{"as_of":"2026-08-13T21:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e8266780ea48d619db3c117fd9c39ccd0101dbb29acf4936f5a7549897cd0306","coverage":[{"denominator":86,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":86,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T18:39:41.471542Z","state":"measured"},{"denominator":88,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":88,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T12:43:16.760644Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-18T21:01:51.399296Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.11389","snapshot_observed_at":"2026-08-06T12:43:16.760644Z","title":"Adapting to cyber threats: A phishing evolu- tion network (pen) framework for phishing generation and analyzing evolution patterns using large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.21538","last_updated":"2025-07-29T07:11:11Z","snapshot_observed_at":"2026-08-10T18:24:50.209624Z","submitted_at":"2025-07-29T07:11:11Z","title":"Can We End the Cat-and-Mouse Game? Simulating Self-Evolving Phishing Attacks with LLMs and Genetic Algorithms","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T12:43:16.760644Z"},"links":{"cited_paper":"/paper/2411.11389","citing_paper":"/paper/2507.21538"},"observation_digest":"sha256:0a7c4905ba0edd1754be2d628d0a860543d856cc9dbf9b801571557bfdc7d4a9","observation_id":"ed0d4329-1abe-4c0e-82e6-d0fb88faac3e","resolution":{"observed_at":"2026-08-06T12:43:16.760644Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"cited_work":{"arxiv_id":"2411.11389","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.11389","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"3a0bf127-14bf-4509-9b34-2498da50ee27","year":2024},"citing_paper":{"arxiv_id":"2508.21457","last_updated":"2026-05-13T04:52:34Z","snapshot_observed_at":"2026-08-07T18:49:22.189633Z","submitted_at":"2025-08-29T09:39:46Z","title":"SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-18T20:57:03.172931Z"},"links":{"cited_paper":"/paper/2411.11389","citing_paper":"/paper/2508.21457"},"observation_digest":"sha256:dd6331b6b299dc67784ada2d198857d07a3487aba4b6ba061201646fe397c7c6","observation_id":"0628af0d-6c0d-498e-8f65-746c7af05a5e","resolution":{"observed_at":"2026-05-18T21:01:51.402303Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.11389/citation-record","integrity":"/paper/2411.11389/integrity","json":"/paper/2411.11389/citation-record.json","paper":"/paper/2411.11389"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.026769Z","title":"Fighting against phishing attacks: state of the art and future challenges,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.026769Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:f95044c10f224383f50bd05cb817d8a813aea42442c5216772d21f4e67405bc5","observation_id":"52eebfe3-609c-4c74-b422-9fa2d6063a36","resolution":{"observed_at":"2026-08-12T18:39:41.026769Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.032884Z","title":"Apwg 2024 phishing report,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.032884Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:53ea98b1528f136a4fd4075251b0d2893033030d9e6b31c670328ab778d04f48","observation_id":"dc61b9cc-ef0a-4db4-a8b9-983fadd7dd60","resolution":{"observed_at":"2026-08-12T18:39:41.032884Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.038081Z","title":"A systematic literature review on phishing email detection using natural language processing techniques,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.038081Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:de31fa7c52349e7539b00a512c1f080679800b2973bfa83902f05b53f4bb5c68","observation_id":"094b0d39-da07-4b02-be10-8ad344a71100","resolution":{"observed_at":"2026-08-12T18:39:41.038081Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.042928Z","title":"Applying machine learn- ing and natural language processing to detect phishing email,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.042928Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:2e9a7672d7e5a524ab597be3e9964de988c71e0023eae8dcf26a8dccf416a48e","observation_id":"795c7db4-9b49-4ba4-a0af-9dc9a342f6ef","resolution":{"observed_at":"2026-08-12T18:39:41.042928Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18093","last_updated":"2024-08-23T05:03:44Z","snapshot_observed_at":"2026-08-13T04:08:32.280624Z","submitted_at":"2024-02-28T06:28:15Z","title":"ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.18093","snapshot_observed_at":"2026-08-12T18:39:41.047995Z","title":"Chatspamdetector: Leveraging large language models for effective phishing email detection,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.047995Z"},"links":{"cited_paper":"/paper/2402.18093","citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:b5bb20941e5a860a7309bcc086f88c8f4d6da4fc480175eb7f9a0ef51c68dbfc","observation_id":"76bd6ce2-4460-4f49-9fb0-42ef6ed91a85","resolution":{"observed_at":"2026-08-12T18:39:41.047995Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.053192Z","title":"A survey of large language models for cyber threat detection,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.053192Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:da3cac7dae8b86e6f7a9bd5a732ccf2a3bfa74dff25c54a815303e2fa3bb3a0b","observation_id":"a2a2129e-2b39-431a-aada-13549a14b1ff","resolution":{"observed_at":"2026-08-12T18:39:41.053192Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.058674Z","title":"Towards security threats of deep learning systems: A survey,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.058674Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:4d57d2c2ecc2c3abb63b1fc1f036a154197ff7be9b5b475df2e1cc405bde1485","observation_id":"4db3bd31-35db-4b6a-9012-678d8486b5cb","resolution":{"observed_at":"2026-08-12T18:39:41.058674Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.064653Z","title":"Privacy engineering in the wild: Understanding the practitioners’ mindset, organizational aspects, and current practices,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.064653Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:d05fa36de150a1b5d428ca6951153186d3d2c67a723c9e7379b39ada6ca5896d","observation_id":"e45d0373-11fb-4941-b203-e8bf8a0299eb","resolution":{"observed_at":"2026-08-12T18:39:41.064653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.815669Z","title":"Sok: a comprehensive reexamination of phishing research from the security perspective,","venue":null,"work_id":"f8c03630-448a-4cfc-b6e0-426008b2429f","year":2019},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.069004Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:794f6165bfe489f3892443d3631bdcb8d2b29d70ec13992bb302496688462996","observation_id":"a2857ce2-7dc5-493a-bc18-dbcd2d562449","resolution":{"observed_at":"2026-08-12T18:39:42.821703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.798808Z","title":"Phish- ing email detection using natural language processing techniques: a literature survey,","venue":null,"work_id":"a0fb43c1-dc16-4b00-ae0c-540473d632fb","year":2021},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.074441Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:6cd8940b1173ecf0eb0ce18f8a8069d627782c42d77600044170700ad64ba503","observation_id":"8d43f7ec-2517-4d07-b131-4faf3e9822bf","resolution":{"observed_at":"2026-08-12T18:39:42.803764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.02033","last_updated":"2021-03-25T14:54:10Z","snapshot_observed_at":"2026-07-06T09:35:20.268205Z","submitted_at":"2020-07-04T07:45:04Z","title":"Text Data Augmentation: Towards better detection of spear-phishing emails","version":2},"cited_work":{"arxiv_id":"2007.02033","doi":null,"metadata_source":"pith","pith_arxiv_id":"2007.02033","snapshot_observed_at":"2026-08-12T18:39:41.726118Z","title":"Text Data Augmentation: Towards better detection of spear-phishing emails","venue":"cs.CL","work_id":"754b3aee-820d-48df-8325-de1f1df2719c","year":2020},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.079703Z"},"links":{"cited_paper":"/paper/2007.02033","citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:126bf360f12fbfdead5f5abb48f4c19a377f3a4155dba37deaa02ae75934f283","observation_id":"6b2786e1-129e-4272-b169-5d32a7ad72d5","resolution":{"observed_at":"2026-08-12T18:39:41.731627Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.782625Z","title":"Ad- versarial sampling attacks against phishing detection,","venue":null,"work_id":"abdfaf62-34ba-44bd-b201-9eddc65fcd79","year":2019},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.086006Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:f472b84009ebf7aba926d97c941d9ab8da8e1b7cbae488598ce0151170b19044","observation_id":"4532749d-1862-4a17-91a5-87f06b3adf5b","resolution":{"observed_at":"2026-08-12T18:39:42.787659Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.766251Z","title":"Enhancing detection of arabic social spam using data augmentation and machine learning,","venue":null,"work_id":"98fd70bb-8d99-4f13-8281-bf735e892424","year":2022},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.091644Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:fee51d272822c9a402f7d7e15f14f5264c57a1386bae51aeaf647d86d26e99cd","observation_id":"08deb28f-f7eb-4f8b-a391-e50e17bdc346","resolution":{"observed_at":"2026-08-12T18:39:42.771723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.749742Z","title":"Data augmenta- tion methods for enhancing robustness in text classifica- tion tasks,","venue":null,"work_id":"63337f70-01a5-441c-abeb-aeeb3247a035","year":2023},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.097179Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:cd034c0d76f979186c19204be3de26f94ceaf29565f7f1091588c14baa396c79","observation_id":"51c34854-171b-4835-b823-65d4984b24cb","resolution":{"observed_at":"2026-08-12T18:39:42.754753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.733704Z","title":"Adversarial examples generation method for chinese text classification,","venue":null,"work_id":"6c9ff454-b3ca-4eb0-ba1f-123628f16fc8","year":2022},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.102691Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:5477534f79eaee999bed6a65f598ff74ef8e0b3dd53311599c16b86572031ff2","observation_id":"58ae4c0d-50c6-4424-93a8-d733fcd8c317","resolution":{"observed_at":"2026-08-12T18:39:42.739144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.716877Z","title":"Rule-based adversarial sample generation for text classification,","venue":null,"work_id":"3e7ba33d-054e-4caa-9b94-e38c24e9a84e","year":2022},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.107184Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:4f2749da162aa73c562695d2572046489dc2ef614ea14b90809fb84d78252ab5","observation_id":"994e753e-8a57-405f-a28c-7f4d72b7cac0","resolution":{"observed_at":"2026-08-12T18:39:42.721809Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.700768Z","title":"An empirical survey of data augmentation for limited data learning in nlp,","venue":null,"work_id":"e3090e09-9a5f-4522-a42b-fecc30764442","year":2023},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.112149Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:16d5b6d13fdb37cf15f85ebc6f68ffccbb064169d7fdde7ef8ff81f27d128023","observation_id":"3c9e0dab-8486-4e1e-86cd-1b7ec442b2de","resolution":{"observed_at":"2026-08-12T18:39:42.705758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.684862Z","title":"Adversarial robustness of phishing email detection models,","venue":null,"work_id":"dcdc519d-28ce-4da1-b83c-f67f0afda822","year":2023},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.116707Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:c0e8675b4595ff5b8eb3dd41b551d46e68e843f631027efe64f773c66d12c3b3","observation_id":"4fc64b81-a4b4-4c6e-992d-0518752df25b","resolution":{"observed_at":"2026-08-12T18:39:42.689783Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.667433Z","title":"Data augmentation in classification and segmentation: A survey and new strategies,","venue":null,"work_id":"8275f75a-ce0a-4416-9f83-7e2596d4b532","year":2023},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.122326Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:403b07376b7d9dade7d7c07e1b39ed5758c87d4963f11a62f9d599ce8724c0f9","observation_id":"8bc3622b-6998-4367-a3fe-30691ec5d7c2","resolution":{"observed_at":"2026-08-12T18:39:42.672922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.650229Z","title":"Analysis and prevention of ai-based phishing email attacks,","venue":null,"work_id":"5617de23-2407-4291-b521-63ba4fdab232","year":2024},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.126944Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:389d1e126b08eca956ef9e66cd47b5314c6282f4591317e3aa40799f6f473ccb","observation_id":"12be2dc7-9fc1-4369-b474-3602008268e5","resolution":{"observed_at":"2026-08-12T18:39:42.656311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.630572Z","title":"Phishing or not phishing? a survey on the detection of phishing websites,","venue":null,"work_id":"c5af48af-46dc-4715-8c5e-6694c0cb65cf","year":2023},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.132217Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:5534e85420b7a9e51367f94e1c963b2d3debc504b4b3b8f3b240c425785678d8","observation_id":"81b90e9d-778d-4742-bf91-c86c6facde1a","resolution":{"observed_at":"2026-08-12T18:39:42.637612Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.12287","last_updated":"2023-11-30T22:17:41Z","snapshot_observed_at":"2026-08-13T10:28:51.819927Z","submitted_at":"2023-08-23T17:58:40Z","title":"Devising and Detecting Phishing: Large Language Models vs. Smaller Human Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.12287","snapshot_observed_at":"2026-08-12T18:39:41.136708Z","title":"Devising and detecting phishing: Large lan- guage models vs. smaller human models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.136708Z"},"links":{"cited_paper":"/paper/2308.12287","citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:059afd3392da69ae0169da37f63c1e83a317bd4ff176935f438a3372fa87be9b","observation_id":"806280cb-86dd-4b30-bca0-a3ebccd5b5a8","resolution":{"observed_at":"2026-08-12T18:39:41.136708Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.09727","last_updated":"2025-04-15T15:38:22Z","snapshot_observed_at":"2026-08-13T04:40:09.735001Z","submitted_at":"2024-01-18T05:06:39Z","title":"Lateral Phishing With Large Language Models: A Large Organization Comparative Study","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.09727","snapshot_observed_at":"2026-08-12T18:39:41.141716Z","title":"Large lan- guage model lateral spear phishing: A comparative study in large-scale organizational settings,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.141716Z"},"links":{"cited_paper":"/paper/2401.09727","citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:5cc5bcf10a3e3b5161b77cf502f3979881063fdd145b65a8a51e3b2e8d3fda8c","observation_id":"c3ca37bd-1410-4c7a-9780-79451661701b","resolution":{"observed_at":"2026-08-12T18:39:41.141716Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.612510Z","title":"A survey on explainable anomaly detection,","venue":null,"work_id":"5a3200c6-a12e-4c12-8b58-a6ce788fa6c5","year":2023},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.147233Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:6a89bbd92c0add17c7cf2eecbc7b6627335d4bbbd01ad64cb674e639003001d1","observation_id":"e50ce6e2-821a-46c2-9c0b-490dd6602de2","resolution":{"observed_at":"2026-08-12T18:39:42.617910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.595685Z","title":"Detection of ai-generated emails-a case study,","venue":null,"work_id":"b8ac743f-b786-4fff-b01e-7ea1a27ab41b","year":2024},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.151841Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:565c8882c451a56435cdc6c44ed8f38e4e4efd5b4336cedeb586dfdcbd950ed4","observation_id":"5da470cf-63f7-485e-a4b4-27472e435a90","resolution":{"observed_at":"2026-08-12T18:39:42.601276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.578531Z","title":"Phishing and social engineering attack prevention with llms,","venue":null,"work_id":"a84b2720-17a6-4f07-a323-e405397d4f4d","year":2025},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.156794Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:e999d578a54e494afe70cf57039c41b7fbb81916b4fc3303e3ec6c4c731a049f","observation_id":"3c0db142-7359-4b59-ab9c-62410411b312","resolution":{"observed_at":"2026-08-12T18:39:42.584159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.562474Z","title":null,"venue":null,"work_id":"25837909-94e8-4f88-b159-ce0cc73b2fe9","year":2024},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.162054Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:cbf7b6c8811565f67682d4216d86f6ac23a4cb695c42b4c132c5fe9242d268d7","observation_id":"90978786-70bd-45ed-ab3f-4bd66c45074a","resolution":{"observed_at":"2026-08-12T18:39:42.567446Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.11805","last_updated":"2025-05-09T21:04:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-19T02:39:27Z","title":"Gemini: A Family of Highly Capable Multimodal Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.11805","snapshot_observed_at":"2026-08-12T18:39:41.166967Z","title":"Gemini: a family of highly capable multi- modal models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.166967Z"},"links":{"cited_paper":"/paper/2312.11805","citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:29fe22efdf94dad2e15b6ec7e3a2647cdcf969531ac9fc091a53cf519be61b22","observation_id":"eae32383-399a-4372-93f2-6dee1da1265d","resolution":{"observed_at":"2026-08-12T18:39:41.166967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-11T08:20:29.798517Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09685","snapshot_observed_at":"2026-08-12T18:39:41.172683Z","title":"Lora: Low-rank adaptation of large language models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.172683Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:37758d1418d8e9ee7b30611c21e72f6da9dd7f3b9cd8af367b427bca271b28cb","observation_id":"86c9c4b2-1b95-4993-bf66-c7394fb5a341","resolution":{"observed_at":"2026-08-12T18:39:41.172683Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.544676Z","title":"Bi-lstm model to increase accuracy in text classification: Combining word2vec cnn and attention mechanism,","venue":null,"work_id":"0c7f45b7-1a63-4740-8e4b-74bbc057ce94","year":2020},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.177820Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:d5536184c830ebbcba737b4212e3ef5ff395b86f13a2f48c34dd39bd3224ee7e","observation_id":"fcbbffe8-77ed-4547-ba2c-96c853d80c2d","resolution":{"observed_at":"2026-08-12T18:39:42.550393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13871","last_updated":"2025-08-14T10:59:03Z","snapshot_observed_at":"2026-08-13T04:13:38.564867Z","submitted_at":"2024-02-21T15:23:21Z","title":"An Explainable Transformer-based Model for Phishing Email Detection: A Large Language Model Approach","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13871","snapshot_observed_at":"2026-08-12T18:39:41.182556Z","title":"An explainable transformer-based model for phishing email detection: A large language model approach,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.182556Z"},"links":{"cited_paper":"/paper/2402.13871","citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:f4298597cc24dee6a1f1bdedb13064fac3a9c6b9f9b708a985f6e7544fce7133","observation_id":"80a51a50-155a-4745-b468-ae2c27c94391","resolution":{"observed_at":"2026-08-12T18:39:41.182556Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.187427Z","title":"Chain-of-thought prompting elicits reasoning in large language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.187427Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:3923eb076654d7ec15a8853e1f517f73606f971a41f56fea6ab579149201a44b","observation_id":"4a683be3-42d8-44a4-837f-40c904c3fb68","resolution":{"observed_at":"2026-08-12T18:39:41.187427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.192687Z","title":"Isolation forest,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.192687Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:270a4eb74255dded8b4b283327165656bd5b1689a1c5d3853a015fdcaec240aa","observation_id":"038dcbc6-dfec-490d-8903-2cb65b719335","resolution":{"observed_at":"2026-08-12T18:39:41.192687Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.197680Z","title":"Latent dirichlet allocation,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.197680Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:5bdfd71e7a43f8ab985a286ee938092455b1573a660ef3c97e232b52c6c8b642","observation_id":"b2fa371b-9454-44a9-b8f3-6fe75349721f","resolution":{"observed_at":"2026-08-12T18:39:41.197680Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.203815Z","title":"The science of persuasion,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.203815Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:dd5b9d0aeae702f39d5eed1f69f170e9bc847f4319a84f69cd511f08bb591a22","observation_id":"d4a16c91-b51f-4702-a5bd-db28849b363b","resolution":{"observed_at":"2026-08-12T18:39:41.203815Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.480847Z","title":"Concept induction: Analyzing unstructured text with high-level concepts using lloom,","venue":null,"work_id":"9d2c9ada-3050-4552-a288-ff63fe4e7d2a","year":2024},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.209116Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:9c686340d07f7e006dbeda38d088f413c484271c7d36658ee22af5bcd6c675ad","observation_id":"8a56f8d8-e903-49f8-98e3-9efcf90b5833","resolution":{"observed_at":"2026-08-12T18:39:42.486197Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.462193Z","title":"The development and psychometric properties of liwc-22,","venue":null,"work_id":"eddc1d7e-44ad-4b06-b3b1-a35e50f37a1c","year":2022},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.215267Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:a8152de70181ba8070d349980ddf429614829d61d091398d92f5acb815190e4b","observation_id":"20741667-1779-4d82-b6e7-1887605950b3","resolution":{"observed_at":"2026-08-12T18:39:42.467962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.446280Z","title":"Optimizing semantic coherence in topic models,","venue":null,"work_id":"1dd2c7f4-c86a-4456-9e56-9d7937baacef","year":2011},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.220639Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:c89a996133ae1c7d57b7df369d64ccbd732676a541cd99c265f158e3e4889532","observation_id":"6594b9b5-999f-4226-90b7-e7147e8acefc","resolution":{"observed_at":"2026-08-12T18:39:42.451354Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.226525Z","title":"Class-based n-gram models of natural language,","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.226525Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:14e0bceb4c99a5c8d180e137c321407f585789515b5dc7fa4e53d946fd70bcca","observation_id":"1a9ea117-a2bc-4f22-ae75-8c9356fe60f4","resolution":{"observed_at":"2026-08-12T18:39:41.226525Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.418381Z","title":"Interpreting tf-idf term weights as making relevance decisions,","venue":null,"work_id":"9c9f2782-06f7-4fcf-90b3-af8d47ac2c9b","year":2008},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.231793Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:3c0bf894ba159adad2a75ec87434d7daf3b0a9c46d9a767fb169bf4e9744925d","observation_id":"f423a8c3-58a6-46f7-ab5d-0f7694b48861","resolution":{"observed_at":"2026-08-12T18:39:42.423305Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6572","last_updated":"2015-03-20T20:19:16Z","snapshot_observed_at":"2026-08-12T17:13:46.394331Z","submitted_at":"2014-12-20T01:17:12Z","title":"Explaining and Harnessing Adversarial Examples","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6572","snapshot_observed_at":"2026-08-12T18:39:41.236781Z","title":"Explaining and harnessing adversarial examples,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.236781Z"},"links":{"cited_paper":"/paper/1412.6572","citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:16b798a52196058b3204869419e461348133e5c9e8cf1257415b792a734be2e8","observation_id":"9b133c9a-c425-44a3-beca-b01e00ed3d90","resolution":{"observed_at":"2026-08-12T18:39:41.236781Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.402329Z","title":"Machine literature searching viii. operational criteria for designing information retrieval systems,","venue":null,"work_id":"cb20f53c-c0e6-4c20-86fa-ad253cf519a5","year":1986},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.242214Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:a30bf09de7747d75bbe905173818642366a6eba4a9caa7bcb29420492d5feb07","observation_id":"21dc6380-18e5-41b4-bdf5-f7100ca25a96","resolution":{"observed_at":"2026-08-12T18:39:42.407547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.385557Z","title":"Iwspa phishing dataset,","venue":null,"work_id":"1437478a-244c-4a06-945c-ea6117ce0a36","year":2021},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.247908Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:409125db958ee64d40de8817eb5469302657a7d25f6922ec38d83003d0556ec1","observation_id":"7d9e5658-1dc4-4084-bcfc-880bd47a00fa","resolution":{"observed_at":"2026-08-12T18:39:42.391085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.368009Z","title":"Nazario phishing dataset,","venue":null,"work_id":"a98057ee-7d4f-422a-b198-2a7313296eba","year":2021},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.252703Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:dfe974b014ea4fdc99043a09f22c0c01f555bf73f6b6eafe2c7ed85d8bf4c7ec","observation_id":"1aad8c3b-6ba3-48d2-aae5-1b90177859ff","resolution":{"observed_at":"2026-08-12T18:39:42.373022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.350436Z","title":"Miller smiles phishing dataset,","venue":null,"work_id":"99c3fc45-aeef-4a7c-bb41-ca976851754d","year":2021},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.257430Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:b0edc4a47be7358071b8f31d15481accde306a078f66fcabd8133abd2e813003","observation_id":"010805cb-29b1-435f-8468-c0068298df8f","resolution":{"observed_at":"2026-08-12T18:39:42.355948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.332747Z","title":"Phish bowl phishing dataset,","venue":null,"work_id":"14194c0f-2d66-495f-9695-53a8385621d8","year":2021},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.262079Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:27c92a48e22bb2b3d60a3fcc8bd08645241cbacf41c5e13696a3ec0175aa5642","observation_id":"f7b7a90b-4904-4582-b955-56350f24548e","resolution":{"observed_at":"2026-08-12T18:39:42.337586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.315357Z","title":"Nigerian fraud dataset,","venue":null,"work_id":"eb1f879a-9e13-43a3-b89d-32361181eb22","year":2021},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.267539Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:2dad2ffa74d21e04acd68f8e78dbd04858475bf47d62d98fd505205fca7aeacb","observation_id":"13b9ddc8-ea17-4f7a-9cf5-3fbb64c0c920","resolution":{"observed_at":"2026-08-12T18:39:42.320493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.296554Z","title":"An improved transformer-based model for detecting phishing, spam and ham emails: A large language model approach,","venue":null,"work_id":"07e16e0b-0043-499e-8a4d-f59fdd272d02","year":2024},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.272172Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:3671b13f44c2d3ec6c00dc058262ffbb66c12e13dfb97775b1ce9e138bd56f4f","observation_id":"e1836623-8c72-4a2a-abc7-c8fb779500c0","resolution":{"observed_at":"2026-08-12T18:39:42.302181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.278079Z","title":"The psychological meaning of words: Liwc and computerized text analysis methods,","venue":null,"work_id":"ec2ea781-d851-4db5-accb-3c56ed0416d4","year":2010},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.277068Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:2aa3c07b0d65b63d5f90931a0aad9e24c1f2a362db66975ea3c77dd64f5ace3d","observation_id":"7144e5d8-9ce1-4c11-ac9a-185ae0f97100","resolution":{"observed_at":"2026-08-12T18:39:42.284201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.259799Z","title":"Changing others’ beliefs online: Online comments’ persuasiveness,","venue":null,"work_id":"eef0c945-dcb8-4a1f-bbc3-4d98730cf0e8","year":2019},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.281790Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:e33685fa55df4bde8f1bc9992a4f4cf9baf62fe12ed196447c72eaa7f9a80b3e","observation_id":"1b6f42fe-f64d-44d0-bc52-6a7c56c1e160","resolution":{"observed_at":"2026-08-12T18:39:42.265494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.242734Z","title":"Cognitive triaging of phishing attacks,","venue":null,"work_id":"25f07e53-1070-42db-9c8e-760a88750a2e","year":2019},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.287223Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:7e7c24ec6f626803cee1ad02f22160f5afcc89d1e3dfe5d0093d275db5f3f07f","observation_id":"29549bb9-ebcc-4efc-aaad-bcf074a75523","resolution":{"observed_at":"2026-08-12T18:39:42.248062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.225775Z","title":"The development and psychometric properties of liwc2015,","venue":null,"work_id":"16413a6a-3c85-4e5b-b783-eb8cda486c06","year":2015},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.291935Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:b3a8872d4e76f59dee05334876c8bd7b6fd458b98b8a33e2dcb112edad83333d","observation_id":"098a2e7b-b673-4821-beef-98e6c52dfa41","resolution":{"observed_at":"2026-08-12T18:39:42.231202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02329","last_updated":"2021-03-22T11:44:30Z","snapshot_observed_at":"2026-08-03T21:26:51.269383Z","submitted_at":"2020-10-05T20:49:26Z","title":"InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02329","snapshot_observed_at":"2026-08-12T18:39:41.296911Z","title":"Infobert: Improving robustness of language models from an information theoretic perspective,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.296911Z"},"links":{"cited_paper":"/paper/2010.02329","citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:f873fac348da080711693ea7f4a38a9f98764ef071beccf649dad0dd1f9ef1ab","observation_id":"f8eb5c3d-3cb5-488b-aa7f-4c70ee035436","resolution":{"observed_at":"2026-08-12T18:39:41.296911Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.209128Z","title":"Generating nat- ural language adversarial examples through probability weighted word saliency,","venue":null,"work_id":"e99ac322-b563-4a56-af31-5984f5d53553","year":2019},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.301787Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:22095e7eb6dc0a5adde7e4a03d096b58397671a4a903858f58b9f2eb63b2f42d","observation_id":"20668650-a631-44d9-b9fa-9a8db1d143e8","resolution":{"observed_at":"2026-08-12T18:39:42.214504Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11268","last_updated":"2019-08-29T15:20:17Z","snapshot_observed_at":"2026-08-10T13:58:14.488263Z","submitted_at":"2019-05-27T14:35:35Z","title":"Combating Adversarial Misspellings with Robust Word Recognition","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.11268","snapshot_observed_at":"2026-08-12T18:39:41.306847Z","title":"Combating adversarial misspellings with robust word recognition,","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.306847Z"},"links":{"cited_paper":"/paper/1905.11268","citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:14402bcc057a18d76187f08582bbab0643ddd15f5c24e565a17138a52e7d0505","observation_id":"f4bf9bb6-2251-4f7f-8c11-71b92cf0e99a","resolution":{"observed_at":"2026-08-12T18:39:41.306847Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.191867Z","title":"Black-box generation of adversarial text sequences to evade deep learning classifiers,","venue":null,"work_id":"422632ab-00aa-49af-a110-eb6d031a5853","year":2018},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.311808Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:e6d868090a7a74a0e990efabdbb6cd16fc10623a13bfb9e37cba21f233112c34","observation_id":"86f85598-28a7-4237-b0df-d041620a112a","resolution":{"observed_at":"2026-08-12T18:39:42.197184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.174591Z","title":"Chatgpt and a new academic reality: Arti- ficial intelligence-written research papers and the ethics of the large language models in scholarly publishing,","venue":null,"work_id":"bd6996c6-f321-460b-9136-ab3c80eadece","year":2023},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.316618Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:52397d24754c93d924bbebf1164a411ed1d33c5ac0dd8f7b64e91546b9f3d4cc","observation_id":"1dbde074-7a50-4f6b-99ae-7efa81fc7f48","resolution":{"observed_at":"2026-08-12T18:39:42.179947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.158334Z","title":"Social engineering in cybersecurity: Effect mechanisms, human vulnerabilities and attack methods,","venue":null,"work_id":"efdc9b0f-61f1-4d7b-958f-0ec2445e0f27","year":2021},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.321781Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:f4e6666b90b82c479029e272858b5a5dacff78d47d1f9692171af1046a375913","observation_id":"8c680be2-342f-403a-a74b-4a740335df54","resolution":{"observed_at":"2026-08-12T18:39:42.163516Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.141400Z","title":"Udh: Universal deep hiding for steganography, water- marking, and light field messaging,","venue":null,"work_id":"55098ca4-bc4a-4803-ae9f-8cc792bd7408","year":2020},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.327432Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:05d4c83af1cc27f8007b3df6329709af341f39af71d2aa9289ea2d704920828e","observation_id":"4e498037-3c7e-48a9-917d-485cfcf4edb8","resolution":{"observed_at":"2026-08-12T18:39:42.146765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.124696Z","title":"Towards adversarial phishing detection,","venue":null,"work_id":"eac02d1c-8965-4c76-9eaa-23e562746da6","year":2020},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.332436Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:2c574d2dd00c2c1299876d04c2b15f678d6f7b663064e2fcd3bcd0e7abe2ece9","observation_id":"953bcc34-2c45-4aae-8861-a482e637827c","resolution":{"observed_at":"2026-08-12T18:39:42.130034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.16230","last_updated":"2024-07-23T07:14:50Z","snapshot_observed_at":"2026-08-12T23:16:43.492393Z","submitted_at":"2024-07-23T07:14:50Z","title":"Hooked: A Real-World Study on QR Code Phishing","version":1},"cited_work":{"arxiv_id":"2407.16230","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.16230","snapshot_observed_at":"2026-08-12T18:39:41.563760Z","title":"Hooked: A Real-World Study on QR Code Phishing","venue":"cs.CR","work_id":"a95805c5-a6c4-43b1-b893-7e36a267964e","year":2024},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.337679Z"},"links":{"cited_paper":"/paper/2407.16230","citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:5e2238a372dae1c04f9857f5e47e6c5b5fb9bf95ce9813bb9dfe404fbe77fbfc","observation_id":"cb435f40-9737-4873-a9d9-a353caa279bf","resolution":{"observed_at":"2026-08-12T18:39:41.570766Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.107928Z","title":"An image is worth a thousand toxic words: A metamorphic testing framework for content moderation software,","venue":null,"work_id":"06009e75-95be-4d31-abbe-ad54a44fcb28","year":2023},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.343302Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:8421a1c9ef0605bed8c6f6058bc457e4d1cacdc4466409ef28a31073abdadc1b","observation_id":"4a1c88b9-2786-4faa-9229-c014a8dbfae7","resolution":{"observed_at":"2026-08-12T18:39:42.113311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.089715Z","title":"{KnowPhish}: Large lan- guage models meet multimodal knowledge graphs for enhancing {Reference-Based} phishing detection,","venue":null,"work_id":"57e12b28-de5d-4e9e-9564-8cd11be2574d","year":2024},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.348426Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:d573dc38ff8447f7d0392a6b2191da1a6340481eb638309dcb8a4f8dd8cd2028","observation_id":"a5d29b2f-5e2b-431c-87e2-442db2712b54","resolution":{"observed_at":"2026-08-12T18:39:42.095491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.071000Z","title":"From chatbots to phishbots?: Phishing scam generation in commercial large language models,","venue":null,"work_id":"9c8d7e68-94b8-4630-a541-4980e1eabb36","year":2024},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.353430Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:823ebf80909952ef0d5f97810aa6c1a71603e9c9a4f3f474f77bdf41290f0e5b","observation_id":"f130d1d3-16cc-4651-ac58-609dfbab8fd9","resolution":{"observed_at":"2026-08-12T18:39:42.077017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.051366Z","title":"Chatgpt’s security risks and benefits: of- fensive and defensive use-cases, mitigation measures, and future implications,","venue":null,"work_id":"da4cace5-a22d-4792-90ea-b88a36d2f2d0","year":2024},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.358762Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:7a0a901579e0ac0fdea0a74ec46f463d58cd0b7f3195dfdaec1558db41778721","observation_id":"ff071e10-7593-484b-a28b-60f79d68adae","resolution":{"observed_at":"2026-08-12T18:39:42.057344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.033217Z","title":"A survey on dataset quality in machine learning,","venue":null,"work_id":"fd2a5b6e-e3cb-4133-a160-c07054522d56","year":2023},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.364941Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:5cb774c4d02808e71131857dd3733a343468b98ea31b5b74c01b2fec423c94b3","observation_id":"d21a6dae-35de-42ab-a19f-5b801ba2af03","resolution":{"observed_at":"2026-08-12T18:39:42.038508Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:42.015515Z","title":"Generating optimal attack paths in generative adversarial phishing,","venue":null,"work_id":"3279f0ae-b64c-4fde-8879-df1aee6e7f02","year":2021},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.370287Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:b26fc72e66a21ae7ac0ac5b5aa96cb1cfd6d1986d3030428dabaa9b6c4e4f547","observation_id":"8e6a8c87-880e-44e8-8279-27b1c42e91ae","resolution":{"observed_at":"2026-08-12T18:39:42.020780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.997178Z","title":"Weaponizing data science for social engineering: Automated e2e spear phishing on twitter,","venue":null,"work_id":"8c0ee788-8c63-4fc9-9133-51e895fe9d09","year":2016},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.375410Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:5a6b31704ca612ad398654e27fbfe1d7a5d45cca9ba45e3428747756051fbe95","observation_id":"ca6f4505-373e-46a4-b538-1f22275790c0","resolution":{"observed_at":"2026-08-12T18:39:42.002936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.380768Z","title":"Is bert really robust? a strong baseline for natural language attack on text classification and entailment,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.380768Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:405d611ff3387b988801d42561ccc84e551c0b40d298d62b86dbfa2c5ba58b06","observation_id":"e5e2031d-3ca6-420c-be97-b984434d745f","resolution":{"observed_at":"2026-08-12T18:39:41.380768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.970279Z","title":"Social engineering in cybersecurity: The evolution of a concept,","venue":null,"work_id":"79a67fc3-f985-4fdb-ab43-51015e30e505","year":2018},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.386811Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:d0afb8107cf3d224b012fc01c4cf7bc353feacdfc566c054c864e04be87704f6","observation_id":"1f4f1b29-d783-4968-a3bd-5f9b869bbe0e","resolution":{"observed_at":"2026-08-12T18:39:41.975456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.953935Z","title":"Defining social engineer- ing in cybersecurity,","venue":null,"work_id":"9f93e603-f382-4f92-b47b-7112e3039d07","year":2020},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.392894Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:ee3bd3e03122b371b17526c48d710795f483902d6817ba23a522a86d684c4368","observation_id":"aa3bcd12-4813-43b3-afe5-c31cb5cd805c","resolution":{"observed_at":"2026-08-12T18:39:41.959531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.936167Z","title":"Email summarization to assist users in phishing iden- tification,","venue":null,"work_id":"03f8548a-0a8f-4bf7-b3db-6135b71adb9a","year":2022},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.398641Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:e3ff4eabef7119cf983d3e573471425bddd7c8c3a6afec8c27e406bb0af0e99f","observation_id":"2216b263-2f0c-47cd-8657-06460f1457d0","resolution":{"observed_at":"2026-08-12T18:39:41.942058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.916903Z","title":"Email phishing and signal detection: How persuasion principles and personality influence response patterns and accuracy,","venue":null,"work_id":"3482b159-429e-400c-b291-32e92135c54d","year":2020},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.403593Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:8bb58570ef30f7705b5dba7ea495a0f664322c9a2dd5c93ab7f1a633f14d571d","observation_id":"e5f25af4-262e-4669-95fa-3f6b8a1cc229","resolution":{"observed_at":"2026-08-12T18:39:41.922358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.900395Z","title":"Detection method of phish- ing email based on persuasion principle,","venue":null,"work_id":"7e11d404-18c7-4b34-82e2-960be3aa0790","year":2020},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.408576Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:d26b6de90f645391210a2c974b4a04f1fb9c65e3d373b5fdf510c302fc6a1853","observation_id":"4a6da0a2-010d-4217-9c01-ca7f0fb10e2d","resolution":{"observed_at":"2026-08-12T18:39:41.905775Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.883417Z","title":"Utilizing large language models with human feedback integration for generating dedicated warning for phishing emails,","venue":null,"work_id":"fb6da842-2276-48c4-ae87-583dd202557c","year":2024},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.413804Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:5f65ea2c357082b805d1aa42a3c02599e01a8376100f1da48627cfe6106c109e","observation_id":"2eec8388-374a-486a-8ef1-621b4f4666e6","resolution":{"observed_at":"2026-08-12T18:39:41.889090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.13715","last_updated":"2023-10-15T07:55:59Z","snapshot_observed_at":"2026-08-13T05:49:10.360886Z","submitted_at":"2023-10-15T07:55:59Z","title":"Digital Deception: Generative Artificial Intelligence in Social Engineering and Phishing","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.13715","snapshot_observed_at":"2026-08-12T18:39:41.419279Z","title":"Digital deception: Gen- erative artificial intelligence in social engineering and phishing,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.419279Z"},"links":{"cited_paper":"/paper/2310.13715","citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:2073a5b7bca6ab341a735c7444bcd4de170548af8484435319f798b27355e09a","observation_id":"5245d107-19be-4100-95f3-099899bfdf88","resolution":{"observed_at":"2026-08-12T18:39:41.419279Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.866917Z","title":"Social engineering at- tacks: A survey,","venue":null,"work_id":"3f73eab9-78f8-463b-a96c-77d9dbdd99e2","year":2019},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.425317Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:afe514cf648085992f9c846f59e5db9baab8979188e75e63b4878cfac0bb7a6e","observation_id":"0a1a05e6-5607-4f49-973d-b8445b6a248d","resolution":{"observed_at":"2026-08-12T18:39:41.872099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.849236Z","title":"Ai2tale: An innovative information theory-based approach for learning to localize phishing attacks,","venue":null,"work_id":"31f7fe8d-3887-4552-9de1-bfea994e40b6","year":2025},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.430238Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:00271756b94fe1d6256449f3d4a5c254512d1a9ef7d4025804743f7f548ceb26","observation_id":"74a0f2c4-e3cf-4a1b-8a91-ad495a96bcbc","resolution":{"observed_at":"2026-08-12T18:39:41.855218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.435087Z","title":"Generative adversarial nets,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.435087Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:8a594fb2ead75e9e75cd4c73a1a94fb753bb2d40233949d3d57cb904ae6d303d","observation_id":"03dee996-bbb4-4ca1-af87-3273fbd90921","resolution":{"observed_at":"2026-08-12T18:39:41.435087Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1411.1784","last_updated":"2014-11-06T22:33:22Z","snapshot_observed_at":"2026-08-12T18:51:00.108451Z","submitted_at":"2014-11-06T22:33:22Z","title":"Conditional Generative Adversarial Nets","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1411.1784","snapshot_observed_at":"2026-08-12T18:39:41.440306Z","title":"Conditional generative ad- versarial nets,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.440306Z"},"links":{"cited_paper":"/paper/1411.1784","citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:d1d4a726a95c939a06e43f74ea3e79a77186210439fe8cad9a39140cbabad6df","observation_id":"a78484e5-beec-42f1-9e1a-74dbd407442d","resolution":{"observed_at":"2026-08-12T18:39:41.440306Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.822063Z","title":null,"venue":null,"work_id":"ce476142-4839-49fc-8e33-c2134f54392b","year":2024},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.445790Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:c6b32bb2d996057a0ba9904ae329928752b9941024840d578b660a87410e1799","observation_id":"e8054ac3-3206-4fa8-b86b-8599eaeef13f","resolution":{"observed_at":"2026-08-12T18:39:41.827141Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13276","last_updated":"2023-05-23T03:39:44Z","snapshot_observed_at":"2026-08-13T11:37:00.999314Z","submitted_at":"2023-05-22T17:36:58Z","title":"Evaluating ChatGPT's Performance for Multilingual and Emoji-based Hate Speech Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13276","snapshot_observed_at":"2026-08-12T18:39:41.450816Z","title":"Evaluating chatgpt’s performance for multilingual and emoji-based hate speech detection,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.450816Z"},"links":{"cited_paper":"/paper/2305.13276","citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:35cb64bc12d842df7cfd2b5ce60566f6f3a566d41854a3d8fa1ef7aac46c0e4c","observation_id":"c98282c8-1aab-44c1-9715-e45de82ee1a7","resolution":{"observed_at":"2026-08-12T18:39:41.450816Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.805469Z","title":"Is chatgpt better than human annotators? potential and limitations of chatgpt in explaining implicit hate speech,","venue":null,"work_id":"c89eb837-69fc-4f1f-a4be-f4ae62f825b7","year":2023},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.456183Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:771cf0372398d7639c54ea0a8ccf44d5b92fe8cc225a2b57eec2ab885962bd21","observation_id":"429f8a9e-6e80-4d63-88bb-9f217533fadf","resolution":{"observed_at":"2026-08-12T18:39:41.810918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.787094Z","title":"Cross-validation methods,","venue":null,"work_id":"87ac5eff-8d2e-4251-a212-61914bdebbe1","year":2000},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.461231Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:e1e590b2d4c2d22d44b6bd55e9a0fd44f0955e912dcb7d79730a8a7cc43eea24","observation_id":"bcb0d1f9-8ed2-41de-bd17-5c6149c80de8","resolution":{"observed_at":"2026-08-12T18:39:41.792595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.466730Z","title":"Language models are unsupervised multitask learners,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.466730Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:77a354ac65e7b60d74cf96b1edd869e3691aeb1149de52b5a36f854df34bd3be","observation_id":"29c487e1-ef66-4e23-a5ab-1d268dd5d915","resolution":{"observed_at":"2026-08-12T18:39:41.466730Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:39:41.759489Z","title":"Nigerian","venue":null,"work_id":"fd089fb5-9a34-4d8a-93f9-b6e41a6d9758","year":2018},"citing_paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-12T18:39:41.471542Z"},"links":{"citing_paper":"/paper/2411.11389"},"observation_digest":"sha256:cf3ec1fd7d9a697aa7954b5b0262ca01d76dfa33fa84bd5003545f41a00a871e","observation_id":"33a439d9-b607-413c-bf89-039db150184a","resolution":{"observed_at":"2026-08-12T18:39:41.764849Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.11389","last_updated":"2025-05-06T02:56:53Z","latest_version":2,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-12T18:31:53.482836Z","submitted_at":"2024-11-18T09:03:51Z","title":"PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models"},"reference_resolution":{"displayed":86,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":29,"verified_exact":2,"verified_fuzzy":54},"total_outbound_references":86},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 86 of 86 outbound references and 2 inbound Pith citation observations for arXiv:2411.11389."}