aiqbench analysis
This research introduces "Knowledge Trap," a novel defense mechanism that mitigates large language model extraction attacks by redirecting adversary queries toward low-value knowledge graphs rather than blocking access or degrading service. By consuming attacker query budgets on benign but useless data, the method significantly reduces surrogate agreement in medical and financial domains while maintaining high accuracy for legitimate users. This approach marks a practical shift from reactive filtering to proactive resource depletion strategies within AI security architectures.
Let Them Steal: Trapping Large Language Model Extraction Attacks with Knowledge Honeypot
arXiv:2606.15810v1 Announce Type: cross Abstract: 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.
Abstract: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.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
arXiv:2606.15810 [cs.CR]
(or arXiv:2606.15810v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2606.15810 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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