Phishing in Organizations: Findings from a Large-Scale and Long-Term Study
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Abstract (arXiv)
In this paper, we present findings from a large-scale and long-term phishing experiment that we conducted in collaboration with a partner company. Our experiment ran for 15 months during which time more than 14,000 study participants (employees of the company) received different simulated phishing emails in their normal working context. We also deployed a reporting button to the company's email client which allowed the participants to report suspicious emails they received. We measured click rates for phishing emails, dangerous actions such as submitting credentials, and reported suspicious emails. The results of our experiment provide three types of contributions. First, some of our findings support previous literature with improved ecological validity. One example of such results is good effectiveness of warnings on emails. Second, some of our results contradict prior literature and common industry practices. Surprisingly, we find that embedded training during simulated phishing exercises, as commonly deployed in the industry today, does not make employees more resilient to phishing, but instead it can have unexpected side effects that can make employees even more susceptible to phishing. And third, we report new findings. In particular, we are the first to demonstrate that using the employees as a collective phishing detection mechanism is practical in large organizations. Our results show that such crowd-sourcing allows fast detection of new phishing campaigns, the operational load for the organization is acceptable, and the employees remain active over long periods of time.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Dec 2021.
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Citing papers per year: 2024: 2; 2025: 1; 2026: 1.
4 papers.
- 2024When LLMs Go Online: The Emerging Threat of Web-Enabled LLMscited by 24
- 2024Lateral Phishing With Large Language Models: A Large Organization Comparative Studycited by 12
- 2025PiMRef: Deducing Ever-evolving Spear-phishing Emails with Knowledge Base Invariantscited by 1
- 2026The End of Trust: How Agentic AI Breaks Security Assumptionscited by 0
- 2026The End of Trust: How Agentic AI Breaks Security Assumptions14 May 2026
- 2025PiMRef: Deducing Ever-evolving Spear-phishing Emails with Knowledge Base Invariants21 Jul 2025
- 2024When LLMs Go Online: The Emerging Threat of Web-Enabled LLMs18 Oct 2024
- 2024Lateral Phishing With Large Language Models: A Large Organization Comparative Study18 Jan 2024