The Impact of Emerging Phishing Threats: Assessing Quishing and LLM-generated Phishing Emails against Organizations
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Abstract (arXiv)
Modern organizations are persistently targeted by phishing emails. Despite advances in detection systems and widespread employee training, attackers continue to innovate, posing ongoing threats. Two emerging vectors stand out in the current landscape: QR-code baits and LLM-enabled pretexting. Yet, little is known about the effectiveness of current defenses against these attacks, particularly when it comes to real-world impact on employees. This gap leaves uncertainty around to what extent related countermeasures are justified or needed. Our work addresses this issue. We conduct three phishing simulations across organizations of varying sizes -- from small-medium businesses to a multinational enterprise. In total, we send over 71k emails targeting employees, including: a "traditional" phishing email with a click-through button; a nearly-identical "quishing" email with a QR code instead; and a phishing email written with the assistance of an LLM and open-source intelligence. Our results show that quishing emails have the same effectiveness as traditional phishing emails at luring users to the landing webpage -- which is worrying, given that quishing emails are much harder to identify even by operational detectors. We also find that LLMs can be very good "social engineers": in one company, over 30% of the emails opened led to visiting the landing webpage -- a rate exceeding some prior benchmarks. Finally, we complement our study by conducting a survey across the organizations' employees, measuring their "perceived" phishing awareness. Our findings suggest a correlation between higher self-reported awareness and organizational resilience to phishing attempts.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 May 2025.
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Citing papers per year: 2025: 2; 2026: 3.
5 papers.
- 2026When Bots Take the Bait: Exposing and Mitigating the Emerging Social Engineering Attack in Web Automation Agentcited by 7
- 2025E-PhishGen: Unlocking Novel Research in Phishing Email Detectioncited by 4
- 2025Can AI Models be Jailbroken to Phish Elderly Victims? An End-to-End Evaluationcited by 1
- 2026Assessing Spear-Phishing Website Generation in Large Language Model Coding Agentscited by 0
- 2026The Fragility of Trigger-Tag Mechanisms for Misuse Detection in Open-Weight LLMscited by 0
- 2026The Fragility of Trigger-Tag Mechanisms for Misuse Detection in Open-Weight LLMs2 Oct 2026
- 2026Assessing Spear-Phishing Website Generation in Large Language Model Coding Agents13 Feb 2026
- 2026When Bots Take the Bait: Exposing and Mitigating the Emerging Social Engineering Attack in Web Automation Agent12 Jan 2026
- 2025Can AI Models be Jailbroken to Phish Elderly Victims? An End-to-End Evaluation13 Nov 2025
- 2025E-PhishGen: Unlocking Novel Research in Phishing Email Detection1 Sep 2025