observed=2026-08-15T20:41:29.119914Z digest=sha256:20c0fbcd3fda4cf26ad305199af485749d8a204c795d9a840160614e13049e52

Observation c81138ce-edf2-4b90-98d0-f3cdb4f2ae42 · inbound

LLM-Powered AI Agent Systems and Their Applications in Industry cites this paper.

LLM-Powered AI Agent Systems and Their Applications in Industry Safeguarding Large Language Models: A Survey

Reference 56

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arxiv_id, observed 2026-05-22T14:06:37.983709Z

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=2026-05-22T14:05:54.535411Z digest=sha256:968bc0fbb1adf7d9e13085073f29ed64eb4e592b4c8571a67a7da9e6b1260a75

Observation 54620ad4-529f-4d99-a217-a596124b7f88 · inbound

Speaking images. A novel framework for the automated self-description of artworks cites this paper.

Speaking images. A novel framework for the automated self-description of artworks Safeguarding Large Language Models: A Survey

Reference 35

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no resolver link, observed 2026-08-07T13:17:52.423688Z

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source=pdf_text observed=2026-08-07T13:17:52.423688Z digest=sha256:9ed7e1f0fc2ea79f207d160fce933eb6c7296196515ba01a1c8e94c33f481a9e

Observation da8bb34e-cc54-471c-ac13-dcc80ec6eb27 · inbound

Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation cites this paper.

Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation Safeguarding Large Language Models: A Survey

Reference 26

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no resolver link, observed 2026-08-07T05:42:43.574246Z

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source=pdf_text observed=2026-08-07T05:42:43.574246Z digest=sha256:cbd0f780b9e4c4b28b4223a4d221661936dd33d23ef814c9876912585079b38d

Observation f027d4f4-8682-4088-8642-ca1e89009efc · 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 Safeguarding Large Language Models: A Survey

Reference 14

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no resolver link, observed 2026-08-06T22:23:08.161276Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:23:08.161276Z digest=sha256:73679e9fae5c3cc721994b07f216d28359aabf8ee8c666ed3ac516e1ee1cf38e

Observation 8f15604c-bf97-45e0-9a35-44986b27c7d8 · inbound

FairI Tales: Evaluation of Fairness in Indian Contexts with a Focus on Bias and Stereotypes cites this paper.

FairI Tales: Evaluation of Fairness in Indian Contexts with a Focus on Bias and Stereotypes Safeguarding Large Language Models: A Survey

Reference 18

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no resolver link, observed 2026-08-06T21:54:43.406674Z

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source=arxiv_source observed=2026-08-06T21:54:43.406674Z digest=sha256:7a2c12254960b1d0f0424170a066cb73cc4dfa28968d2a1f2a34b9b79e43d9ab

Observation 8fa32afa-929e-42f6-bd85-521963fd7ed4 · inbound

Enhancing Robustness of LLM-Driven Multi-Agent Systems through Randomized Smoothing cites this paper.

Enhancing Robustness of LLM-Driven Multi-Agent Systems through Randomized Smoothing Safeguarding Large Language Models: A Survey

Reference 26

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no resolver link, observed 2026-08-06T20:00:41.256371Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:00:41.256371Z digest=sha256:b0a93625daec89b50ebad27c5ea285d98451ae2a4b37906032f30577ae73f725

Observation e9c870e8-2021-488e-9b6a-bae749d1296d · 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 Safeguarding Large Language Models: A Survey

Reference 269

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no resolver link, observed 2026-08-05T10:39:08.229433Z

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source=pdf_text observed=2026-08-05T10:39:08.229433Z digest=sha256:43f5d08536251eeda2dbd1c1934bf59fb0168b65d64d6221ffc3eda4ddc2d4d1

Observation 07fa4d6f-25a8-460a-b643-a0350afdd30e · inbound

Tailored untruths: How personalisation challenges LLM safeguards cites this paper.

Tailored untruths: How personalisation challenges LLM safeguards Safeguarding Large Language Models: A Survey

Reference 12

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no resolver link, observed 2026-08-04T09:55:53.383379Z

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source=pdf_text observed=2026-08-04T09:55:53.383379Z digest=sha256:e9ce737b1288a154fd8df2bb70ebb6a3a67d896fadf434a376e31b949063a32f

Observation 6d02c81b-ab5d-4824-b7ca-8f7de9a736ae · inbound

Echoes of Human Malice in Agents: Benchmarking LLMs for Multi-Turn Online Harassment Attacks cites this paper.

Echoes of Human Malice in Agents: Benchmarking LLMs for Multi-Turn Online Harassment Attacks Safeguarding Large Language Models: A Survey

Reference 2024

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no resolver link, observed 2026-08-04T09:40:44.396113Z

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source=pdf_text observed=2026-08-04T09:40:44.396113Z digest=sha256:0a3cf9f1cc440232dcb77714d6375b3f1d252652d4a83cffefa1ada54d96f94b

Observation f6d173ed-a532-47f8-91c3-80d4f1d2d754 · inbound

LLM Harms: A Taxonomy and Discussion cites this paper.

LLM Harms: A Taxonomy and Discussion Safeguarding Large Language Models: A Survey

Reference 214

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verified exact
arxiv_id, observed 2026-05-17T00:31:24.867780Z

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=2026-05-17T00:29:07.951709Z digest=sha256:8ad9695420a80a387b8c1459399aac97d1b82eb53fe185fb5367ec8958e7d293

Observation d286cf62-9298-42e4-92d1-666116b4b6cc · inbound

LLM Harms: A Taxonomy and Discussion cites this paper.

LLM Harms: A Taxonomy and Discussion Safeguarding Large Language Models: A Survey

Reference 214

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no resolver link, observed 2026-08-03T18:19:30.420092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T18:19:30.420092Z digest=sha256:fabb85d1df9a6b42121d675b349cce9edd6da06a0046111308e299eba4377422

Observation 0ee03ff2-c705-4cca-9977-da9ffb59cd7a · inbound

Bielik Guard: Efficient Polish Language Safety Classifiers for LLM Content Moderation cites this paper.

Bielik Guard: Efficient Polish Language Safety Classifiers for LLM Content Moderation Safeguarding Large Language Models: A Survey

Reference 6

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arxiv_id, observed 2026-05-16T06:07:25.577605Z

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=2026-05-16T06:07:23.060966Z digest=sha256:e0930a0814d2ffe25ee88d9b40bbdf773216d1903bcf9abba9c05e820692c4bf

Observation 5c35d94a-549e-41a0-9f55-5b1c394d7d25 · inbound

From Governance Norms to Enforceable Controls: A Layered Translation Method for Runtime Guardrails in Agentic AI cites this paper.

From Governance Norms to Enforceable Controls: A Layered Translation Method for Runtime Guardrails in Agentic AI Safeguarding Large Language Models: A Survey

Reference 19

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arxiv_id, observed 2026-05-10T23:55:51.825116Z

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=2026-05-10T18:47:09.928166Z digest=sha256:7fd87c577965bcd2ab62d8618dcaaea80e7a7c51d1fe811bc927e36308bb5052

Observation c6dca80f-bd59-46b3-87fd-a703bcd64c11 · inbound

Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs cites this paper.

Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs Safeguarding Large Language Models: A Survey

Reference 16

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arxiv_id, observed 2026-05-11T06:46:39.524396Z

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

source=pdf_text observed=2026-05-10T17:27:13.339411Z digest=sha256:fe8bea20c61d5d2834ae26ffe027b4983011a7927a39069757817e763cb20d13

Observation 02fddf65-530a-41fb-8964-d3f1aab5b460 · inbound

ConsisGuard: Aligning Safety Deliberation with Policy Enforcement in LLM Guardrails cites this paper.

ConsisGuard: Aligning Safety Deliberation with Policy Enforcement in LLM Guardrails Safeguarding Large Language Models: A Survey

Reference 10

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arxiv_id, observed 2026-06-28T23:12:46.492824Z

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

source=arxiv_source observed=2026-06-28T23:03:01.151745Z digest=sha256:f1d03354d6ff81c259e986694f5f6059d001ceafe3d08cbb0496edabfcfd89fa

Observation 9945c231-4833-45d2-801a-6200a58d1a14 · inbound

Operational Evidence Gaps for LLMs in Fraud Detection and Trust-and-Safety Workflows cites this paper.

Operational Evidence Gaps for LLMs in Fraud Detection and Trust-and-Safety Workflows Safeguarding Large Language Models: A Survey

Reference 16

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no resolver link, observed 2026-08-02T07:06:19.372134Z

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source=pdf_text observed=2026-08-02T07:06:19.372134Z digest=sha256:5691fde6653250b8483a16665abceacd43563844008041d347f28f5fb3a579b6

Observation 692f1e92-cfde-4121-a3b6-811b0bb08bd2 · inbound

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models cites this paper.

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models Safeguarding Large Language Models: A Survey

Reference 32

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no resolver link, observed 2026-08-15T14:21:41.957376Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T14:21:41.957376Z digest=sha256:1938b7ba6c887f960bf2bf9064e213a6c97d7bb06213640557ff2edfe60fabfa