Auditing Dark-Pattern Risk in LLM Interaction Prompts: Leakage-Controlled Routing, Confidence-Aware Review, and an Autonomy-Centered Audit Design on DarkBench
Keywords:
Dark Patterns, Large Language Models, Sycophancy, Brand Bias, Anthropomorphism, User Retention, Selective Classification, Calibration, User Autonomy, Interaction AuditingAbstract
Large language model interfaces can steer users through brand preference, retention pressure, agreement-seeking behavior, anthropomorphic framing, harmful assistance, or silent changes to user intent. DarkBench organizes these risks into 660 prompts and six balanced categories. This study asks a narrower but operationally important question: how reliably can an audit gateway recognize the risk category of a prompt before a model generates a reply? The released records were audited at field, ordering, duplication, and source-code levels. A training-prior baseline, a fixed description lexicon, Multinomial Naive Bayes (NB), logistic regression, and a linear support vector machine (SVM) were evaluated with repeated five-fold cross-validation over five repeats. The primary protocol kept connected components of prompts with character n-gram cosine similarity of at least 0.80 in the same fold; a conventional repeated-stratified protocol estimated the performance difference associated with near-duplicate grouping. All learned features were word unigram–bigram TF–IDF fitted within each training fold. Under grouped evaluation, NB obtained 0.9742 accuracy and 0.9741 macro-F1, followed by SVM at 0.9721 and 0.9720 and logistic regression at 0.9697 and 0.9694. Random splitting raised SVM macro-F1 to 0.9930, a 2.11 percentage-point gain that did not survive template-aware grouping. Brand bias was the hardest NB category (F1 0.9334). Confidence ranking was useful despite underconfident probabilities: the highest-confidence 80% of prompts were classified without error by both probabilistic models across all five repeats, while 90% coverage retained 0.9980 accuracy for NB and 0.9993 for logistic regression. The corpus contains prompts and risk targets but no model responses, benign controls, response annotations, or model identities. Accordingly, the measured task is pre-response risk routing rather than detection of manipulative behavior in generated text. The resulting workflow sends uncertain or high-stakes requests to evidence-based response review and retains ordinary routing for high-confidence cases as part of an autonomy-centered audit design.
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