Fudu Xing, Kailong Wang, Tianlong Yu · 2026 · arXiv
DOI: 10.48550/arXiv.2610.30666 · open-access PDF
LLM-based scam systems increasingly combine adaptive dialogue, persistent context, external tools, and multi-agent coordination to automate personalized fraud. These capabilities also create a resource dependency: every reasoning step, model call, and coordination round incurs inference cost. We present TokenTrap, a resource-oriented defense that redirects suspected scam systems toward benign, low-value computation while withholding credentials, payments, and other exploitable information. TokenTrap combines three components: an architecture-specific token-task library, a router that selects a
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