Let Them Steal: Trapping Large Language Model Extraction Attacks with Knowledge Honeypot
For API providers, it offers a practical defense against extraction attacks that preserves user experience, outperforming existing methods.
Knowledge Trap defends against LLM model extraction attacks by redirecting attackers to low-utility knowledge, reducing surrogate agreement by 6.2% without harming legitimate users.
Large language models deployed as commercial APIs are vulnerable to model extraction attacks, while existing defenses either act too late or degrade utility for legitimate users. We propose \textbf{Knowledge Trap}, a defense that redirects extraction attacks toward low-transferability knowledge through a \emph{Honeypot Knowledge Graph} (HKG) and breadcrumb-guided exploration. Instead of blocking queries or perturbing outputs, Knowledge Trap consumes the attacker's limited query budget on knowledge with negligible downstream utility while preserving benign-user performance. Experiments in medical and financial domains show that Knowledge Trap reduces surrogate Agreement by 6.2\% on average without degrading legitimate-user accuracy, outperforming existing defenses that impose measurable user impact. These results suggest that defending knowledge-space traversal is a practical direction for mitigating LLM extraction attacks.