Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-18T20:57:03.172931Z
Paper Citation Record · LEDGER
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing
As of 5 August 2026, this Paper Citation Record lists 100 of 100 outbound references and 1 inbound Pith citation observation for arXiv:2508.21457.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T14:26:44.302311Z
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
100 of 100 outbound references displayed
- verified exact19
- verified fuzzy77
- unresolved4
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 5eee025b-a492-4050-9f2a-ae05812f8ea7 · outbound
This paper cites https://www.verizon.com/business /resources/T646/reports/2024-dbir-data- breach-investigations-report.pdf.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing https://www.verizon.com/business /resources/T646/reports/2024-dbir-data- breach-investigations-report.pdf
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:02:50.901822Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation db254edb-e76a-4bfb-ac91-14863d827e1f · outbound
This paper cites https://hoxhunt.com/guide/ phishing-trends-report.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing https://hoxhunt.com/guide/ phishing-trends-report
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.660647Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1368e090-507c-4f75-b353-bbb62ba82ea8 · outbound
This paper cites https://www.ibm.com/downloads/ documents/us-en/107a02e94948f4ec.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing https://www.ibm.com/downloads/ documents/us-en/107a02e94948f4ec
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.677858Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation aa1f12c1-4fc9-47d6-97b8-d82ce6d16c87 · outbound
This paper cites https://apnews.com/article/microsoft -generative-ai-offensive-cyber-operations- 3482b8467c81830012a9283fd6b5f529.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing https://apnews.com/article/microsoft -generative-ai-offensive-cyber-operations- 3482b8467c81830012a9283fd6b5f529
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.646475Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 20ea569e-5bfb-465f-aa95-bc207307ae9b · outbound
This paper cites https://controld.com/blog/ phishing-statistics-industry-trends/.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing https://controld.com/blog/ phishing-statistics-industry-trends/
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.636922Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation cc509441-5461-4396-a5cf-e1926dc407fb · outbound
This paper cites Next-generation phishing: How llm agents empower cyber at- tackers.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Next-generation phishing: How llm agents empower cyber at- tackers
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.664512Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ea2a0a5c-ccfd-4c0f-865e-597bb0949b34 · outbound
This paper cites Exploring the potential implications of ai-generated content in social engineering attacks.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Exploring the potential implications of ai-generated content in social engineering attacks
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.624228Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 285709f7-3374-473f-a5cf-1fe97b5cb426 · outbound
This paper cites Proceedings of the Future Technologies Con- ference (FTC) 2020, Volume 2, volume 1289.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Proceedings of the Future Technologies Con- ference (FTC) 2020, Volume 2, volume 1289
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.602442Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5d4c1dcb-5ee1-45f5-9507-fbd5aaa2da06 · outbound
This paper cites an unresolved cited work.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Unresolved cited work
Reference 9
- Resolution
- unresolved
- raw_fallback, observed 2026-05-18T21:01:52.605358Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 14f03ab1-d5bc-432a-aee1-ccb91ba25839 · outbound
This paper cites Lateral phishing with large language models: A large organization com- parative study.IEEE Access.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Lateral phishing with large language models: A large organization com- parative study.IEEE Access
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:02:50.907912Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a413e6a2-d347-436c-bd07-8a60c16b6365 · outbound
This paper cites Deciphering textual authenticity: A generalized strategy through the lens of large language semantics for detect- ing human vs.{Machine-Generated}text.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Deciphering textual authenticity: A generalized strategy through the lens of large language semantics for detect- ing human vs.{Machine-Generated}text
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:02:50.904922Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 867e6df8-6143-4fd7-8582-9a34bc06dddd · outbound
This paper cites Ai-enhanced social en- gineering: Evolving tactics in cyber fraud and manipulation.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Ai-enhanced social en- gineering: Evolving tactics in cyber fraud and manipulation
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:02:50.938785Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation bd4cdd51-7cd6-43b0-93e3-7885cb7c7bea · outbound
This paper cites On the Feasibility of Using MultiModal LLMs to Execute AR Social Engineering Attacks.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing On the Feasibility of Using MultiModal LLMs to Execute AR Social Engineering Attacks
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T21:01:51.414990Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a9fdcef4-ea2f-4142-bc1b-50998a8b2249 · outbound
This paper cites com/epu6w4cp.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing com/epu6w4cp
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:02:50.942661Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 2b418db7-568e-49df-9687-a3c313fb428a · outbound
This paper cites Analyz- ing the impact of ai-generated email marketing content on email deliverability in spam folder placement.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Analyz- ing the impact of ai-generated email marketing content on email deliverability in spam folder placement
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:02:50.921631Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 751a5d89-7a17-4fcc-a17d-6b4e4274eb55 · outbound
This paper cites Leveraging eud and generative ai for ethical phishing campaigns.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Leveraging eud and generative ai for ethical phishing campaigns
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:02:50.949009Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 7ab98a30-0b5c-4538-bbae-ae338a852d86 · outbound
This paper cites Machine learn- ing and watermarking for accurate detection of ai generated phishing emails.Electronics, 14(13):1–21.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Machine learn- ing and watermarking for accurate detection of ai generated phishing emails.Electronics, 14(13):1–21
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.719525Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4b91b637-36f6-440d-9634-e84f882bacec · outbound
This paper cites Finding differences be- tween llm-generated and human-written text: A phishing emails case study.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Finding differences be- tween llm-generated and human-written text: A phishing emails case study
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:02:50.911920Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 8e296ebe-ae69-4ebe-8f51-97b485a90f12 · outbound
This paper cites Multi-turn hidden back- door in large language model-powered chatbot models.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Multi-turn hidden back- door in large language model-powered chatbot models
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:02:50.915052Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0628af0d-6c0d-498e-8f65-746c7af05a5e · outbound
This paper cites PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T21:01:51.402303Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation cbafbe9a-4657-4cde-87c8-03b504699aee · outbound
This paper cites Voice phishing fraud and its modus operandi.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Voice phishing fraud and its modus operandi
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:02:50.952612Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 05408210-3a53-4e53-8063-420e5f4a45a3 · outbound
This paper cites Phreshphish: A real-world, high-quality, large-scale phishing website dataset and bench- mark.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Phreshphish: A real-world, high-quality, large-scale phishing website dataset and bench- mark
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T21:01:51.424473Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 2e7e371b-4831-4cff-8cc6-c6426a894dfa · outbound
This paper cites https://github.com/rmodi6/ Email-Classification/tree/master/ dataset/meetings.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing https://github.com/rmodi6/ Email-Classification/tree/master/ dataset/meetings
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.814813Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c9ca1165-4075-4a65-84ca-32988694a649 · outbound
This paper cites https://www.kaggle.com/datasets/ mandygu/lingspam-dataset.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing https://www.kaggle.com/datasets/ mandygu/lingspam-dataset
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.722549Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 17cb55c6-5a07-4d26-8b3c-53582c60383a · outbound
This paper cites an unresolved cited work.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Unresolved cited work
Reference 25
- Resolution
- unresolved
- raw_fallback, observed 2026-05-18T21:01:52.732573Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 969b5d1b-8fe6-4698-ac37-eea47d64aca7 · outbound
This paper cites https: //it.cornell.edu/phish-bowl.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing https: //it.cornell.edu/phish-bowl
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.805324Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 2353b7cc-4c4c-4c87-8ab9-ca35ebb0083b · outbound
This paper cites an unresolved cited work.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Unresolved cited work
Reference 27
- Resolution
- unresolved
- raw_fallback, observed 2026-05-18T21:02:50.918542Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a1fe9ef2-a52b-47a5-99dc-0bd4ceb19b8a · outbound
This paper cites https:// www.kaggle.com/datasets/jackksoncsie/ spam-email-dataset/data.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing https:// www.kaggle.com/datasets/jackksoncsie/ spam-email-dataset/data
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.671202Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation db0a89db-7e30-4966-ae07-33c2c0a6984e · outbound
This paper cites A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T21:01:51.429023Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f088d0b2-aa21-4799-a726-5d9b7669a6c3 · outbound
This paper cites System- atization of knowledge (sok): A systematic re- view of software-based web phishing detection.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing System- atization of knowledge (sok): A systematic re- view of software-based web phishing detection
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.801869Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1f3fbb4d-30ef-40f5-8058-1adc8f063abd · outbound
This paper cites Getting the general public to create phishing emails: A study on the persua- siveness of ai-generated phishing emails versus human methods.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Getting the general public to create phishing emails: A study on the persua- siveness of ai-generated phishing emails versus human methods
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.798446Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e7afe22e-04ed-4048-b22c-10835ac3e7df · outbound
This paper cites Detecting ai-generated phishing emails targeting health- care practitioners using ensemble techniques.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Detecting ai-generated phishing emails targeting health- care practitioners using ensemble techniques
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.794880Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3b5a488f-1c7b-4703-9f0a-deddaf676890 · outbound
This paper cites Ai and prompt engineering: The new weapons of so- cial engineering attacks.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Ai and prompt engineering: The new weapons of so- cial engineering attacks
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.791723Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1fba0c2f-ac2f-4508-9155-40251a9935dd · outbound
This paper cites Anal- ysis and prevention of ai-based phishing email attacks.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Anal- ysis and prevention of ai-based phishing email attacks
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.788263Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c9af3ea7-10c9-4599-8456-f7484aae9898 · outbound
This paper cites Gen- erating phishing attacks and novel detection algorithms in the era of large language models.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Gen- erating phishing attacks and novel detection algorithms in the era of large language models
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.742016Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation bda422c3-096a-46b4-8f91-b160a8459779 · outbound
This paper cites Assessing AI vs Human-Authored Spear Phishing SMS Attacks: An Empirical Study.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Assessing AI vs Human-Authored Spear Phishing SMS Attacks: An Empirical Study
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T21:01:51.442494Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 00d09c31-984d-4fcd-933b-2f88e88fd32f · outbound
This paper cites GLTR: Statistical Detection and Visualization of Generated Text.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing GLTR: Statistical Detection and Visualization of Generated Text
Reference 37
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T21:01:51.451143Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b2c0ea26-8427-4515-85b7-ce7f732c7cd1 · outbound
This paper cites David ver- sus goliath: Can machine learning detect llm- generated text? a case study in the detection of phishing emails.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing David ver- sus goliath: Can machine learning detect llm- generated text? a case study in the detection of phishing emails
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.783946Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 88c067ee-2005-47b1-ad93-0656d260670b · outbound
This paper cites Detection of ai-generated emails-a case study.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Detection of ai-generated emails-a case study
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.779475Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5330ed51-5522-4dba-82c9-deebad697588 · outbound
This paper cites Generat- ing personalized phishing emails forsocial engi- neeringtrainingbasedon neurallanguagemod- els.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Generat- ing personalized phishing emails forsocial engi- neeringtrainingbasedon neurallanguagemod- els
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.769141Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e88d9d3c-c066-4ad6-b1a9-d34dd5237b5e · outbound
This paper cites X- phishing-writer: A framework for cross-lingual phishing email generation.ACM Transactions on Asian and Low-Resource Language Infor- mation Processing.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing X- phishing-writer: A framework for cross-lingual phishing email generation.ACM Transactions on Asian and Low-Resource Language Infor- mation Processing
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.808836Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b05c082d-9e6d-4947-bf62-902bd3ca203a · outbound
This paper cites Fighting against phishing attacks: state of the art and future challenges.Neural Computing and Ap- plications, 28:3629–3654.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Fighting against phishing attacks: state of the art and future challenges.Neural Computing and Ap- plications, 28:3629–3654
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.764969Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 60a55b41-41d8-407f-983f-9597f6898508 · outbound
This paper cites Application of large language models in cybersecurity: A sys- tematic literature review.IEEE Access.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Application of large language models in cybersecurity: A sys- tematic literature review.IEEE Access
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.773207Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ced84a6b-4865-4211-a609-b7383746a48d · outbound
This paper cites Spear Phishing With Large Language Models.
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 2026-08-04T06:34:03.388597+00:00.
Observation b6da78f3-ccca-4274-bc33-d8798e0f2f92 · outbound
This paper cites Evaluating Large Language Models' Capability to Launch Fully Automated Spear Phishing Campaigns: Validated on Human Subjects.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Evaluating Large Language Models' Capability to Launch Fully Automated Spear Phishing Campaigns: Validated on Human Subjects
Reference 46
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T21:01:51.466133Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e9a89035-a099-44d5-8f14-0c51ad0dbf0b · outbound
This paper cites Devising and Detecting Phishing: Large Language Models vs. Smaller Human Models.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Devising and Detecting Phishing: Large Language Models vs. Smaller Human Models
Reference 47
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T21:01:51.447027Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 32c83027-9548-4c10-8b33-8849f675e159 · outbound
This paper cites Perplexity—a measure of the difficulty of speech recognition tasks.The Journal of the Acoustical Society of America, 62(S1):S63–S63.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Perplexity—a measure of the difficulty of speech recognition tasks.The Journal of the Acoustical Society of America, 62(S1):S63–S63
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.760553Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 55fd2fda-801e-428b-992d-d82db19c99ed · outbound
This paper cites Exploiting programmatic behav- ior of llms: Dual-use through standard security attacks.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Exploiting programmatic behav- ior of llms: Dual-use through standard security attacks
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.755692Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 34cbe7d3-bdf5-46d6-ae56-45cbb9fc1cb2 · outbound
This paper cites Targeted Phishing Campaigns using Large Scale Language Models.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Targeted Phishing Campaigns using Large Scale Language Models
Reference 50
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T21:01:51.437716Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4324b529-1309-465e-beb0-1f8121fc509d · outbound
This paper cites From vulnerability to defense: The role of large language models in enhancing cybersecurity.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing From vulnerability to defense: The role of large language models in enhancing cybersecurity
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.752721Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 057f62a6-7669-4479-9973-0b992c42dafd · outbound
This paper cites Offensive ai: unification of email generation through gpt-2 model with a game-theoretic approach for spear-phishing attacks.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Offensive ai: unification of email generation through gpt-2 model with a game-theoretic approach for spear-phishing attacks
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.818039Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d6dbf650-0db9-4ae8-b646-34a95693b761 · outbound
This paper cites Securityanalysis on practices of certificate authorities in the https phishing ecosystem.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Securityanalysis on practices of certificate authorities in the https phishing ecosystem
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.749618Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b612654e-7b98-48a9-9368-7ec8870bb8db · outbound
This paper cites A watermark for large language models.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing A watermark for large language models
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.811836Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 401d6091-63c7-468c-99ee-92dbe6fbe180 · outbound
This paper cites Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
Reference 55
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T21:01:51.471247Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 81c7efae-8845-456b-81dc-8ec1799f54e0 · outbound
This paper cites A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterization.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterization
Reference 56
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T21:01:51.419841Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 274ff4e1-9df6-4c05-84b7-c99b8c5d25b4 · outbound
This paper cites Phish- ing faster: Implementing chatgpt into phishing campaigns.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Phish- ing faster: Implementing chatgpt into phishing campaigns
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.745920Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b7c9bf32-a571-494b-a902-1af1c5bb0fe6 · outbound
This paper cites https://faker.readthedocs.io/en/master/.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing https://faker.readthedocs.io/en/master/
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:02:50.930013Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 010b0dee-dd17-4c1e-8ac3-4db6a1a17445 · outbound
This paper cites Rouge: A package for auto- matic evaluation of summaries.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Rouge: A package for auto- matic evaluation of summaries
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:02:50.956600Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4a4040a8-371c-4ac9-b38f-9b4768da4850 · outbound
This paper cites Phish- pedia: A hybrid deep learning based approach to visually identify phishing webpages.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Phish- pedia: A hybrid deep learning based approach to visually identify phishing webpages
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:02:50.945639Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f999c744-a468-4252-8377-0c62f161a22b · outbound
This paper cites SecureNet: A Comparative Study of DeBERTa and Large Language Models for Phishing Detection.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing SecureNet: A Comparative Study of DeBERTa and Large Language Models for Phishing Detection
Reference 61
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T21:01:51.410693Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a6a66f18-3a55-48ba-95ca-037409c02f7c · outbound
This paper cites Training Users Against Human and GPT-4 Generated Social Engineering Attacks.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Training Users Against Human and GPT-4 Generated Social Engineering Attacks
Reference 62
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T21:01:51.433263Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 648d5319-0445-4b66-bcb5-b352ce1e6165 · outbound
This paper cites Characterising deception in ai: A survey.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Characterising deception in ai: A survey
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.738765Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 61c25cc0-5816-4c70-9a60-aa6ee75b616a · outbound
This paper cites Enhancing security in large language models: A comprehensive review of prompt injection attacks and defenses.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Enhancing security in large language models: A comprehensive review of prompt injection attacks and defenses
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.735780Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 737fb783-a1df-4fd0-9a59-d7141330a0f2 · outbound
This paper cites Adversarial robustness of phishing email detection models.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Adversarial robustness of phishing email detection models
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.674229Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 8b0a363e-5322-4958-9850-7f0a84daaf8c · outbound
This paper cites Spam filtering with naive bayes-which naive bayes? InCEAS,volume 17, pages 28–69.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Spam filtering with naive bayes-which naive bayes? InCEAS,volume 17, pages 28–69
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:02:50.925756Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 156176a7-7666-45f7-919c-564a89f5b9f5 · outbound
This paper cites News Category Dataset.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing News Category Dataset
Reference 67
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T21:01:51.460897Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e9ffbed3-d357-4b82-b473-c030de87c107 · outbound
This paper cites Detectgpt: Zero-shot machine-generated text detection using probability curvature.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Detectgpt: Zero-shot machine-generated text detection using probability curvature
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:02:50.933585Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0bcac711-6768-4c39-9eda-63593eabec69 · outbound
This paper cites Benchmarking 21 open-source large language models for phish- ing link detection with prompt engineering.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Benchmarking 21 open-source large language models for phish- ing link detection with prompt engineering
Reference 69
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.729665Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3817fbac-82d1-42ed-9d20-523481a34410 · outbound
This paper cites Phishing for trust in the ai age: A quasi-experimental study on individual human factors influencing trust in ai-driven phishing attempts.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Phishing for trust in the ai age: A quasi-experimental study on individual human factors influencing trust in ai-driven phishing attempts
Reference 70
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.627962Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f29403af-adf3-4baa-aa69-334f657697f1 · outbound
This paper cites Teach LLMs to Phish: Stealing Private Information from Language Models.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Teach LLMs to Phish: Stealing Private Information from Language Models
Reference 71
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T21:01:51.381695Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 021f2e26-d721-48e4-8b48-e9a364830f90 · outbound
This paper cites Bleu: a method for auto- matic evaluation of machine translation.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Bleu: a method for auto- matic evaluation of machine translation
Reference 72
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.598906Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e8d35549-b8d3-45b5-a7a2-4661f8273ca9 · outbound
This paper cites Creatively malicious prompt engineering.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Creatively malicious prompt engineering
Reference 73
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.726155Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b9cdda8b-e95d-412b-8523-4a1c27115471 · outbound
This paper cites Leveraging generative pre-trained transformers for the detection and generation of social engineering attacks: A case study on youtube collusion scams.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Leveraging generative pre-trained transformers for the detection and generation of social engineering attacks: A case study on youtube collusion scams
Reference 74
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.591205Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 003ab904-75cc-498d-8101-d2849ae6ea12 · outbound
This paper cites Ap- plying large language model (llm) for develop- ing cybersecurity policies to counteract spear phishing attacks on senior corporate managers.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Ap- plying large language model (llm) for develop- ing cybersecurity policies to counteract spear phishing attacks on senior corporate managers
Reference 75
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.595663Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f2097bfa-ae2a-4f24-9529-8ccb94508fa3 · outbound
This paper cites X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agents.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agents
Reference 76
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T21:01:51.387198Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1502030e-e7f7-452d-b5b9-3a55aa445d2c · outbound
This paper cites Exploring the impact of ethnicity on susceptibility to voice phishing.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Exploring the impact of ethnicity on susceptibility to voice phishing
Reference 77
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.716022Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 12ee1306-ba54-4eeb-acc9-6bb0113614a3 · outbound
This paper cites https://www.knowbe4.com/resources /reports/phishing-by-industry-benchmarking- report.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing https://www.knowbe4.com/resources /reports/phishing-by-industry-benchmarking- report
Reference 78
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.712823Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4170a437-f602-488b-a456-09b736e4b66f · outbound
This paper cites https://blog.barracuda.com/2025/03/19/threat- spotlight-phishing-as-a-service-fast-evolving- threat.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing https://blog.barracuda.com/2025/03/19/threat- spotlight-phishing-as-a-service-fast-evolving- threat
Reference 79
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.634063Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f508194e-a620-4e55-9e52-182982c46abe · outbound
This paper cites From chatbots to phishbots?: Phishing scam generation in commercial large language models.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing From chatbots to phishbots?: Phishing scam generation in commercial large language models
Reference 80
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.708873Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b979d0bc-ebe0-4024-976b-164a1910762f · outbound
This paper cites Creating and detecting fake reviews of online products.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Creating and detecting fake reviews of online products
Reference 81
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.615459Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 503b07a5-f577-491d-8d28-8ef68579bb73 · outbound
This paper cites Digital decep- tion: Generative artificial intelligence in social engineering and phishing.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Digital decep- tion: Generative artificial intelligence in social engineering and phishing
Reference 82
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.608740Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 7543415e-c917-4cd8-b692-e50a0d127f1b · outbound
This paper cites Linguistic Deception Detection–Models, Domains, Behaviors, Stylis- tic Patterns to Large Language Models (LLMs).
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Linguistic Deception Detection–Models, Domains, Behaviors, Stylis- tic Patterns to Large Language Models (LLMs)
Reference 83
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.612026Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c0c428bd-e3a0-417d-96ae-47d970175904 · outbound
This paper cites How well does gpt phish people? an investigation involving cognitive biases and feedback.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing How well does gpt phish people? an investigation involving cognitive biases and feedback
Reference 84
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.704981Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation aba9dcc1-ce42-461e-833b-e1492615c086 · outbound
This paper cites Abusegpt: Abuseofgenerative ai chatbots to create smishing campaigns.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Abusegpt: Abuseofgenerative ai chatbots to create smishing campaigns
Reference 85
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.620146Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c4462948-a669-413f-9328-619a79c55f0a · outbound
This paper cites The dual-edged sword of large lan- guage models in phishing.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing The dual-edged sword of large lan- guage models in phishing
Reference 86
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.701664Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5444bfaf-ba4a-427a-8bfd-6120fac72ced · outbound
This paper cites Exploiting large language models (llms) through deception techniques and per- suasion principles.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Exploiting large language models (llms) through deception techniques and per- suasion principles
Reference 87
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.631135Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 6d1cd820-b764-4513-8e1f-6bb27e9449ec · outbound
This paper cites A roundup of the top phishing attacks in 2024 so far.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing A roundup of the top phishing attacks in 2024 so far
Reference 88
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.698772Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3b58591e-94ae-4773-8158-866b9c0c946d · outbound
This paper cites Evaluating the effec- tiveness of llm-generated phishing campaigns.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Evaluating the effec- tiveness of llm-generated phishing campaigns
Reference 89
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.681460Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1cbcff59-75b3-46d5-ab5b-5cfb764757a6 · outbound
This paper cites https://umatechnology.org/19- phishing-statistics-and-trends-updated- 2025/.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing https://umatechnology.org/19- phishing-statistics-and-trends-updated- 2025/
Reference 90
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.650137Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 83c26c55-5ec3-45cf-8439-7c181f044b5f · outbound
This paper cites Chatgpt: A threat to spam filtering systems.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Chatgpt: A threat to spam filtering systems
Reference 91
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.640167Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 110fcfeb-2f0c-44e4-988e-9cde77bb7b02 · outbound
This paper cites Ai-generated spam 18 review detection framework with deep learning algorithms and natural language processing.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Ai-generated spam 18 review detection framework with deep learning algorithms and natural language processing
Reference 92
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.695836Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 02b5ab2d-4b92-4d59-9bd6-d1599e52e651 · outbound
This paper cites https://deepai.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing https://deepai
Reference 93
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.691840Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5add78b3-f6b0-4fe1-aa22-c18268ce7c1d · outbound
This paper cites The Impact of Emerging Phishing Threats: Assessing Quishing and LLM-generated Phishing Emails against Organizations.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing The Impact of Emerging Phishing Threats: Assessing Quishing and LLM-generated Phishing Emails against Organizations
Reference 94
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T21:01:51.397461Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a614ad2f-375a-4d47-beff-f0e7d94a72ed · outbound
This paper cites an unresolved cited work.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Unresolved cited work
Reference 95
- Resolution
- unresolved
- raw_fallback, observed 2026-05-18T21:01:52.688472Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 05d2c720-3b13-4370-97e7-8b5b11aea66b · outbound
This paper cites A survey on llm-generated text detection: Ne- cessity, methods, and future directions.Com- putational Linguistics, 51(1):275–338.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing A survey on llm-generated text detection: Ne- cessity, methods, and future directions.Com- putational Linguistics, 51(1):275–338
Reference 96
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.643178Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation daf7f93e-5a29-4a27-99e1-0f1f7f1a72ec · outbound
This paper cites MultiPhishGuard: An Explainable and Adaptive Multi-Agent LLM System for Phishing Email Detection.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing MultiPhishGuard: An Explainable and Adaptive Multi-Agent LLM System for Phishing Email Detection
Reference 97
- Resolution
- verified exact
- arxiv_id, observed 2026-05-26T02:02:26.392055Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 48bad993-2cbc-4d67-a770-ecccd5b8dd2b · outbound
This paper cites Chain of attack: Hide your intention through multi-turn inter- rogation.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Chain of attack: Hide your intention through multi-turn inter- rogation
Reference 98
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.685149Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 6759e98f-ef77-4ce2-a094-6e263f9c5290 · outbound
This paper cites A survey on large language model (llm) security and privacy: The good,the bad,andthe ugly.High- Confidence Computing, 4(2):100211.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing A survey on large language model (llm) security and privacy: The good,the bad,andthe ugly.High- Confidence Computing, 4(2):100211
Reference 99
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.668184Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 8b3f892d-985a-4dc2-b8c5-fb086805e94d · outbound
This paper cites Prompt engineering for detecting phishing.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Prompt engineering for detecting phishing
Reference 100
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.656977Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 7fddc0cd-31ff-43e8-8dce-bf3d600b8082 · outbound
This paper cites Sok: Human-centered phishing susceptibility.ACM Transactions on Privacy and Security, 26(3):1– 27.
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing Sok: Human-centered phishing susceptibility.ACM Transactions on Privacy and Security, 26(3):1– 27
Reference 101
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T21:01:52.653544Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Pith citing papers
Observation 85dca295-e501-4268-a9aa-1cf1bd5bfa1f · inbound
The Impact of Editorial Intervention on Detecting Native Language Traces cites this paper.
The Impact of Editorial Intervention on Detecting Native Language Traces SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T14:26:44.302311Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.