“I don’t really give them piece of mind”: User Perceptions of Social Engineering Attacks
Bibliographic record
Abstract
How do end users understand social engineering attacks, and how do their perceptions of these attacks differ from reality? To investigate, we proposed a new social engineering attack framework, and ran two studies to examine exactly how and when users are misunderstanding social engineering attacks. In our first study, we conducted 30 qualitative interviews asking people about their understanding of, and experiences with social engineering attacks. We found that confidence and accuracy are the two main factors affecting users’ knowledge of social engineering attacks. In our second study, we quantified how confidence and accuracy impact users’ perceptions at different stages of an attack. We found that users tend to be overconfident in their ability to understand social engineering attacks, but hold inaccurate beliefs. Participants had major misconceptions of what constitutes social engineering, and the risks of these attacks. Based on our results, we propose educational and design opportunities to match social engineering mitigation strategies to user perceptions of social engineering.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".