Users really do respond to smishing
Not on the map. Why?
Abstract (arXiv)
Text phish messages, referred to as Smishing is a type of social engineering attack where fake text messages are created, and used to lure users into responding to those messages. These messages aim to obtain user credentials, install malware on the phones, or launch smishing attacks. They ask users to reply to their message, click on a URL that redirects them to a phishing website, or call the provided number. Thousands of mobile users are affected by smishing attacks daily. Drawing inspiration by the works of Tu et al. (USENIX Security, 2019) on Robocalls and Tischer et al. (IEEE Symposium on Security and Privacy, 2016) on USB drives, this paper investigates why smishing works. Accordingly, we designed smishing experiments and sent phishing SMSes to 265 users to measure the efficacy of smishing attacks. We sent eight fake text messages to participants and recorded their CLICK, REPLY, and CALL responses along with their feedback in a post-test survey. Our results reveal that 16.92% of our participants had potentially fallen for our smishing attack. To test repeat phishing, we subjected a set of randomly selected participants to a second round of smishing attacks with a different message than the one they received in the first round. As a result, we observed that 12.82% potentially fell for the attack again. Using logistic regression, we observed that a combination of user REPLY and CLICK actions increased the odds that a user would respond to our smishing message when compared to CLICK. Additionally, we found a similar statistically significant increase when comparing Facebook and Walmart entity scenario to our IRS baseline.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Dec 2022.
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Citing papers per year: 2024: 3; 2025: 1.
4 papers.
- 2024AbuseGPT: Abuse of Generative AI ChatBots to Create Smishing Campaignscited by 7
- 2024Opportunities and Challenges of Generative-AI in Financecited by 6
- 2024A Persuasion-Based Prompt Learning Approach to Improve Smishing Detection through Data Augmentationcited by 0
- 2025Hybrid Machine Learning Model for Detecting Bangla Smishing Text Using BERT and Character-Level CNNcited by 0
- 2025Hybrid Machine Learning Model for Detecting Bangla Smishing Text Using BERT and Character-Level CNN3 Feb 2025
- 2024Opportunities and Challenges of Generative-AI in Finance21 Oct 2024
- 2024A Persuasion-Based Prompt Learning Approach to Improve Smishing Detection through Data Augmentation18 Oct 2024
- 2024AbuseGPT: Abuse of Generative AI ChatBots to Create Smishing Campaigns15 Feb 2024