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 preser
The proliferation of commercial large language models as APIs creates immediate vulnerabilities to model extraction, necessitating timely and effective defense mechanisms.
This development addresses a critical security flaw in current AI deployment, protecting intellectual property and revenue streams for model providers while improving model integrity.
Model providers can now employ a proactive defense that actively misleads attackers without degrading service for legitimate users, shifting the economics of AI model security.
- · Large Language Model Providers
- · Cybersecurity Firms (AI)
- · API-based AI Services
- · Malicious Adversaries (Model Extractors)
- · Competitors reliant on reverse engineering
- · Unsecured AI API Platforms
AI model providers can deploy their services with reduced risk of intellectual property theft and unauthorized replication.
The cost of conducting successful model extraction attacks will significantly increase, making them less economically viable for adversaries.
This could lead to a 'security arms race' in AI, where new extraction techniques emerge, countered by more sophisticated honeypots and defensive strategies.
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Read at arXiv cs.AI