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Paper Citation Record · LEDGER

Spear Phishing With Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 31 inbound Pith citation observations for arXiv:2305.06972.

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

pith.paper-citation-record.v1
2305.06972 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 31 of 31 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:04:33.457578Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T23:16:24.030578Z

Reference resolution

0 of 0 outbound references displayed

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  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b85661fd-b5bb-479e-bea3-93725c2a825f · inbound

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

Jailbroken: How Does LLM Safety Training Fail? Spear Phishing With Large Language Models

Reference 28

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verified exact
arxiv_id, observed 2026-05-14T18:17:42.994890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-14T18:17:42.752997Z digest=sha256:17a37be1f80d3c63927fcae52ac8c01a6e70f983a7c852154296ee718859b5d4

Observation ec8e641b-cce0-4efe-935d-1ffa7f4e60f0 · inbound

"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models cites this paper.

"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models Spear Phishing With Large Language Models

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-17T08:39:28.112723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-17T08:39:28.047394Z digest=sha256:b94952898060e05f9e4a240abc148a3a29f0ee8740cf2beb84479dba678eb4f5

Observation 52808e7d-0c82-4295-b72f-9eeb9a665270 · inbound

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

AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models Spear Phishing With Large Language Models

Reference 8

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verified exact
arxiv_id, observed 2026-05-12T16:28:04.057582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-12T16:28:03.996446Z digest=sha256:55fe7a9b0483553c3fc1c1fd4858287fe3979e79e1410bfbbfb360f2f362078d

Observation 69e329c2-f20c-4242-a2ac-95fbb3154a78 · inbound

Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation cites this paper.

Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation Spear Phishing With Large Language Models

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-16T22:00:51.548747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-16T22:00:51.487120Z digest=sha256:28dd143f9b5a0246932d11fee7652feab87011f7afa2a01512796c5c0c96777a

Observation e5a891b0-a0e6-464a-94b9-e03252340017 · inbound

TrustLLM: Trustworthiness in Large Language Models cites this paper.

TrustLLM: Trustworthiness in Large Language Models Spear Phishing With Large Language Models

Reference 248

Resolution
verified exact
arxiv_id, observed 2026-05-18T11:17:08.709512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T11:17:08.108565Z digest=sha256:158ebb968ac03a107ae683f73a956b91959d50031b9d67469c9a3593216f2543

Observation ba7e8388-6430-4110-b26f-cedd2ab27edb · inbound

LLM Cyber Evaluations Don't Capture Real-World Risk cites this paper.

LLM Cyber Evaluations Don't Capture Real-World Risk Spear Phishing With Large Language Models

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-09T22:04:33.457578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:04:33.457578Z digest=sha256:f588bc7e3c7814eba0b296718c00aae4adef00255314c6196d0f5c8662ba51fb

Observation d8d054ba-8c09-4edd-be97-db3efe0181b9 · inbound

Cyri: A Conversational AI-based Assistant for Supporting the Human User in Detecting and Responding to Phishing Attacks cites this paper.

Cyri: A Conversational AI-based Assistant for Supporting the Human User in Detecting and Responding to Phishing Attacks Spear Phishing With Large Language Models

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-08T17:19:44.135516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:19:44.135516Z digest=sha256:8488a026e702430f98074bda920be13e93257e28f9fed6380f2a9134c69a6581

Observation 388087b7-93a3-4211-8be7-13ff05381fa9 · inbound

Attacks on Machine-Text Detectors Retain Stylistic Fingerprints cites this paper.

Attacks on Machine-Text Detectors Retain Stylistic Fingerprints Spear Phishing With Large Language Models

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T15:37:12.971181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:37:12.971181Z digest=sha256:631f77e78ecf0bf72e371878795cdbd688a5c797c1e5968aacc50ba72f95e09e

Observation 2d191b0f-e804-4ca7-83f5-40cd29966215 · inbound

Cracking Aegis: An Adversarial LLM-based Game for Raising Awareness of Vulnerabilities in Privacy Protection cites this paper.

Cracking Aegis: An Adversarial LLM-based Game for Raising Awareness of Vulnerabilities in Privacy Protection Spear Phishing With Large Language Models

Reference 60

Resolution
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no resolver link, observed 2026-08-07T14:55:18.450654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:55:18.450654Z digest=sha256:1045430b6a80591fc6bebd8e7d8234d9bdbd1f69f3248b7fc5581eb8be80b952

Observation e0237721-2db5-44ed-9c0f-6ab4cdad0b3e · inbound

MultiPhishGuard: An Explainable and Adaptive Multi-Agent LLM System for Phishing Email Detection cites this paper.

MultiPhishGuard: An Explainable and Adaptive Multi-Agent LLM System for Phishing Email Detection Spear Phishing With Large Language Models

Reference 20

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no resolver link, observed 2026-08-07T13:59:15.335698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:59:15.335698Z digest=sha256:ad17008d03d02350e9b1464c9fc77c70ca38ac56753ff14f851f079105b16df2

Observation b174b748-b56c-4d1d-8de9-fc0724f7a3e5 · inbound

Military AI Cyber Agents (MAICAs) Constitute a Global Threat to Critical Infrastructure cites this paper.

Military AI Cyber Agents (MAICAs) Constitute a Global Threat to Critical Infrastructure Spear Phishing With Large Language Models

Reference 17

Resolution
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no resolver link, observed 2026-08-07T04:27:40.141588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:27:40.141588Z digest=sha256:7751568a7c767c683844c3bf8cfbe2b65319ed0a599e6034d132c1b15434c1db

Observation 9c540631-7b90-4b1a-83f4-db2764d76a06 · inbound

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability cites this paper.

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability Spear Phishing With Large Language Models

Reference 9

Resolution
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no resolver link, observed 2026-08-07T00:32:37.366010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:32:37.366010Z digest=sha256:7c9e46f7fd97fa4febcfde48e77a16982a41d066e99055e47b21b4ef36415a9f

Observation 80b1ca02-e5b2-4eee-9478-02fdb5d7fc13 · inbound

Circumventing Safety Alignment in Large Language Models Through Embedding Space Toxicity Attenuation cites this paper.

Circumventing Safety Alignment in Large Language Models Through Embedding Space Toxicity Attenuation Spear Phishing With Large Language Models

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T19:26:13.644775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:26:13.644775Z digest=sha256:78ee13b13cbbb025348233c44e7e6f6512a681f0ccd76a3693aa56bbc9e33ba6

Observation c523d54f-454f-4aa8-af78-06b43d995613 · inbound

Can We End the Cat-and-Mouse Game? Simulating Self-Evolving Phishing Attacks with LLMs and Genetic Algorithms cites this paper.

Can We End the Cat-and-Mouse Game? Simulating Self-Evolving Phishing Attacks with LLMs and Genetic Algorithms Spear Phishing With Large Language Models

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T12:43:16.778997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T12:43:16.778997Z digest=sha256:31b775ed02b0c2191da67e63ebd23af7f1267b455f8f32633c9a44bfe1f87e2b

Observation 92f69221-07f3-48d4-b130-fd0428948258 · inbound

JADES: A Universal Framework for Jailbreak Assessment via Decompositional Scoring cites this paper.

JADES: A Universal Framework for Jailbreak Assessment via Decompositional Scoring Spear Phishing With Large Language Models

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-05T14:51:03.671313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:51:03.671313Z digest=sha256:f7811432768b4c3e011451092c7a79a811e7ec0de6424dec707024ff215f6d3d

Observation ced84a6b-4865-4211-a609-b7383746a48d · inbound

SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing cites this paper.

SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Spear Phishing With Large Language Models

Reference 45

Resolution
verified exact
arxiv_id, observed 2026-05-18T21:01:51.392133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T20:57:03.172931Z digest=sha256:1a94f929710b79f54b4b90f38361bd1346a837171d5cfce20124057913e77021

Observation cd411e46-a3fb-49d2-8a12-4bdd5d8ab87c · inbound

Character-Level Perturbations Disrupt LLM Watermarks cites this paper.

Character-Level Perturbations Disrupt LLM Watermarks Spear Phishing With Large Language Models

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-04T19:46:50.589714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T19:46:50.589714Z digest=sha256:56b65d5353d52d94eee7984a6e2d39bf42ff39454b5c1cc02d5887e9b1940c73

Observation c045e90a-c886-4084-adac-18a0017d8dfc · inbound

An Independent Safety Evaluation of Kimi K2.5 cites this paper.

An Independent Safety Evaluation of Kimi K2.5 Spear Phishing With Large Language Models

Reference 79

Resolution
verified exact
arxiv_id, observed 2026-05-13T19:43:11.550995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-13T19:38:18.674355Z digest=sha256:3237796ccbe0a0012b0c25167bb8f968fe6293909dbd6ad47395d7183318e0a9

Observation be66978b-f0d4-4934-b2a4-0c350d99b635 · inbound

Large Language Models Generate Harmful Responses Using a Distinct Mechanism, Shared Across Harm Types cites this paper.

Large Language Models Generate Harmful Responses Using a Distinct Mechanism, Shared Across Harm Types Spear Phishing With Large Language Models

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-11T07:31:00.289967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T17:08:25.471462Z digest=sha256:189342aa562307cd6bd70b4ee9352598e24e90e385d877006c0de9412ab86993

Observation 0b9592c7-60cb-47ec-a123-fbce096a2b74 · inbound

Beyond A Fixed Seal: Adaptive Stealing Watermark in Large Language Models cites this paper.

Beyond A Fixed Seal: Adaptive Stealing Watermark in Large Language Models Spear Phishing With Large Language Models

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-11T08:25:59.507720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T16:39:50.659037Z digest=sha256:b27b6c27cdea8a14e2155c9f4307e3b627678c61a86aa1996d39faa4cf86b97f

Observation 27e1d575-1032-4f12-8894-dd920b4822eb · inbound

Process Matters more than Output for Distinguishing Humans from Machines cites this paper.

Process Matters more than Output for Distinguishing Humans from Machines Spear Phishing With Large Language Models

Reference 63

Resolution
verified exact
arxiv_id, observed 2026-05-11T20:16:09.971747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-08T09:49:10.392752Z digest=sha256:4ee573e1d90a43c3705d93c5460d4968d08a7c4a9d0c2c29d3d29a4074d87cdf

Observation 0da9b06d-6559-456d-bcc5-e8597db8b39f · inbound

Process Matters more than Output for Distinguishing Humans from Machines cites this paper.

Process Matters more than Output for Distinguishing Humans from Machines Spear Phishing With Large Language Models

Reference 63

Resolution
verified exact
arxiv_id, observed 2026-05-12T07:46:44.400923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-12T01:53:15.463453Z digest=sha256:7959c9d62c487270ee305ecf01e4c11beff36f972ca3ac3f0b83b6093e5cec09

Observation b72ac158-53e3-4c16-8e88-342454f51e61 · inbound

PASA: A Principled Embedding-Space Watermarking Approach for LLM-Generated Text under Semantic-Invariant Attacks cites this paper.

PASA: A Principled Embedding-Space Watermarking Approach for LLM-Generated Text under Semantic-Invariant Attacks Spear Phishing With Large Language Models

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-13T01:17:02.392792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-13T01:13:55.067785Z digest=sha256:57e238ebd4c0bfcb466eba934369b627387b87d6d563493948ac882f85ca6b34

Observation 03c51c18-4a2a-4bed-8aeb-a61092c3c1dc · inbound

PASA: A Principled Embedding-Space Watermarking Approach for LLM-Generated Text under Semantic-Invariant Attacks cites this paper.

PASA: A Principled Embedding-Space Watermarking Approach for LLM-Generated Text under Semantic-Invariant Attacks Spear Phishing With Large Language Models

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-06-30T23:45:08.402323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-30T23:37:21.982973Z digest=sha256:100f9ff97c3cdf6c1fa5ddd4a3c4db21dc08c00cf0bbc15c0890bf4fc957b319

Observation ef3a2f02-abfa-4503-ae49-25ea4361a306 · inbound

The End of Trust: How Agentic AI Breaks Security Assumptions cites this paper.

The End of Trust: How Agentic AI Breaks Security Assumptions Spear Phishing With Large Language Models

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-20T20:03:43.947085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T20:00:21.768607Z digest=sha256:f5597e0abd5f162f4ef4f74c0f963a814a1e04a4fbca0fcf886757c547323df3

Observation b81514b9-c1e5-4687-841c-a0dac42c4704 · inbound

From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AI cites this paper.

From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AI Spear Phishing With Large Language Models

Reference 53

Resolution
verified exact
arxiv_id, observed 2026-05-20T18:08:50.593231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T18:08:24.901025Z digest=sha256:f3399a6122ed2e8540cbf2c6d9efcfdabf049d73521525db2e034a5f16f0cc76

Observation ec43087f-6f3c-4d40-8935-4d1238cb4c0c · inbound

Multilingual jailbreaking of LLMs using low-resource languages cites this paper.

Multilingual jailbreaking of LLMs using low-resource languages Spear Phishing With Large Language Models

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-20T10:33:13.002339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T10:29:42.872902Z digest=sha256:87f760cce988284e7364af832c4af8c35e49480138bd0bcb55bd54d544adb3bf

Observation 1390e46d-4be7-4231-ab82-345b302744ba · inbound

DataShield: Safety-degrading Data Filtering for LLM Benign Instruction Fine-Tuning cites this paper.

DataShield: Safety-degrading Data Filtering for LLM Benign Instruction Fine-Tuning Spear Phishing With Large Language Models

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-07-01T19:46:10.207018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T22:09:02.498712Z digest=sha256:52de17679f74facb801cd11d7b2641d17e552c198ddeb7af0a96ed5a02b2c902

Observation d9cea878-8c65-4454-9710-1d60df9a65f1 · inbound

Investigating and Alleviating Harm Amplification in LLM Interactions cites this paper.

Investigating and Alleviating Harm Amplification in LLM Interactions Spear Phishing With Large Language Models

Reference 25

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T23:16:24.032911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-28T14:31:52.027889Z digest=sha256:9e193485e02759d5de2ccc8f9a9e94c00128847f0db23ac51badf78de7aeda24

Observation ba27317e-1c9e-4616-b5be-543229052a9e · inbound

Position: Preventing AI-Generated CSAM Necessitates New Approaches to AI Safety cites this paper.

Position: Preventing AI-Generated CSAM Necessitates New Approaches to AI Safety Spear Phishing With Large Language Models

Reference 151

Resolution
unresolved
no resolver link, observed 2026-07-12T14:28:50.627444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T14:28:50.627444Z digest=sha256:72ca31ccf9e6ac6bc482137b6bb1ddc16769f8ed7379efc0cc3e7de8d34e46f6

Observation 09b0e124-85c4-4065-89ac-509194da2c6b · inbound

AI Deployment and Cyber Governance Failures in Public-Sector Organizations: A Typological Analysis cites this paper.

AI Deployment and Cyber Governance Failures in Public-Sector Organizations: A Typological Analysis Spear Phishing With Large Language Models

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-01T02:42:52.201288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T02:42:52.201288Z digest=sha256:e5c8d21c0106c32a98d92c2ef19f7538456a77c1c81a4702d35ab1bb74a018cf