KBF: Knowledge Boundary as Fingerprint for Language Model and Black-Box API Auditing
Yijia FangYiqing FengBingyu LiMingxun Zhou
Oct 2026
Artificial IntelligenceCybersecurity
Abstract
Relay and reseller APIs mediate access to large language models (LLMs), but users cannot directly verify which model serves them. We introduce \name, a black-box auditing protocol based on stable factual recall near the knowledge boundary, including repeatable wrong answers. KBF generates benign, renewable probes and calibrates audit decisions against reference self-variation. Across 16 production endpoints, KBF detects all 155 economically relevant substitutions without rejecting any of the 16 same-reference controls. KBF remains robust to deployment variation and reaches 95\% TPR at a substitution rate as low as 15\% in mixed-routing simulations. Field audits flag 7 of 28 endpoints across six platforms as statistically inconsistent with their references. After reference enrollment, even GPT-6 Astra costs only approximately \$0.67 per online audit at the recorded API prices.
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