SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation | Paper Citation Record

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SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Unresolved cited work

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This paper cites phishing feed.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation phishing feed

Reference 2

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This paper cites an unresolved cited work.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Unresolved cited work

Reference 3

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This paper cites phishtank,.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation phishtank,

Reference 4

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SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Unresolved cited work

Reference 5

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Observation e3a7a0ce-5d8d-4982-931c-a2d3922275c4 · outbound

This paper cites A phishing mitigation solution using human behaviour and emotions that influence the success of phishing attacks.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation A phishing mitigation solution using human behaviour and emotions that influence the success of phishing attacks

Reference 6

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This paper cites Advancing phishing email detection: A comparative study of deep learning models.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Advancing phishing email detection: A comparative study of deep learning models

Reference 7

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This paper cites https://www.anthropic.com/news/introducing-claude.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation https://www.anthropic.com/news/introducing-claude

Reference 8

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Observation b37cb39b-dc5d-4ded-a60b-f4c5225a5345 · outbound

This paper cites Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks

Reference 9

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Observation 66c7dea3-b133-437e-b1f7-6c7fc158ab03 · outbound

This paper cites Lateral Phishing With Large Language Models: A Large Organization Comparative Study.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Lateral Phishing With Large Language Models: A Large Organization Comparative Study

Reference 10

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This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 11

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This paper cites Cre- ative natural language generation.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Cre- ative natural language generation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.768518Z

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This paper cites I., R ABBI , F., AND ZIBRAN , M.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation I., R ABBI , F., AND ZIBRAN , M

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.761187Z

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

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Jailbreaking Black Box Large Language Models in Twenty Queries

Reference 14

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This paper cites Safe RLHF: Safe Reinforcement Learning from Human Feedback.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Safe RLHF: Safe Reinforcement Learning from Human Feedback

Reference 15

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Observation 30ceaedf-2531-4ca9-a8f2-8b00ed4f2991 · outbound

This paper cites Masterkey: Automated jailbreaking of large language model chatbots.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Masterkey: Automated jailbreaking of large language model chatbots

Reference 16

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This paper cites S., M ARRELLA , A., C ATARCI , T., AND COSTABILE , M.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation S., M ARRELLA , A., C ATARCI , T., AND COSTABILE , M

Reference 17

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Observation bf686269-4e99-4e32-81b7-1783e3e713f1 · outbound

This paper cites BERT: Pre-training of deep bidirectional transformers for language understand- ing.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation BERT: Pre-training of deep bidirectional transformers for language understand- ing

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.738487Z

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Observation 27d8c300-16b9-405e-8eda-01e2977f51b6 · outbound

This paper cites BERT: Pre-training of deep bidirectional transformers for language understand- ing.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation BERT: Pre-training of deep bidirectional transformers for language understand- ing

Reference 19

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Observation c54b8a1d-4ade-4e73-93d7-5206c379b2cc · outbound

This paper cites Phish responder: A hybrid machine learning approach to detect phishing and spam emails.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Phish responder: A hybrid machine learning approach to detect phishing and spam emails

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.725882Z

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Observation 48f4668e-9522-435a-ace5-680d8e3302f1 · outbound

This paper cites D., AND HEARST , M.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation D., AND HEARST , M

Reference 21

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Observation 8534b1db-af8d-4f64-a717-a43a66096e22 · outbound

This paper cites The phishing landscape 2023, [online].

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation The phishing landscape 2023, [online]

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.712290Z

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Observation e90a10b7-b875-4345-89c4-3a6494d530ee · outbound

This paper cites A comprehensive dual-layer architecture for phishing and spam email detection.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation A comprehensive dual-layer architecture for phishing and spam email detection

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.705475Z

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Observation 3757171b-05db-461a-a37d-d1bbada69c16 · outbound

This paper cites Devising and Detecting Phishing: Large Language Models vs. Smaller Human Models.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Devising and Detecting Phishing: Large Language Models vs. Smaller Human Models

Reference 24

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

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Reference 25

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Observation 51a52641-29b4-4b05-8789-4f375929dc6a · outbound

This paper cites The design and evaluation of a theory-based intervention to promote security behaviour against phish- ing.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation The design and evaluation of a theory-based intervention to promote security behaviour against phish- ing

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.698150Z

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This paper cites Adam: A Method for Stochastic Optimization.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Adam: A Method for Stochastic Optimization

Reference 27

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This paper cites The enron corpus: A new dataset for email classification research.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation The enron corpus: A new dataset for email classification research

Reference 28

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Observation 19c933f5-0ccd-43d0-8a8a-6fad79e2ccbf · outbound

This paper cites ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection

Reference 29

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This paper cites V., B UCKLEY , C., PHANG , J., B OWMAN , S.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation V., B UCKLEY , C., PHANG , J., B OWMAN , S

Reference 30

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Observation c01316c5-ec25-40a3-b1c4-3451f9499bc5 · outbound

This paper cites The value, benefits, and concerns of generative ai-powered assistance in writing.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation The value, benefits, and concerns of generative ai-powered assistance in writing

Reference 31

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Observation e0e902fb-090e-4171-b119-b719fcd96a12 · outbound

This paper cites https://www.bitdefender.com/solutions/trafficlight.html.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation https://www.bitdefender.com/solutions/trafficlight.html

Reference 32

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Observation c788bb24-4ca7-42bf-8e65-1ae1f22e3c78 · outbound

This paper cites Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study

Reference 33

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This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 34

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Observation 2b09ff6f-28a8-459d-a149-4e8803cf23fd · outbound

This paper cites LLM Critics Help Catch LLM Bugs.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation LLM Critics Help Catch LLM Bugs

Reference 35

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This paper cites M., T HABTAH , F., AND MCCLUSKEY , L.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation M., T HABTAH , F., AND MCCLUSKEY , L

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.660613Z

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Observation 4adf2ee8-bd40-456e-a03e-1446afe534fc · outbound

This paper cites Improving malicious email detection through novel designated deep-learning architectures utilizing entire email.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Improving malicious email detection through novel designated deep-learning architectures utilizing entire email

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.654412Z

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Observation d9e7c57d-bb83-44b8-9071-a1551920b754 · outbound

This paper cites Identifying the level of user awareness and factors on phishing attempt among students.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Identifying the level of user awareness and factors on phishing attempt among students

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.648019Z

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Observation d4d855ec-530f-4dd4-a7dc-d8b307621de6 · outbound

This paper cites PhishTime: Continuous longi- tudinal measurement of the effectiveness of anti-phishing blacklists.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation PhishTime: Continuous longi- tudinal measurement of the effectiveness of anti-phishing blacklists

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.640362Z

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Observation ce202d89-78a6-425b-b442-65534e7fbbd7 · outbound

This paper cites Sunrise to sunset: Analyzing the end-to-end life cycle and effectiveness of phish- ing attacks at scale.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Sunrise to sunset: Analyzing the end-to-end life cycle and effectiveness of phish- ing attacks at scale

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.632693Z

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Observation e0cdf39c-9c0c-434a-a2da-75be9db62e5c · outbound

This paper cites https://openai.com/index/gpt-4/.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation https://openai.com/index/gpt-4/

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.624647Z

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Observation 714613fb-e5d1-4d4e-a4fe-2f3a2921479c · outbound

This paper cites Training language models to follow instructions with hu- man feedback.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Training language models to follow instructions with hu- man feedback

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.615982Z

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Observation e8793a5e-f30f-49d9-88ee-ab783fd010d8 · outbound

This paper cites to click or not to click is the question.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation to click or not to click is the question

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.607422Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8c88d3df-85aa-4591-9b8d-0a6408e0ea1f · outbound

This paper cites Creative persuasion: a study on adversarial behaviors and strategies in phishing attacks.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Creative persuasion: a study on adversarial behaviors and strategies in phishing attacks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.600144Z

Source-reported events for the cited work

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Observation 23989529-7c76-43f2-b000-645705f10ca2 · outbound

This paper cites From Chatbots to PhishBots? -- Preventing Phishing scams created using ChatGPT, Google Bard and Claude.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation From Chatbots to PhishBots? -- Preventing Phishing scams created using ChatGPT, Google Bard and Claude

Reference 45

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Observation bd65a748-59c5-47fe-a7fc-ea9c99e9aa56 · outbound

This paper cites D., AND STAMATOPOULOS , P.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation D., AND STAMATOPOULOS , P

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.592718Z

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Observation b9fc1164-9b31-451a-818d-0949d485e344 · outbound

This paper cites Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation

Reference 47

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Observation eeb27f23-edc5-4b1e-bc58-98e63516a5fc · outbound

This paper cites Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challenges.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challenges

Reference 48

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Observation fb3c7e10-e19d-42a5-8626-e6cd6ee348a1 · outbound

This paper cites an unresolved cited work.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Unresolved cited work

Reference 49

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Source-reported events for the cited work

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Observation ea3c8440-bbbd-41fc-8e97-04387834397c · outbound

This paper cites J., H INDS , J., AND JOINSON , A.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation J., H INDS , J., AND JOINSON , A

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.576977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 82988d3d-4659-4baa-8b04-58e18ea7aeb1 · outbound

This paper cites Personalized persuasion: Quan- tifying susceptibility to information exploitation in spear-phishing at- tacks.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Personalized persuasion: Quan- tifying susceptibility to information exploitation in spear-phishing at- tacks

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.567821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 894f27ee-f34b-4f90-a4cb-36c856540de2 · outbound

This paper cites SEED-Story: Multimodal Long Story Generation with Large Language Model.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation SEED-Story: Multimodal Long Story Generation with Large Language Model

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-11T15:21:48.443272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e08ee3f9-b1fd-4c8e-9221-594398135074 · outbound

This paper cites Invita- tion to Exclusive Bridge Builders Webinar.

SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation Invita- tion to Exclusive Bridge Builders Webinar

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:21:48.560250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers